Anyone who works or worked in e-commerce, they know how crucial this one month is going to be for the business. 🎉This is time for Great Indian Festival and Big Billion Days respectively in Amazon and Flipkart. In Amazon, they call this event internally as Jupiter.

This would be a testing time for the brands, retailers, sellers/vendors, category managers, marketing team, pricing time, supply-chain all of it. It’s a month of relentless firefighting, competing priorities, and decisions that can have a significant impact on the business.

Even though the planning starts months before, things are bound to break. OOS will happen. There will be decisions on fly.

This year, we’re now in the thick of the festive sale season.

One piece of feedback I’ve received about Thinking in Categories is that readers particularly love the chapter on event planning. 🙏

They liked it because I lived it and breathed it. It comes from the experience of being in the thick of things, with boots on the ground.

So here is your page no. 130 :

Hope you also find the chapter useful.

May you find Thinking in Categories on Amazon this festive season. 🪔
🎁 And a small festive surprise: a 5% coupon, available only until 14th October.
Here is the link - https://www.amazon.in/dp/9360682667

Now here is the checklist for you if you are a brand selling on Amazon-

1. Event Planning: Align with Amazon and Flipkart Category teams on targets, deals, hero SKUs, and event strategy.

2. Demand Forecasting: Analyze last year’s event performance and recent weekly sales to forecast SKU-level demand and DRR.

3. Inventory Planning: Align production and replenishment plans with forecast demand; maintain adequate safety stock for demand spikes.

4. Inwarding & Availability: Schedule warehouse appointments and shipping in advance to ensure inventory is available for sale before the event.

5. Pricing & Promotions: Finalize deal funding, discounts, coupons, bank offers, and margin calculations.

6. Catalogue Readiness: Ensure all listings, including new product launches, are live, buyable, optimized, and error-free.

7. Advertising: Finalize search ad campaigns, budgets, bids, target ROAS, and keyword strategy in advance.

8. Marketing & Visibility: Plan banners, storefronts, and social media promotions to maximize event visibility.

9. Order Fulfilment: Ensure fulfilment readiness, and operational support for peak order volumes.

10. Live Performance Tracking: Monitor DRR, sales targets, inventory, ad performance, and margins daily; take corrective action wherever goal to gap.

See you on the other side of the event :)
Let me know in the comment how has been your experience as a customer, category leaders, or brand owners this festival.

Thanks,
Neetu
Thinking in Categories | LinkedIn | X

The promotion was remarkably successful. Historical accounts suggest that millions of free glasses were redeemed by 1913.

And then there were the days when people use to clip coupons from the newspaper and use them to save money on groceries and household items. Good old days.

Now, it’s all digital :)
So here I am, running 5% coupon on my book - thinking in categories on Amazon.

Speaking of Coupons, let’s do a case interview.

GlowGlam is a growing D2C skincare brand selling some amazing products through their own website. The company has invested heavily in the digital marketing and generating strong traffic to PDP (Product display page). Users are checking the products, adding to the cart but leaving without completing their first purchase.

You are the Growth Product Manager there and was asked to develop a coupon campaign that increases first-time purchases while protecting contribution margin.
How would you approach this?

Below is just the basic framework (not the detailed one) for you to get started :

1. What’s the objective of this promotion? (Increasing the number of first-time customers, or improving conversion?)
2. Who is the primary target customer? Is the campaign targeted at all new users, specifically users who have added to cart but abandoned checkout, or a particular segment based on cart value, product, traffic source etc..?
3. Are there any specific guardrails around minimum contribution margin per order, total contribution, CAC, or maximum discount?
4. What is the baseline metrics such as Traffic, add-to-cart rate, AOV, conversion.
5. Is there any specific timeframe that we are considering to run the coupons?
6. Are there any other reason that customers are abandoning - shipping fees, lack of trust, payment friction, delivery timelines.

Based on these clarification, you can set the campaign goal and target metrics.

Next step, break-even analysis for coupon. Assume these are the information available to you.

Based on these numbers, you can calculate what is contribution with and without coupon.

Post coupon, if you get 12.5 orders, it will be break-even (5000 = 400*12.5) so basically your conversion need to improve from 10% —> 12.5%.
Anything above 12.5% is incremental contribution and would make sense to run the coupons. (I am assuming that conversion is same as redemption here, meaning people who are offered coupons they all use the coupons to redeem the purchase)

You can also calculate how much % you should give in coupon by doing the scenario analysis.

Then, what is the design mechanism of the coupon (% i.e. 10% off your first order, Fixed amount, time-bound coupon, threshold coupon i.e. ₹200 off above ₹1,499 etc.)

Once the campaign is launched, it needed to be continuously monitored both the customer funnel and the economics to understand what is working and where we need to intervene.

So there you go. Feel free to expand on this.

Coming back to the question - Does coupon even work? Let me report back to you next week with actual data.

Till then, happy weekend ❤️🤩

Neetu,
LinkedIn | X

Hope you are enjoying your Sunday Morning as I am doing :)❤️

One observations that I’ve had while working with early-stage marketplaces is that they tend to focus heavily on output metrics such as GMV (Gross Merchandise Value) and Contribution Margin. These are, nonetheless, important metrics.

But the challenge is that output metrics tell you what happened, not necessarily why it happened.

To build a sustainable marketplace, you also need to understand the underlying drivers: customer traffic, conversion, assortment, pricing, availability, seller performance and repeat behavior.

If GMV is declining, for example, you need to know what is driving the decline. Are fewer customers visiting the platform? Are they finding fewer relevant products? Is the conversion rate falling? Are customers adding products to their carts but not completing the purchase (Add to cart)? Or are existing customers simply not coming back? Where the customers are dropping in the funnel? Where are customers dropping off in the funnel?

A customer funnel helps break down the journey from awareness to consideration to purchase and, eventually, re-engagement. Instead of looking only at the final outcome—GMV or Contribution Margin—you can see the different stages and identify where customers are dropping off.

For a marketplace, this could look something like:

Visitors → Product Views → Add to Cart → Checkout → Purchase → Repeat Purchase

The customer funnel turns output metrics into actionable questions about customer behavior.

In my book, Thinking in Categories, I’ve included a couple of case studies that explore these concepts in greater depth and show how they can be applied in real-world situations.

If you haven’t checked out Thinking in Categories yet, do take a look 🎁. And happy solving 🎈❤️

Get your copy now

If you prefer kindle, here you go 🤩

Thinking In Categories

So What is customer funnel?

A customer funnel is a structured view of the customer journey that tracks how customers move from one stage to the next—and identifies where they drop off before, during, or after a purchase.

customer funnel and customer journey aren’t exactly the same thing. A funnel is a measurement/analytical representation of the journey; the actual customer journey can be non-linear, with customers moving back and forth between stages.

I look forward to your feedback :)

Happy Weekend ❤️❤️

Neetu
LinkedIn | X

Fukubukuro (福袋) literally means “lucky bag” 🧺🛍️in Japanese.

It is a Japanese New Year retail tradition where stores put a selection of products into a sealed bag (Random items), usually sell it at a discount, and customers don’t know exactly what is inside until they buy it. A surprise at the beginning of the new year.

The appeal comes from three things:

• 🎁 Surprise — you don’t know exactly what you’re getting.

• 💰 Perceived value — the combined value of the products is typically higher than the price of the bag.

• 🎯 Discovery — customers get a chance to discover products they might not have bought individually.

Image source : Google image

Talking about surprises, I’ve received some wonderful feedbacks on the book. 😊Especially folks who are new to category roles and want to learn e-commerce, consumer space in depth.

Feedbacks like these makes my day, this one is from top-level CXO in MNC and my alma mater 😊😊

Thank you so much! I am grateful for all of you. 🎈❤️

It has been incredibly encouraging to see subscribers of this newsletter engage with the book and share their thoughts.

Get Your Copy

If you prefer kindle or want to check before buying paperback, here you go.

Kindle

ok. So, This idea of Fukubukuro has also been adopted by global brands. Here are two interesting examples.

1. Sephora

Sephora’s new mystery bag caused a stir online after an eagle-eyed shopper, who goes by @whitepolkydots on Instagram, posted a photo on the platform of the kits she discovered at a Sephora store in Germany. The three different bags — a “mystery eyeliner kit,” “mystery lip kit” and “mystery cheek kit” — each costing $16, contained two known products, one surprise item and a collectable pin. According to a source close to the company, the decision to focus on products like eyeliner and lipsticks were because they were “booming categories.”

Read more here: https://www.businessoffashion.com/articles/beauty/why-makeup-brands-are-selling-mystery-bags

2. Starbucks

the lucky bags from Starbucks have become so popular that in recent years they’ve been made available only through lottery applications, making them some of the most hard-to-get fukubukuro in the country.

Read more here : https://soranews24.com/2020/01/03/starbucks-japan-lucky-bag-is-the-most-hard-to-get-fukubukuro-of-the-new-year/

Surprise can be a powerful marketing tool. A Harvard Business Review article explains why surprise has such a strong impact on customer behaviour and emotions.

Surprise is addictive.

Surprise changes behavior.

Surprise is cheap.

Surprise turbocharges emotions.

Surprise fuels passionate relationships.

Surprise is not the final emotion. It is the trigger.

A mystery box first creates surprise → the customer pauses → curiosity and anticipation kick in → they discover what’s inside → if the outcome exceeds expectations, joy follows → the emotional experience becomes something worth talking about.

So Fukubukuro isn’t simply selling a bag of products. It is about designing an emotional experience for your customers.

Similar to the surprise that you gonna get when you open my book ❤️🤩

Looking forward to what you have to say.

Leave a comment

Do share this post if you like it :)

Share

Thanks,
Neetu
Thinking in Categories | LinkedIn | X

First of all, Happy Independence day 🎉

A year ago, I came across a story in my favorite magazine - Reader’s digest about how the shopping cart came to be. To me, this was interesting that how someone recognized the need for such product that didn’t exist before - and then imagined, designed, and today, almost everyone of us would have used it.

Today, when you walk into almost any store, picking up a shopping cart 🛒 is almost automatic. It has become such a familiar part of shopping that we barely give it a second thought.

What fascinates me even more is how the shopping cart🛒 evolved into one of the most important levers for driving Average Order Value (AOV) in retail.

In Thinking in Categories - Building and scaling high-growth categories in consumer retail, I explain how AOV can be broken down into its underlying equations and, more importantly, how retailers can influence each of those components to drive category and business growth. 🚀📖

Now it is available in India in Paperback format as well 🙏:)

Add it to your shopping cart :)

When we think about how to drive AOV, the shopping cart might not be the first idea that comes to mind. Yet, it has been a game changer.

SO let me tell you what I learnt about the history of shopping cart.

Till 1937, the size of the customer’s cart was limited by how much they could physically carry. Shoppers either use their arms or basket so basically it would mean that shoppers will buy only that much that they can carry in one trip.

A user wrote on Quora,

In my grandmother’s day, giant groceries stores did not exist. Instead, there were specialty stores - the butcher, the fishmonger, a cheese store, the baker, the green grocer etc. Women who lived in town shopped more frequently (often every day or two)and in small quantities, because storing fresh food like meat was a challenge. My gran had net bags made of crocheted string, baskets, canvas bags etc. However if you are just shopping for one or two days worth of meat, you don’t need many bags.

Store owner Sylvan N. Goldman noticed this problem at his Oklahoma city supermarket. Customers were not shop more not because they didn’t have money or interest to buy but because their basket 🧺 got too heavy or full.

Sylvan Goldman and his mechanic employee Fred Young invented the first shopping cart in 1937 by placing wire baskets on a wheeled folding-chair frame. Goldman filed his patent for the "Combination Basket and Carriage" on May 4, 1937.

Even though anyone can see today how shopping cart makes shopping so frictionless and convenient, it didn’t pick up immediately. You see, there was a psychological aspect to it.

The men reportedly felt that it made them look weak for needing a cart to carry groceries and women associated it with the baby strollers.

So Goldman hired models and actors to walk through his stores, normalizing the behavior of using shopping cart. In my opinion, that was a stroke of marketing genius.

Once shoppers accepted the cart, the effect on purchasing behavior was immediate - people bough more per visit 🛍️. Then rising car ownership and Refrigerator also helped shoppers to buy in bulk, further driving AOV.

In 1999, design firm IDEO took a famous five-day challenge for ABC’s Nightline to completely redesign the standard grocery shopping cart. They redesigned the cart considering issues such as maneuverability, shopping behavior, child safety, and maintenance cost.

Today, we use “Add to cart” button , a digital equivalent of placing an item in a physical shopping basket. It lets customer add items without thinking of the total cost, encouraging larger basket without that final decision-making until the end.

The idea of shopping cart is so everyday yet so functional and customer-centric. That, It simply changed how much money customers spend per visit, in short, Average order value (AOV). And today, Retailers measure basket size as one of the core performance indicator.

What do you think could be some future hardware inventions that have the potential to move category levers?

Leave a comment

Happy Weekend 🎈❤️:)

If you are interested, you can read the story here - https://www.history.com/articles/shopping-cart-inventor-sylvan-goldman-supermarkets

If you are interested in understanding the Ideo process, check the YouTube video here :

I started this newsletter with an intent to share my experience in e-commerce and in category management. I have formally structured it into a book and last week, I have published it on amazon 🚀📖.

When I started, I didn’t know it’s gonna be this hard :)

But here you go, after so many weekends 🥳📘🎉

First of all, a huge thank you

Many of you have been reading this newsletter since the beginning.

Some of you have replied to my posts, shared your own experiences, and encouraged me to keep writing. 🙏

This book exists because of your encouragement. Thank you for being part of this journey. I am really grateful for all of you.

If you decide to read the book, I’d genuinely love to know what resonates with you—and what doesn’t. Your feedback will help me make future editions even better.

What will you get in the book :

The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

One section I'm particularly excited about is the final chapter, where I've included real-world case interviews designed to help you prepare for and confidently crack category management and retail interviews.

Have fun reading it 📚✨

📖 Kindle Unlimited subscribers can read it FREE.

Read for Free

It’s available in India, US, France, Australia, Canada, North America , and other european countires. You would need a kindle app to read the book.

If you get a chance to read it, I’d be truly grateful for your feedback. And if you find it useful, an honest Amazon review—even a short one—would mean a lot to me.

📚 The paperback edition for US, France, Australia, Canada, North America, EU is live.

Order here

I’m also working on bringing the paperback to India, and I’ll keep you posted on the progress.

If you’d like to pre-order a copy, simply reply to this post or send me a message. No forms, no sign-up links, no lengthy questionnaires—just a simple “I’m interested” is enough, and I’ll make sure you’re among the first to know when it’s available.

Drop me a note

Cheers 🎈❤️,

Thank you for reading.

— Neetu

Hope you are enjoying sip of Tea/Coffee on lazy Sunday morning.

In the beginning of this month, I set out to write a series on AI 😊, specifically contextualized to e-commerce, digital-native companies, and platform businesses. The idea was to explore the application of Generative AI within my own functions and areas of work.

You can read last 3 posts here:

Part 1: I will construct scenarios and examine the likelihood of fully-autonomous agentic-AI taking over how we shop.
Part 2: I will deep-dive into real-world case studies (mainly in ecommerce, marketplace, and digital –native companies) about where and how AI is getting is deployed across workflows.
Part 3: I will lay out the AI safety framework and non-negotiable guardrails that e-commerce and marketplace companies must establish to build responsibly.

Here I am with part-4, the last leg of this series, which feels closest to my passion as it intersects between AI and sustainability.

With this part-4, my hope is that as business leaders of today and tomorrow, we will reflect our own responsibilities towards sustainability in an increasingly AI-driven world. 🌱🌱

The paradox of AI and sustainability

Before I start deep-diving into use-cases where AI can drive sustainability, I want to acknowledge the underlying paradox here. The elephant in the room.

Training large language models, operating GPU farms, and cooling data centers consumes vast amounts of electricity and water.

According to Goldman Sachs Report: Generational Growth — AI/data centers’ global power surge and the sustainability impact :

Now with the pace of power efficiencies decelerating and AI demand building, data center power demand is poised to grow 160% by the end of the decade.

AI development will require fresh water for the data centers to run. Data center acceleration will accelerate the climate crisis. There are communities which are already impacted due to lack of water. Increase in carbon emission, lack of drinkable water - all this is concerning and there has to be a proactive action-plan to manage the net impact of AI development and deployment.

So the question is whether the overall impact will be net positive—can we offset AI's environmental costs with the sustainability gains it enables? Well, let’s try :)

Here are some key use-cases where AI can help reduce carbon footprints across various functions in e-commerce, consumer brands, and marketplace businesses:

♻️AI helps ensure your supply-chain, logistics is transparent, ethical, and fully compliant.

a. Ethical Raw Material Traceability

AI-powered tools can extract, clean, and merge data from disparate systems (ERP, supplier forms, IoT devices, shipping logs), even when formats differ — giving brands a unified and verifiable picture of the supply chain.

Companies like Minespider and Optel Group are helping battery and electronics manufacturers trace the origin of raw materials like lithium. Using blockchain and AI, they map the entire supply chain to verify ethical sourcing — free from child labor, environmental harm, or opaque practices.

If you are a consumer electronics brand, this means that your can monitor your entire supply chain and align your overall sustainability efforts in one supplier platform.

b. Anomaly Detection and Risk Flags

Machine learning models detect anomalies in sourcing data — such as unusual transit routes, inconsistent supplier documentation, or regions flagged for labor violations — and raise alerts in real time.

c. Satellite and Image Analysis

AI analyzes satellite images of mining regions to detect illegal mining, deforestation, or unsafe labor conditions. This data can be cross-referenced with supplier claims for due diligence.

d. Predictive Analytics for Compliance

AI predicts potential regulatory risks or ESG violations based on historical supplier behavior, country risk profiles, or upcoming geopolitical shifts.

e. Natural Language Processing (NLP)

AI scans documents, certifications, or even news/social media for supplier-related risks — helping identify hidden red flags in opaque regions of the supply chain.

This might not be possible to do at scale without AI but with AI, with you have that opportunity to do that.

Doing this at scale isn’t feasible without AI, but with AI, it becomes entirely possible.

Imagine that you are running a sustainable marketplace — You are selling from electronics to clothing. Transparency, traceability, and trust are at the heart of your brand. Clothing should be producted in fair-wage factories. Minerals used in the manufacturing of electronics should be sourced from conflict-free zone.

But, How do you verify thousands of products, from dozens of categories, supplied by hundreds of vendors across the globe — all while ensuring your claims of sustainability hold up?

• AI automates supplier risk checks by scanning documents, certifications, and even public news to identify red flags.

• Computer vision verifies sustainability labels and packaging claims via product photos.

• Machine learning models flag inconsistencies in supplier declarations vs. past behavior.

• Natural language processing summarizes ESG audit reports or supplier statements — saving hours of manual review.

• Blockchain + AI integration creates an immutable trail of raw material origins, shipping logs, and processing history.

AI can ensure that transparency, traceability, and trust - Which are the core pillars of your sustainability Marketplace brand.

If you're looking to understand and improve your supply chain, emission mapping is one of the most powerful diagnostic tools available.

Emission Mapping

Emissions are categorized into three scopes as defined by the Greenhouse Gas (GHG) Protocol. Emission mapping is basically visulize and analyzing emission from the origin itself and the intensity.

Scope 1 : Direct emissions

• Emissions from company-owned vehicles 🚚

• On-site fuel combustion (like gas boilers or diesel generators)

• Emissions from manufacturing or processing facilities

If you are running a fulfillment centers, you can calculate and monitor emission in real-time using AI+IoT.

Scope 2: Indirect Emissions from Purchased Energy 🔋

These are emissions from the electricity, heating, or cooling you purchase and consume, even though the emissions happen at the utility’s site.

Scope 3: Indirect Emissions from Supply Chain and Beyond

This is where most companies have the largest, yet least visible, emissions — coming from suppliers, product distribution, and even end-of-life product disposal. Scope 3 is often ignored because it’s hard to measure.

Emissions from raw material extraction, logistics (3rd party fleets, international shipping), product usage, and waste are key components of Scope 3 emissions.

You can identify which parts of your supply chain are most carbon-intensive — is it raw material extraction? International shipping? Packaging?

AI helps tackle this complexity by parsing supplier reports, shipping data, and purchase orders to estimate emissions. NLP tools extract ESG insights from unstructured formats, while machine learning fills data gaps using industry benchmarks. Combined with blockchain, AI enables end-to-end traceability and supplier-level accountability across the value chain.

b. Sourcing and Procurement

Say you are in ecommerce business, and you're sourcing packaging material. AI-backed procurement tools can now suggest suppliers that not only meet specs for your packaging material but also have a lower carbon footprint — giving buyers environmental visibility at the decision point.

C. Packaging

5 ago, when I wrote "The Wasteland of Cardboard," my goal was to raise awareness among e-commerce companies about the issue of overpackaging and advocate for smarter, more sustainable packaging tailored to each product. I would like to believe that I have played a role there when e-commerce companies switched to more sustainable packaging.

Today, with AI-powered procurement tools, we have the opportunity to take that approach even further — enabling data-driven, dynamic packaging decisions that minimize waste and maximize sustainability across the supply chain.

Companies like Esko and ManageArtworks are using generative AI to design packaging that uses the least material while protecting the product over a 1000-km journey.This is particularly relevant for complex or fragile products shipped globally — furniture, electronics, specialty items.

Product images or dimensions can be used to optimize packaging

• Computer vision models analyze product images (or 3D scans) to determine exact size and shape.

• Based on that, AI recommends the smallest possible box or envelope, eliminating empty space and reducing the need for fillers like bubble wrap or foam.

Amazon has developed an AI-powered Packaging Decision Engine, which uses machine learning and product data to recommend the most efficient packaging option, reducing waste and shipping costs.

Packsize, an American corrugated material manufacturer, offers “on-demand” packaging systems powered by AI to create custom-sized boxes for every order. It reduces cardboard waste and shipping volume. And my hope is that we see similar innovation in India as well.

E. Inventory planning and demand forecasting, Return

One of the major challenges I faced as a category lead was managing ageing and unhealthy inventory — especially in fast-moving consumer goods (FMCG). In this category, time is critical. If inventory is not sold within a defined shelf-life window, it often means the products are either already expired or dangerously close to expiry. Every day of delay increases the risk of unsellable stock, leading to massive wastage — both in terms of financial loss and environmental impact.

Millions of units can end up in landfills due to poor visibility or reactive inventory planning. It requires real-time tracking, faster liquidation strategies, smarter markdown decisions, and collaboration with discounting or donation partners — often all within a compressed timeline.

• AI models analyze historical sales, seasonality, promotions, regional trends, and external factors (like weather or festivals) to accurately forecast demand, reducing the risk of overstocking slow-moving SKUs.

• AI tracks product shelf life at batch and warehouse level using expiry data, velocity, and sell-through rates — enabling real-time prioritization of items at risk of expiry.

• AI can also identify appropriate donation partners, alternate channels (e.g., quick commerce, institutional buyers), or “save from landfill” programs when liquidation isn’t viable.

  ---

WHY YOU SHOULD INVEST INTO SUSTAINABILY if you are a start-up. 💰🎯

In today’s climate-conscious investment landscape, Environmental 🌍, Social, and Governance (ESG) scores are becoming a key factor in venture capital decisions — especially for startups in consumer goods, tech, manufacturing, and logistics. VCs are willing to pay for tools that help assess ESG in deal flow or portfolio monitoring.

More and more, your customers would demand it. The next generation cares about the enviornmental impact. Combining Sustainability + AI isn’t just about doing good for the planet; it’s a smart business strategy that drives both bottom-line efficiency and top-line growth 📈. AI predicts demand accurately → minimizes overproduction and excess inventory. By optimizing your supply-chain, you can save on Lower transportation costs + avoidance of carbon taxes and penalties.

And AI can operationalizes sustainability — turning goals into measurable actions, savings, and sales.

Most companies aren’t in the business of sustainability — they often lack the in-house expertise or tools to manage it effectively. Instead, they partner with specialized firms that help them measure, manage, and reduce their carbon footprint through data, technology, and compliance support.

it's one thing to use AI for sustainability (e.g., optimizing logistics or reducing waste), but equally important is to make sustainable choices when building or using AI itself. AI systems can be energy-intensive, especially during training and deployment phases, so organizations must be intentional about how they develop and use these technologies.

Choice of AI model, location of processing (Data centers), right infrastructure (GPU, Tensor Processing Unit, Grace Hopper Superchip etc.)

The lifecycle of an AI model typically includes three phases: training, tuning (or fine-tuning), and inferencing.

1. Training (learning from massive datasets)
2. Tuning (customizing for specific tasks)
3. Inferencing (actually using the model to generate outputs)

At each stage, energy and computing resources are used — and this impacts sustainability.

🤖 Larger models (like GPT-4, PaLM-2, LLaMA 2 65B) require much more computational power to train and run. Smaller or optimized models (like DistilBERT, MobileBERT, Whisper Tiny, or LLaMA 2 7B) can often achieve comparable performance for specific tasks with drastically lower resource usage. Some models are designed to be energy efficient and faster at inference (e.g., OpenAI’s GPT-4o, Google’s Gemini Nano, or TinyML models for edge devices). Hosting a model on carbon-neutral cloud providers or using on-device AI (for low-latency apps) can further reduce emissions.

💡Using a right-sized model for your task = fewer servers, less electricity, and lower emissions

Assume an electronics consumer-brand marketplace wants to build a chatbot that helps customers navigate electronics warranty policies. It can reduce its carbon foot-print by:

• Feeding Sustainability Info into the Chatbot - By making customers aware about the repairs over the replacement.

“Your warranty covers repair — would you prefer a fix over a replacement to reduce e-waste?”

• Deploying lightweight NLP models (e.g., fine-tuned BERT or DistilBERT instead of GPT-4) for this specific use-case can save compute resources.

• Ensuring inference runs on green cloud infrastructure or carbon-optimized servers.

With this, I am wrapping up my AI series on ecommerce, marketplace, and platform business.

I am happy to meet folks who are working in the AI + sustainability area. DM in case you are one of those champs, I am inspired by you already :)

Happy to connect

Keep learning 🚀, Have a great weekend🎉 To the sunshine, rainbows and sustainable future 🌈🕊️

Thanks,
Neetu

© 2025 [Neetu Barmecha]. All rights reserved.
This content is the intellectual property of the author. Do not reproduce without permission.

Thinking Category

On to the this week post,

What I learned from building a mental and physical wellness conversational-AI product

At the beginning of 2025, I started building AI-native products using AI-powered Platforms. There are platforms like lovable, bolt, Replit – no-code tools that allow anyone to build web applications without coding. This trend is popularly known as vibe-coding.

I began using ChatGPT and other LLMs to observe and analyse my own behaviour, emotional triggers, and impulses post an injury and setback.

My goal was to identify recurring patterns in how I reacted to situations—particularly during moments of stress or pain. I wanted to take a hard, honest look at my own behaviour and intentionally recalibrate my reactions. It was a personal experiment in improving self-awareness, regulating day-to-day emotional responses, and building emotional intelligence. I was using these tools to help design a more intentional, balanced, and emotionally resilient life.

I realized that when it comes to navigating everyday life and dealing with people, you don’t always need a therapist. Often, what you truly need is the ability to understand your own responses and understand them more deeply. That’s where large language models can make a meaningful difference—as thoughtful, accessible wellness companions. This insight led me to build Serenely (An experiment)

Serenely is a conversational, AI-powered mental wellness companion designed to support users in their emotional journeys with empathy, clarity, and self-reflection. It helps individuals recognize behavioral patterns, reflect before responding, and enhance their emotional intelligence in the context of each specific situation. (You can check for more details here but the chat functionality might not work anymore)

In the initial stages of building the product, my focus was primarily on solving user pain points through product features, refining the UI/UX, designing smooth navigation flows, integrating with Supabase, and setting up login and chat functionalities using OpenAI and Gemini APIs. I was addressing real user needs and quickly iterating on features and functionality.

In hindsight, I overlooked a critical user pain-point in building a wellness app: user safety.

I overlooked something very critical: establishing a solid AI safety and governance framework from the very beginning. And I wasn't alone—many product folks I knew were focused on features, speed, and scalability, but almost no one was thinking about safety.

This was a serious gap—especially when building for mental wellness. In a product that interacts with users at their most vulnerable, that’s not just a design oversight—it’s a fundamental risk.

Later I got to know that there are companion chatbots in the market and one such bot on app Chai caused suicide of a Belgian Man.

Data privacy, emotional sensitivity, and safeguards against hallucinations aren’t optional—they’re the very core of such products. When you're creating AI tools that engage with human psychology, safety can’t be an afterthought—it’s the foundational layer on which you build your product.

User trust isn’t earned with clean UI or seamless UX. It's earned by making sure the system doesn’t do harm—by being transparent, by handling data with care, and by ensuring the AI communicates responsibly.

This changed how I view product development in the AI age. It’s easy to get caught up in the magic of what these tools can do. But responsible innovation starts with responsible infrastructure—and that includes building for safety, privacy, and responsibility from the day one.

From my own experience of building in AI, I learnt:

No matter what the product is, AI safety must be a fundamental building block from the start.

So in this part 3 of AI series, I will try to examine AI safety framework and non-negotiable guardrails that e-commerce and marketplace companies must establish to build responsibly.

Before this, I encourage you to check:

Part 1: I will construct scenarios and examine the likelihood of fully-autonomous agentic-AI taking over how we shop.
Part 2: I will deep-dive into real-world case studies (mainly in ecommerce, marketplace, and digital –native companies) about where and how AI is getting is deployed across workflows.

You might heard of technological singularity - a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable consequences for human civilization.

With the invention of AI, we might have reached closer to this hypothetical future point.

Stephen Hawking had warned us:

You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green energy project and there's an anthill in the region to be flooded, too bad for the ants. Let's not place humanity in the position of those ants."

But, wait a minute.

Why is AI safety and governance framework important for business leaders?

Ensuring AI safety might be the responsibility of the tech team, Business is a critical stakeholder because risks such as bias, hallucinations, or unpredictable outputs are no longer theoretical concerns—they have real consequences that can affect revenue, brand reputation, and regulatory compliance.

Imagine that The AI system generating product descriptions occasionally produces hallucinated or inaccurate details — such as claiming a phone has features it doesn’t actually support.

Customer receive the product and felt cheated. They leave negative reviews about the brand. The platform now faces potential legal action for false advertising. Customers lose the trust in the brand. Revenue declines. You got the point :)

While AI might be built by data scientists, category leaders are ultimately responsible for how it impacts business outcomes, customer experience, and brand trust.

That’s why, Business and product leaders must actively engage and ask critical questions around safety to safeguard business continuity.

Speaking of critical, If you haven’t subscribed to the newsletter yet, I encourage you to do so. It’s a great way for me to know that there are readers who find value in what I share.

So how should you build a strategic, actionable roadmap tailored for ecommerce:

1. Risk assessment and use-case prioritization:

In e-commerce, AI can be applied across a wide range of use cases—but not all carry the same level of risk. The first step is to map these use cases based on their potential impact and associated risks:

a. High-risk, high-impact areas include AI applications in dynamic pricing, credit scoring, personalized recommendations, fraud detection, customer service chatbots, and automated hiring tools. These systems directly affect customer trust, financial performance, and regulatory compliance, making them critical to get right.

b. Lower-risk areas such as inventory forecasting, SEO optimization, and image tagging typically pose minimal risk to customer experience or legal exposure, though they can still drive efficiency and value.

To manage AI deployment responsibly, prioritize use cases using a risk-benefit matrix, focusing first on high-impact systems—particularly those involving sensitive customer data, financial decisions, or areas under regulatory oversight (e.g., GDPR, CPRA, or the EU AI Act). Focus on the most consequential use-case first to maximize business value.

Dear readers of Thinking Category,

Exciting news 🥳📘🎉I published a book recently - Thinking in Categories. The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

It is availble in India via Kindle. Outside of India, it is available on paperback as well. This book is the only book that you need if you are aiming for Amazon, Flipkart, Walmart, Myntra or category/marketing manager roles in E-commerce.

Buy on Amazon

If you get a chance to read it, I’d be truly grateful for your feedback. And if you find it useful, an honest Amazon review—even a short one—would mean a lot and help the book reach more readers. Thank you ❤️❤️

2. Establish core Pillars of Your AI Safety Framework

A comprehensive AI safety framework for ecommerce or digital applications must be built on five core pillars:

• Data governance, privacy and ethics

• Fairness & Bias Mitigation

• Transparency & Explainability

• Robustness & Reliability

• Governance, Compliance & Human Oversight

  

  A. Data governance, privacy and ethics
  

  Data governance refers to the management of data quality, consistency, security, and compliance across the organization. It ensures that AI and analytics are built on reliable, accurate, and ethical data—not fragmented, outdated, or biased inputs.

  Ecommerce companies handle millions of data points daily—from customer behavior and transactions to inventory levels and supply chain movements. If that data is inaccurate, it will lead to wrong recommendation.

  Imagine if Walmart’s AI forecasted demand using outdated sales data, or if different systems stored product data in inconsistent formats. This could lead to stockouts or building unhealthy inventory.

  In the context of AI-powered mental wellness conversational app that I was working on, this pillar would mean that conversations between user and chatbot remains secure and confidential, preventing any data leakage.

  B. Fairness & Bias Mitigation

AI system may unintentionally treats different group of people unequally —based on factors like gender, age, location, language, or income level. This is demographic disparity. (It could be similar to earlier report of quick-commerce app - Zepto showing different price based on the smartphone that you have, an example of discriminatory pricing). This is Demographic disparity.

Fairness and bias mitigation in ecommerce means constantly monitoring and testing your AI systems to ensure they treat all users equitably, regardless of who they are or where they come from.

To catch and fix such issues, you can use tools like - IBM fairness 360 or Google’s what-if tool. Team can simulate how an AI model performs across different demographic segment.

This is important because these disparities can break customer trust.

{Even though the AI system wasn't designed to treat different groups unfairly, it learns from the data it's trained on. If that data reflects past inequalities, stereotypes, or patterns of exclusion—then the AI may unintentionally repeat or amplify those biases.}

Another example in digital-native companies - Job platforms which are powered by AI recommendations, male users may be disproportionately shown high-paying or technical roles, while female users are more frequently shown lower-paying, clerical, or support roles—and not leadership or executive-level opportunities. This reinforces existing workplace biases and limits equal access to career growth for women working-professionals.

C. Transparency & Explainability

Let’s say you're running an ecommerce marketplace like Meesho, Myntra, Nykaa. A seller complains that their product (say, a new Bluetooth speaker) is consistently ranked much lower in search results, even though it has competitive pricing and decent reviews.

Poor visibility means fewer clicks, fewer sales, and potential revenue loss. If the seller escalates the issue, your team needs to explain why the AI model made that decision.

Product search and ranking

The team apply SHAP (SHapley Additive exPlanations) to interpret the model's decision on why the product is ranked low. SHAP breaks down the contribution of each feature to the ranking score.

The SHAP output shows:

⭐ Customer rating contributed positively to ranking (+0.15)

🛠️ Product title relevance was neutral (+0.02)

🚚 Delivery time contributed negatively (-0.20)

📦 Inventory availability also negatively impacted ranking (-0.10)

So this core pillar is about -

Transparency: Making the logic and factors behind AI decisions visible.

Explainability: Providing clear, understandable reasons for those decisions—especially in high-impact or sensitive scenarios.

In a McKinsey survey of the state of AI in 2024, 40 percent of respondents ( business leaders) identified explainability as a key risk in adopting gen AI. Transparency & Explainability make the AI systems accountable. And not just another Blackbox.

D. Robustness and reliability

AI sometimes make things up. It is called Hallucination. Why? I was curious to know and found this video with a pretty good explanation.

Answer is - Data quality, Generation method, Input context

Let’s take an example of Ikea - Virtual assistant answering product queries of the customers.

A customer asks, “Is this bookshelf waterproof?” The AI replies, “Yes, it’s water-resistant and safe for outdoor use.”
Reality: The bookshelf is not waterproof and is only meant for indoor use.

Customer buys the product, uses it outdoor and product get damaged. Leads to return, and bad customer reviews.

Hallucination is a business risk.

There have been report of AI chatbot telling users to kill themselves and other harmful suggestions. While these are extreme cases, they highlight the serious risks involved and underscore the importance for businesses to prioritize safety and ethical considerations when deploying AI systems.

E. Governance, Compliance & Human Oversight

AI excels at pattern recognition and scale, and it can interpret context and mimic empathy to some extent, but they lack the human depth of understanding, moral reasoning, and emotional intuition. That’s why a human-in-the-loop (HITL) model is critical.

For an instance - You are a marketplace. During a festive season, there are unexpected delivery delays by sellers. As a category manager, you know that it was not seller controllable. Let’s say, The spike happened due to change in consumer preferences. Sellers should not be accountable for that.

Now, the platform's AI-based seller performance system will automatically tracks these issues and flags the seller as underperforming. Based on thresholds, the AI suspends the seller account to maintain customer experience standards. But that should not be the case.

Point is : Without human insight and oversight, system can penalize genuine cases.

And, The combination of AI efficiency and human judgment reduces false positives.

🔑 Key Components of Effective Human Oversight:

I am not getting into legal aspects but needless to say, the framework should still ensure legal compliance and applicable laws.

3. Implementation, continuous monitoring in place and metrics for success

Once all relevant scenarios have been identified, planned, and documented for your business use-cases, the next step is rigorous implementation to ensure responsible AI deployment.

But it’s not over yet. Continuous monitoring and governance are vital to anticipate risks, correct unintended consequences, and ensure long-term AI performance aligns with business values.

Additionally, a clear and well-documented incident response plan is critical. This plan should clearly outline protocols for handling system failures or harm — such as biased pricing algorithms being deployed — including immediate rollback procedures, internal investigations, stakeholder communication, and long-term corrective actions.

As with any business functions, it’s important to set clear KPIs for AI safety. Lack of measurable goals may result in lack of accountability.

Primary KPIs could be :

AI Incident Rate & Operational Health related metrics- Number of AI-related failures, safety breaches, or unplanned model rollbacks per period

You can set measurable goals like -

• Critical incidents: System failures causing service disruption (Target: 0 per quarter)

• Safety breaches: Instances where AI outputs violate safety guidelines (Target: <0.1% of total interactions)

• Model rollbacks: Emergency reversions due to performance degradation (Target: <2 per quarter)

• False positive/negative rates: Accuracy degradation in critical applications (Target: <5% deviation from baseline)

• Mean Time to Resolution (MTTR): Average time to fix AI-related incidents (Target: <2

Customer Trust & User Experience specific Primary KPI: Net Promoter Score (NPS) specifically for AI-powered interactions and overall user confidence metrics

There could be KPIs specific to 5 core pillars of AI safety governance framework in place. For example - Explanation request fulfillment: Percentage of user requests for AI decision explanations that are successfully answered (Target: >95%) to ensure transparency and explainability.

Instead of tracking everything and risking analysis paralysis, focus on a small set of metrics that truly matter to your business outcomes.

Let’s explore some existing framework:

While no single global framework exists yet, several significant AI safety initiatives, guidelines, and regulatory frameworks are emerging across governments, industry, and research organizations.

Not all AI companies have fully developed, proprietary safety frameworks for their LLMs—but most serious players implement some form of safety protocols.

You can go through some of these:

1. https://safety.google/cybersecurity-advancements/saif/#secure-ai-principles

2. https://openai.com/safety/

3. https://www.anthropic.com/news/core-views-on-ai-safety
   

I am of this strong opinion that AI safety and responsible AI should not be treated as a cost center but as a revenue-driving function.

Yoshua Bengio, a pioneer of artificial neural networks and deep learning, is advocating to build a Scientist AI whose primary job will be to is to assess the likelihood that another AI's action could cause harm — and intervene to block it if the risk exceeds an acceptable threshold. (Ted Talk) AI companies could integrate a Scientist AI layer into their models to prevent them from taking potentially dangerous actions.

Until the time we have scientist AI, let’s act as our own—building internal safeguards and oversight within the company.

Let me end with a quote from Nick Bostrom, writer of Superintelligence : Paths, Dangers, Strategies and a philosopher known for his work on existential risk,

As a final note, let’s not be the ant that Stephen Hawking refers to. Embed safety guardrails from the get-go. Scale responsibly. To the responsible AI 🎉 Hope I have scared you enough :p

I would love to connect with folks who are setting up AI guardrails in their org. We’ve grown to a community of 3,500+ engaged readers include CXOs and top-management. If you’re looking to promote your business or explore brand sponsorship opportunities, Feel free to DM. Our upcoming posts will focus on sustainability. If this aligns with your brand or business values, it could be a great opportunity for collaboration or sponsorship.

Feel Free to DM.

Keep learning, Have a great weekend🎉

Thanks,
Neetu

© 2025 [Neetu Barmecha]. All rights reserved.
This content is the intellectual property of the author. Do not reproduce without permission.

In the previous post, AI series part 1 - Marketplace and digital-native companies 3.0: A Primer on AI-Powered commerce, I discussed likelihood of agentic-driven commerce and built upon scenarios based on market trends, and rapid development in the AI field. I hope you enjoy reading it.

A few years ago, during a Disney Strategic Foresight workshop, Richard Ramsey—then a VP of Latin America HR—shared something very profound with a group of about 25 of us that has stayed with me ever since: “The future is not evenly distributed.” Nearly a decade later, those words still seems so true and relevant to me.

Whether AI will level the playing field or further widen the gap remains up for debate. But one thing is clear: e-commerce and digital companies are racing to embrace AI—compelled to act and stay relevant so that they are not on the left tail side of the distribution.

According to an IBM study,

Indian Companies are Investing in AI for the Long Term, Intend to Use Open-Source Tools to Drive ROI and Innovation.

So In this post, I intend to share some of the use-cases that are currently implemented by ecommerce and Internet companies - Meesho, Razorpay, and redBus. I selected these three from different domains to offer a broader view of the current AI landscape.

Some of the use cases I’m sharing are drawn from my own consulting experience with SMBs, MSMEs, and early-stage startups. As an advisor, I work closely with founders on driving revenue growth and exploring practical applications of AI across e-commerce and Martech workflows.

Update on my book - I’ve received some wonderful feedbacks on the book. 😊Especially folks who are new to category roles and want to learn e-commerce, consumer space in depth.

Feedbacks like these makes my day, this one is from top-level CXO in MNC and my alma mater 😊😊

Thank you so much! I am grateful for all of you. 🎈❤️

It has been incredibly encouraging to see subscribers of this newsletter engage with the book and share their thoughts.

Get Your Copy

If you prefer kindle or want to check before buying paperback, here you go.

Kindle

So let’s start without further ado.

Case study -1 redBus leveraging AI for analytics, CX and to improve operational efficiency

redBus is currently embedding AI into their existing workflows. Last week, I got a chance to connect with the team and learnt about some of the work-flows where team is leveraging AI and agentic-AI to improve CX and gain operational efficiency.

Below are a few of the use-cases that the team is currently working on:

A. To improve customer experience, redBus shares AI-driven insights and recommendations with bus operators for focused interventions:

redBus has millions of reviews from customers who travel on buses operated by bus operators through the redBus platform.

The team uses GPT-4 enterprise model to categorize feedback into key parameters – Vehicle quality (Speed, rash driving), service quality (Cleanliness, amenities), driver performance etc and extracts granular insights on customer experience.

The model not only considers high-level trends, but also brings out sub-issues such as “AC malfunction” or about “rest stop poor hygiene”. This enables a deeper understanding of customer pain-points.

Once GPT-4 model produces the insights, redBus can flag low-performing buses or operators ((e.g., “Sangam Travels Bus #5 has 12 complaints about broken charging ports”) and can also share recommendations for targeted interventions with their bus partners. This help in improving service standards across the customer journey.

This kind of granular-level analysis and extraction without AI would have been expensive, manual, error-prone, difficult to scale, and lacks real-time capabilities.

B. AI-powered competition and market gap analysis can help product team to improve product features.

Another use-case where redBus is using AI is competition analysis. Another use-case where redBus is using AI is competition analysis. Through APIs, The aggregates app store reviews and benchmarks specific features against competition’s features i.e. competitors Y’s real-time tracking rated 4.8 vs. our 4.2”

By comparing feature performance, the analysis reveals gaps—for example, redBus lags behind Competitor A in refund speed, indicating a key area for improvement.

C. AI-Generated Summaries & Smart Tagging to Enhance UX

redBus is working on leveraging AI to make concise, meaningful summaries out of complex customer reviews, helping users make faster, and more informed decision. This is a very common use-case across ecommerce. Amazon executes this exceptionally well. While I don’t see this particular feature on Myntra or Flipkart.

D. text-to-SQL tool - Agentic-AI for analytics function and SQL for internal team

The category team often needs to run SQL queries to evaluate various performance metrics — and at times, these can be complex, multi-line queries that require advanced coding.

redBus has made it easier for the internal teams to run queries like, “What’s the order share of the top 10 Bangalore-Hyderabad buses?” which is basically Number of orders for a bus divided by Total number of orders for that route. First you have to filter data by route and a timeframe, then you identify top 10 buses, then you calculate total orders for the route. Phew! You got my point. It’s too much work.

With agentic-AI workflows, you can run query database using natural language. Behind the scenes, orchestrator agents break down the task into intent parsing, metadata retrieval, SQL generation, and dataset merging. It can do complex metric calculation such as calculating order share or conversion rates in a minute.

And all of that, without writing a single line of SQL :)

(Marketers can use AI to get insights - like “What was the sales impact of our last campaign?” or track KPIs such as cart abandonment rates by region— without relying on data scientists, accelerating decision-making across teams.)

E. Smart Filters

The team is currently working upon intelligent, dynamic filtering systems that go beyond traditional dropdowns or checkboxes by leveraging machine learning, user behaviour, and contextual data to surface more relevant results with minimal manual input.

Imagine having a natural conversation with an AI tomorrow, where you casually share pain-points that you've experienced. For example, you might tell redBus’s chatbot that during your last trip, the berth was too short for your height, and since you're tall, you'd prefer a bus with a longer berth next time. And next time you book the redBus, it remembers that you are tall and you would need a longer berth, even when you don’t. Wouldn’t that be a great way to delight customers? :) Making them feel truly valued and understood.

Key implementation insights that can be learned from redBus Playbook :

1.	LLM Selection: Test different models (GPT, Gemini, Claude) for fit; Redbus selected GPT-4 for its accuracy in travel-specific contexts.
2.	Data Privacy: Use synthetic data during prototyping to avoid privacy risks, and switch to enterprise APIs for scalability.
3.	Knowledge Base Tuning: Feed LLMs with structured metadata such as product specs, refund policies, or category taxonomies to improve output accuracy.
4.	Token Management: For large-scale datasets (e.g., >1M tokens), use enterprise-grade solutions like GPT-4 Enterprise.
5.	Agentic Workflows: Break complex processes into modular AI agent tasks (e.g., parsing → retrieval → analysis → output), especially in data-heavy tasks like text-to-SQL.

Companies can use actionable insights and AI-driven feedback analysis to ship new product features and product development. For instance, in case of redBus -

Customer feedbacks that redBus receive are highly specific and contextual. It can be used by Product and brand team for specific targeted improvement.

For instance, if there are getting recurring complaints about “App is difficult for non-English speaker” and there is a significant customer demand at regional-level, the product team can roll-out platform experience in regional language or voice-based search or booking.

AI can be used to close the gap between customer pain-points and product solutioning.

Another example — if customers are consistently commenting on difficulty with live bus tracking when reviewing competitors, redBus can recognize it as a key differentiator. The brand can then double down by highlighting real-time tracking as a core value proposition in its campaigns — turning a product feature into a strategic lever to win market share.

Case study 2 : Razorpay is using Agentic-AI to automate Customer (Merchant) Support to bring cost efficiency and CX

Razorpay serves over 1 million customers and handles a high volume of merchant queries. It needs a robust support model to ensure that these queries are addressed promptly and at scale.

Manual handling of L1 merchant queries (Level 1 support) is slow and inefficient. Customer satisfaction scores is low due to delayed responses. There are complex internal workflows with ~500 conditions based on severity, priority, type of query that needed to route correctly. Then there are multiple team handoff required, creating delays and SLA management.

To solve this problem statement, Razorpay deploy agents that handles L1 merchant support queries automatically. It pulls answers from the internal documents and Freshdesk knowledge base. AI scans incoming tickets against 500+ predefined conditions to determine severity, priority, and the right team.AI scans incoming tickets against 500+ predefined conditions to determine severity, priority, and the right team. Dynamically sets and updates “promise time” (expected turnaround time) based on issue type. It understands the customer tone to frame appropriate response. It then prioritize and intelligently route.

Escalation : Agents can also manage the escalations. If SLA is breached, AI triggers the correct escalation workflow (who to escalate to, which team, etc.). And then track SLAs for each ticket.

If AI can’t resolve the query or the customer is dissatisfied, it escalates to an L2 human agent.

Case study -3 Meesho is working on leveraging AI across customer touch-points.

I had the opportunity to connect with leaders at Meesho who are spearheading AI initiatives. Across the board, they emphasized that AI is not just a tool but a central pillar of Meesho’s strategy. From customer support and seller tools to search, operations, and marketing, AI is deeply embedded in every critical business function. Notably, Meesho has also built an in-house AI Services team with the vision of offering its capabilities beyond the company—transforming internal excellence into a revenue stream.

A. Gen-AI powered Bot for customer support as well as seller support

Similar to Razorpay, Meesho is leveraging AI to drive customer support automation and cost optimization at scale.

Recently, It has launched GenAI-powered voice bot which can handle 60K calls per day in Hindi and English. It is planning to expand this to more regional languages.

We all have experienced frustration talking to Chatbots that keep us asking the same set of questions that feels like waking up in a time loop. Now, imagine the frustration of trying to communicate with a voice bot who doesn’t understand you. It becomes more important to have a voice bot with minimum lag and context-aware and have a real-time conversational capabilities.

The moment it sounds robotic or delayed — like there's clearly no human on the other side — it can instantly break trust for the seller.

This real-time conversational AI that Meesho has set up, it is claimed to have significantly reduced cost per call—currently at just 25% of what human agents cost, with expectations to lower it further to 15% as the system scales.

B. Vernacular search and Discovery

Meesho is building for Bharat. Hence, It becomes important to take India-specific context and languages into consideration.

In case of Meesho, you get search queries like - "lehenga dupatta for sister wedding pinkish red." To run this query, LLM requires high-quality, diverse dataset in Hindi, Hinglish, and regional dialects.

Traditional systems might struggle to understand this . To get the result, Base models (like GPT or LLaMA) need to be fine-tuned specifically on Indian queries, with reinforcement based on correctness and intent understanding.

Once implemented, It can understand what the user really wants and shows better results. Over time, AI also learns from how millions of people search, click, and buy—just like how language tools learn from reading lots of text. This helps Meesho build better systems for showing the most relevant products, improving what you see when you search or browse—making shopping feel easier, more personal, and spot-on.

C. Seller operation (Product listing, images, analysing market trends, and price competitiveness
In a recent talk that I attended at SPC, Meesho’s founder Vidit shared an insightful story about their early days. He spoke about spending significant time visiting local merchant shops that they aimed to on-board. A key insight that he shared: these merchants wanted to expand their business reach beyond the usual 5-kilometer radius but lacked the time, resources, or digital know-how to manage operations online.

To bridge this gap, Meesho initially empowered women running households—those eager to contribute financially from home—to handle these online operations.

Today, that role can increasingly be taken over by AI. AI now can play a central role by automating key functions for the sellers: from cataloguing products and generating images or videos to analysing price competitiveness. AI-powered Chatbots guide new sellers through the onboarding journey in real-time, making it easier for first-time entrepreneurs to set up shop. Advanced AI tool has potential to help sellers optimize pricing based on real-time market trends, while vernacular translation tools convert local-language product descriptions into standardized, platform-ready formats.

D. Operation and supply-chain

Meesho relies on predictive AI models for demand forecasting, fraud detection, and logistics optimization.

E. Marketing

Generative AI is used to automatically create product descriptions, email campaigns, and social media content, significantly reducing time-to-market for new campaigns.

Case study 4: Automating complex task across Martech, D2C

Best way to learn is to do it.

I built AI agents to automate marketing workflows - One of the agents that I created can plan, write, and schedule social media posts from a podcast in just minutes. It understands the brand tone, picks up on trends, and even generates images or hashtags.

If you’re curious about the details, I’ve shared more here :

How My AI Marketing Agents Work

Another agent helps with ad campaigns. It looks at past performance data (Previous campaigns), suggests what kind of ad to run, writes the copy, and even recommends how much budget to spend where.

These are some more agents that I am currently building for clients:

• Working on deploying a 24/7 intelligent AI assistant for a D2C brand website that can handle FAQ, order tracking, return processing, and product recommendations autonomously — without human support (Tools - Whisper API + Gumloop + Zapier + GPT-4)

• Creating an end-to-end AI-powered email system for the D2C brands – Sign In, Trigger – cart abandonment, order placement, Discount campaigns etc.

• Automating entire catalogue operation – Image, Video, Campaign design, content

Key Implementation Insights from my hands-on Experience with AI Agents

1. AI Improves Through Feedback Loops:

   One of the most critical aspects of working with AI agents is enabling continuous learning through feedback. For example, if you're using an agentic-AI framework to manage social media posts, the agents should learn and optimize based on audience engagement metrics like impressions, views, likes, and shares.

   On the surface, the idea that "AI improves with feedback" might sound obvious or even cliché—especially to those familiar with machine learning or AI operations.

   However, in practice, many AI implementations fail precisely because teams don't build robust feedback loops into their systems. Teams may gather insights but don't update prompts, fine-tune behavior, or retrain components regularly.

   A true agentic-AI setup should autonomously interpret results and iteratively improve—without manual intervention. That’s hard to build.

   Many companies deploy AI agents without clear access to post-action metrics (e.g., engagement per post, click-through rates, etc.).

   When I was working on AI agents to convert YouTube podcasts into social media content, the initial version didn’t include a feedback loop. I incorporated it in a later iteration.

2. Establish Clear Guardrails:

   
   It's essential to put guardrails in place to ensure AI agents operate within defined ethical, brand, and performance boundaries. This helps avoid unexpected outcomes and ensures reliability. For examples, if you are building an agent that can automate campaigns, you might need to incorporate brand guidelines.

   But more on this in the part 3 of this series in which : I will lay out the AI safety framework and non-negotiable guardrails that e-commerce and marketplace companies must establish to build responsibly.

   If you’d like to receive the part-3 of this series as soon as it is published, make sure to subscribe to the newsletter.

   
   

   My suggestion to build an AI agents or agentic-AI - start with repetitive tasks:
   

   Identifying repetitive, time-consuming tasks within your organization. AI agents are highly effective at taking over such tasks, which often take humans hours or even days to complete, enabling teams to focus on strategy and creativity. Define the goal, break down those tasks into clear, structured workflows with defined inputs, outputs, and decision points, identify the right tool (APIs, LLM model), deploy and test iteratively.

The decreasing cost of computing and storage is making possible for companies to deploy AI across functions. AI is not a moonshot anymore, it’s a table stake. Companies that act early—experimenting, learning, and iterating—will shape the next era of digital transformation.

With this final thought, I say adieu. I would love to connect with more professionals who are building and implementing agents and learn more about the use-cases in the Industry. Feel free to DM or comment below.

Keep learning, Have a great weekend🎉

Thanks,
Neetu
Thinking in Categories | LinkedIn | X

© 2025 [Neetu Barmecha]. All rights reserved.
This content is the intellectual property of the author. Do not reproduce without permission.

There are inflection points in history when the way businesses operate changes fundamentally.

In the 1980s, Internet made it possible for people to go online, access information and communicate through Transfer Control Protocol/Internetwork Protocol (TCP/IP). It laid the groundwork for how businesses would operate in the years to come. A stack of papers on the office desk was replaced by a square-looking, mainly, white computers and the primary mode of communication to bosses became emails even when your boss sitting just a cabin away.

In the 2020s, another world event – global pandemic pushed even small merchants in India to move their businesses online since offline operation was no longer a possibility. Start-ups were awarded handsomely- those that were able to align themselves with customer needs, adapt to market shifts, and respond with agility.

Another such world event is currently unfolding in the form of Generative AI.

One thing is very clear: from the historical shifts and learnings. We must be ready for a human-AI collaborative future. Because the genie is out of the bottle.

My journey of learning AI and its tools started when ChatGPT appeared few years back like an omniscient GOD who is like that friend of yours who knows-it-all but somehow never manages to irk you. (The version of Sheldon Cooper that his friends would like) and in the near future, can do it all.

In the last few months, I have spoken with dozens of business, tech leaders and on-ground operators who are figuring out how AI will impact their business, how they can leverage AI to drive revenue and bring out operational efficiency by deploying across workflows and use-cases (specifically in the context of my work, role and Industry).

In this 4-part AI special series, I’m going to decode the conversation and distill my learnings.

Part 1: I will construct scenarios and examine the likelihood of fully-autonomous agentic-AI taking over how we shop.
Part 2: I will deep-dive into real-world case studies (mainly in ecommerce, marketplace, and digital –native companies) about where and how AI is getting is deployed across workflows.
Part 3: I will lay out the AI safety framework and non-negotiable guardrails that e-commerce and marketplace companies must establish to build responsibly.
Part 4: I’ll explore how AI can power sustainability across the e-commerce value chain and the paradoxical relationship of AI with sustainability.

On to the task at hand:

For all the history buff there - let’s trace how the marketplaces have evolved through the ages. And, then let’s draw a line to the future. ⌛

Marketplaces have evolved/are evolving through 3 distinct phases - each driven by a major technological shift.

Marketplace 1.0: The earliest form of marketplaces was physical. Imagine the vibrancy and hustle-bustle of Indian Haat Bazaars or ancient Greek Agoras or Trajan's Market or Markets of Mesopotamia. Whichever market you imagine, you know that it is all about humans interacting with other humans. Haggling over whatever it was they haggled over. They were the commercial heart of the early civilization. As centuries passed, it evolved into fancy malls, shopping centres, and mom and pop stores.

Then came the mail-order catalogues. Telephone-based shopping. But the operating model was same: human interacting with other human beings. The promise-land of full-on digital was still few years away.

Marketplace 2.0: In the year 1995, the launch of eBay marked the beginning of a new digital world where people started buying online. Amazon sold books online. Later, niche vertical marketplaces started cropping up. Now we are living in a time when there is a marketplace for everything. Uber, Rapido for drivers. Airbnb, MMT for hotels and accommodation and so on and so forth.

The phase can be broadly defined into 2 phases:

1. Listing-based platforms like Craigslist, where users could post products or services; and
2. Transaction-enabled platforms, powered by tools like PayPal that simplified digital payments.

These platforms evolved into sophisticated, multi-sided ecosystems, leveraging behavioral data, API integrations, and real-time feedback loops to seamlessly serve buyers, sellers, and service providers at scale.

All in all, this new era of marketplaces was made possible because of the invention of Web 2.0 and thrived because of platformization.

Marketplace 3.0:

Every retailer must become an AI retailer." - Marc Benioff (CEO, Salesforce)

During the discussion with the founder of a new marketplace I advise, the founder raised an intriguing question: “What will the marketplace look like in the near and long-term future?"

Right now, you look for an information on the internet, it follows a predictable set of steps: You open a browser, type the query and keywords into the search bar, and then scroll the results. While you do that, you also hope that you don’t fall into the rabbit hole of endless tabs.

But now, I am sure that you are acquainted with the Generative AI models/interface available - ChatGPT, Perplexity, Claude, or another LLM-powered assistant. These models synthesize and deliver curated content with contextual answers in natural language. It saves you the joy — or agony, whichever it is for you — of scuttling through webpages. Apart from occasional cases of hallucinations, bias, inaccuracy, or patronizing you by responding – “Great question!” it just works.

Dear readers of Thinking Category,

Exciting news 🥳📘🎉I published a book recently - Thinking in Categories. The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

Here you go :)

Thinking in Categories

It is available in India via Kindle. Outside of India, it is available on paperback as well. This book is the only book that you need if you are aiming for Amazon, Flipkart, Walmart, Myntra or category/marketing manager roles in E-commerce.

The ChatGPT of shopping : The future

For long, amazon has taken pride in offering the world’s largest selections to its customers. The flywheel of amazon counts on it, moves because of it.

Does it overwhelm, confuse, or slow down decision-making for the customers? We might need contrarian thinking there.

So now imagine a sleek, intuitive interface – much like these model – where you don’t scroll through the digital equivalent of endless aisles, put one filter after another, and glance view hundreds of irrelevant products.

You just type - “I need a branded blue colour formal shirt under 2000 Rs.”

The platform understands your preferences, context, and intent. Multiple agents working in the background on your behalf. They know your size, style or colour choices.

This is Agentic-AI in action: Orchestrating the entire customer journey from discovery to checkout. Even completing transaction process. It has one objective: Get the shopping done. You don’t need to be rich after all to get your own butler.

In this future, AI will be the primary interface between buyers, sellers, and commerce infrastructure. A complete tech-stack overhaul to fully leverage agentic-AI might not be required. Companies will implement AI as flexible, and parallel layers that augment their existing tech-stack. Tech-stack required to enable the marketplace 3.0 would change from traditional e-commerce infrastructure to AI-native architectures:

Legacy rule-based workflows → Self-learning, dynamic decision engines
Manual merchandising → Autonomous selection and bundling
Static UX flows → Conversational and multimodal interfaces 
Fragmented data systems → Unified real-time customer intent models

Marketplace 3.0 is about reimagining everything from catalogue operation to transaction. We will discuss this in the Part 2.

Let’s have a poll. I am curious to know what you guys think:

To paraphrase James Thurber, “One poll is all right. Two are too many, and three are not enough.”

Till the time I get sufficient datapoints to draw a conclusion : To test my hypothesis, I asked this question to LLM models - ChatGPT, Claude and Deepseek.

“Based on the current technological, societal, and economic trends, what is the estimated probability that a majority of routine consumer shopping will be delegated to autonomous AI agents (requiring no per-transaction human approval) within the next 10-15 years?"

Response from ChatGPT:

It estimated 65% probability that the future of shopping will be autonomous agents. It is based on -

1. Technologic trends, Infrastructure and rapid AI advancement

• Advanced AI models - High maturity of large language models (LLMs), coupled with the emergence of autonomous agent frameworks (like AutoGPT, Devin, and OpenAgents)

• Advancement in recommender system

• Wallet integration

• Decreasing AI cost

2. Consumer needs to reduce decision fatigue and time spent shopping

It also stated that this will be at the very least possible for the routine/grocery/household product that consumer buy frequently.

Response from Deepseek:

While ChatGPT and Deepseek were more optimistic about this form of shopping, Claude, not so much.

Response from Claude:

Reasoning: Privacy concerns. “Nearly 82 percent of consumers say they are somewhat or very concerned about how the use of AI for marketing, customer service, and technical support could potentially compromise their online privacy.”

ChatGPT not only provided estimation at the macro-level, it drilled it down further by region and country-level probability:

Let’s do another exercise – Scenario Building. We will map out different scenarios -Unlikely, Realistic, and Optimistic futures of agentic commerce—based on technological, regulatory, and behavioural factors.

These scenarios are based on 3 components:

• AI Agent Autonomy (how independently the agent operates)

• Consumer Involvement (how much input the user gives)

• Regulatory Maturity (how evolved and enabling the regulation is)

Scenario Building is about trends spotting. In the below section, you will find some trends emerging from the conversation.

From Trends to Tomorrow: What Industry Leaders Are Saying

• The Wall Street Journal (WSJ) reported on Walmart- The retail giant is exploring how to make its products not only attractive to human buyers but also to autonomous AI agents that may soon handle shopping tasks on behalf of consumers. By Jerry Sheldon, retail analyst at IHL -

• Andy Jassy of Amazon CEO emphasized shopping as a key area for AI disruption- Increasingly, you'll see AI change the norms in coding, search, shopping, personal assistants, and a lot of other customer experiences.

• Airbnb CEO Brian Chesky on how Artificial Intelligence can be used to personalize experience for the customers. He said in one of the podcasts :

• Garima Singh - Chief Architect of the Ingka Group explained how Ikea is leveraging AI as a co-pilot to improve CX -

  You can sit on your sofa and take a picture of the space that you want to renovate or design, then you can completely erase the room and redesign it with the help of our AI-powered digital assistant. Or you can select one of the sofas or chairs from our catalogue and place it where it suits you best. Then would you like to combine it with this carpet or that painting? This particular feature has helped our customers to design a space virtually without visiting the store, and that really enhances their creativity, rather than replaces it.

This is all for the day. As I write this, I hear news of AI refusing to shut down. So I just hope that whatever the future of shopping is, we live to see it 🙂

In the next post, I plan to discuss business use-case specific to ecommerce, or digital-native companies and how AI is going to redefine consumer experience (CX). But, more on that later. If you’d like to receive the part-2 of this series as soon as it is published, make sure to subscribe to the newsletter.

Subscribe now

Until then, see ya! And, I'd love to connect, exchange ideas, and learn more if you're building or working on something exciting in the Agentic AI + commerce space. DM me at LinkedIn.

As an ending note, some fun reddit comments for your lazy weekend afternoon:

Have a great weekend🎉

Thanks,
Neetu

© 2025 [Neetu Barmecha]. All rights reserved.
This content is the intellectual property of the author. Do not reproduce without permission.

Thanks,
Neetu
Thinking in Categories | LinkedIn | X

Last few days of year 2021…..hope you had a good year - a year of good memories both in personal and professional life.

About the same time last year, I started thinking category and made a sort of new year resolution that I will write more and use this platform to learn and share my learning with everyone.

So far, I wrote 30+ posts (On Average 2.5 every month). Here I am after one year with 2000+ subscribers 😍. I am grateful to everyone who were the part of my growth journey this year. Many Industry leaders, entrepreneurs, and category leaders have shared their time and their rich experiences in the industry with me, and that made my journey more enriching and worthwhile. Thank you, all. 🙏

If you are not the subscriber yet,

Subscribe now

Seriously, do subscribe 🙂

Now, on to the last post of this year. I hope that you enjoy reading it.

The dynamics of Scarcity

Scarcity is one interesting topic that is close to my heart. In the book - Psychology of money, Author Morgan Housel writes,

Spreadsheets can model the historic frequency of big stock market declines. But they can’t model the feeling of coming home, looking at your kids, and wondering if you’ve made a mistake that will impact their lives. Studying history makes you feel like you understand something. But until you’ve lived through it and personally felt its consequences, you may not understand it enough to change your behaviour.

The author talks about how the experience of living in the depression era - an era of scarce resources and financial instability - influence the risk appetite of a person in the context of making investment decision even in the long-run. Your experience in the past stays with you even if things are not the same anymore.

Scarcity (In the past) not only affects the way we think about money, but it could also influence the way we buy or consume. Marketers use this concept to create a sense of urgency in the mind of customer/buyer and that leads to improved conversion rate / velocity of purchase for their products.

You might have noticed on Amazon prime - “Expiring from Prime in the Next 30 Days” or by so and so date. This might have made you to watch something due to fear of disappearance from the platform.

I believe that most of you are also familiar with the lightening deals (Amazon) or limited time offers on the marketplaces. Before we get into it, let’s look at product lifecycle.

Life of a product :

We know that product goes to distinct stages in the market (Depending on the product, product category etc.)- When new products are introduced in the market, initially it might see some slow growth since the product is not known. With marketing or word of mouth marketing, it would experience jump in the demand. And then there occur some time when the demand is declining. This is liquidation or markdown period.

In this stage, the brand can introduce some promotions or flash sales that could boost the sales artificially for the shorter period of time. Is it a good time for lightening deals if you selling through marketplaces?

Leave a comment

What is the lightening deal (Flash Sales) ?

I believe the term has been coined by Amazon. It has basically two elements:

1. Limited time (a short time window within which the promotion is running)⌛

2. Limited quantity (Artificial supply constraint) 🧱 🧱

For example - Above shown promotion will end in 6 hours. Discount - 74% off. If you add one more bar showing the remaining quantity left, that will add supply constraint element also.

Economics of the lightening deal:

Let’s try to look it at from supply-demand curve perspective.

In normal situation, the quantity in demand is 60 at Price point 100. At the time of lightening deal with 50% of discount suddenly the demand has increased to 80. Hence, the sudden rush of customers to claim the deal. (Limited quantity + limited time frame ⌛ + High demand due to low price).

Why this is an effective promotion strategy for marketers, marketplaces and sellers :

Loss aversion : Loss aversion theory in this case would mean :

Pain you will feel if you miss this discount opportunity > The pleasure that you will get if you avail this offer

Hence, the tendency to not miss this limited-time opportunity. You might feel a sense of urgency. You can call it FOMO or whatever, the point is that

This leads to

• Higher inventory turnover of otherwise slow moving inventory

• Volume —> Sales, Profitability ❤️

• New sellers with less reviews or rating can use this feature to improve their rating since more sales will happen due to limited deal offers.

• Higher visibility on the marketplaces platform for sellers / brands

• Improved conversion for marketplace or ecommerce platforms

How?

Let’s discuss what all aspects to be covered if you are working on limited deal offers in a marketplace environment. This is from a marketplace point of view.

Lightening deal construction :

A snapshot of how multiple lightening deals looks like on Amazon:

To configure the lightening deal at the marketplace, you can use this framework:

That’s about it. I hope enjoyed reading it. Please do share with your colleagues who might be interested in this 👇:

Share

Also, if you have been involved in developing such limited deal offers, would be happy to do some chit-chat.

Merry X-Mas 🎅🎄and Happy New year to all 🥂. Would see you all in Year 2022 😊.

In case you are new here, welcome to Thinking category. I would suggest you read some of the most liked posts by our readers:

1. Category creation - The story of bold and creative socks - Stance

2. Open to buy (OTB) plan - a tool to manage your inventory better - Part I

3. Customer Lifetime value - 80% focus to 20% of your customers

4. A Japanese concept to delight your customers

✨ 2000+ 🎉people have subscribed to this newsletter so far. To read on FMCG, category management tools, consider subscribing🙏:

Subscribe now

Today’s post is about the reward loyalty program, hope you like it. In case you have an experience in setting up such programs, would love to hear from you.

Leave a comment

Let’s start with the basic question, Why loyalty program? These days every large retailer are running some form of loyalty reward program. Almost all mobile app have such programs - gpay, Paytm scratch card..

The basic idea is “Loyal customers, frequent purchase, higher CLV”.

Customers win points when they join the program, or when they make a purchase, or when they refer a friend, or when they have collected stamps in a gamified reward program depending upon your reward mechanism.

Here is the link of an HBR article if you want to read more.

With each additional year of a relationship, customers become less costly to serve. Over time, as the loyalty life cycle plays out, loyal customers even become business builders: buying more, paying premium prices, and bringing in new customers through referrals.

Now, let’s play devil advocate 😈.

From another HBR article,

Loyalty programs — that effectively bribe people into buying more of your products — are lazy.

Subway, the restaurant chain, got rid of its Sub Club cards, which allowed diners to earn a free sandwich after purchasing eight. In Australia, Coles supermarkets phased out a program that rewarded owners of the company’s stock with merchandise discounts ranging from 3% to 7.5%. Online phenomenon eBay quietly pulled the plug on its Anything Points program for U.S. customers. American Airlines and America Online jettisoned their joint customer-loyalty program.

Are reward loyalty programs just another gimmick- a short-term fad that enables economic transaction for a short-term window till the program is running and the customer disappears as soon as rewards disappear.

My Take : For a new product in the market with so many competitors in the market, an effectively designed loyalty program could be helpful to gain initial traction (a hard problem to solve).

The framework of a reward loyalty program

Let’s start with a real-world consulting assignment: A Direct-to-Consumer (D2C) brand which sells health drinks 🧉 in the market was struggling to retain customers. They wanted to develop a strong customer acquisition strategy and they wanted to focus on their loyal customers.

A reward and loyalty program would not only enable them to provide additional benefits to their most loyal customer but also help them to get customer data and analyze the habits of frequent shoppers on their platform.

Step 1: Define the Objective of the loyalty program

What do you want to achieve through the loyalty program? This is the first step to develop an effective loyalty program.

Loyalty programs are designed for many purposes - Marketer also uses these programs to incentivize participants in market research survey besides to drive customer loyalty. HR uses such programs for employee rewards and benefits.

So, how do you get there? Talk to internal stakeholders to understand what is it that they want to get out of the loyalty program. What is the most important metric for them? What is the North star metric for the business?

Although North Star metric might not be the objective of your program, knowing what is your company goal in the long run will put things into perspective. Your objective could be to gain new customers, keep your customer engaged with your business, or improve customer satisfaction by providing additional value to your most loyal customers.

Whatever it is, quantify it #️⃣. In a particular timeframe ⌛.

In the case of the D2C health brand, during the conversation with the business leaders, it came out that they want to build a community of health-conscious people. Although the primary goal was to create awareness about the product, they were aware that customer retention was the key to the success of the product. Their most loyal members (or high users) can get access to elective community content and forum.

Their objective was to improve customer retention by 20-25% by converting users to the most loyal members.

Step 2 : Identify your target customer using RFM analysis

RFM analysis can help you to find out your most loyal customers, who should be awarded additional benefits for their loyalty to your business. It will also indicate to the users who should from one group to another. Not loyal to loyal.

Consider sharing this with your friends and colleague,

Share

Step 3: Reward structure, differentiating points

Tie your reward structure to your customer segment.

(A) Reward benefits or catalog

it’s important to understand how will the program create value for the customers? There are so many loyalty programs. Are you different or just part of the bandwagon?

Starbucks Reward points💯 : Star

Starbucks has been running the stored value card program since 20008 and it is considered one of the successful loyalty programs.

Customers can order and pay through the Starbucks app before reaching the store location. And they can buy a free drink, food or merchandise using the Star earned in the app. From the customer perspective, ability to order and pay without staying in line is a huge incentive for them to become the loyal member of the program.

(B) Triggers <—> actions/benefits and validation rules

You can set automated rules for behavioral trigger and assign benefits to each activity taken by users.

Step 4 : Loyalty Budget, Financial forecast, ROI analysis to check the financial feasibility of the program

Loyalty points are to be assigned a real value. The exchange of points for tangible / Intangible benefits is a cost to the company and the same is accounted in the balance sheet as Loyalty program liabilities and revenue.

In Starbuck annual report,

Deferred Revenues - Our deferred revenue primarily consists of the up-front prepaid royalty from Nestlé, for which we have continuing performance obligations to support the Global Coffee Alliance, and our unredeemed stored value card liability and unredeemed Stars associated with our loyalty program.

The point I am trying to make here is, loyalty programs are complex from financial POV. And most ignored part also. You might want to check a fiasco turned into a lucky chance in “Golden Ticket - The office” before you design the program. What a genius idea from Michael 😂!

It’s very important for retailers to have some idea of redemption value, the timing of redemption, and how many points are likely to be expired or not redeemed by users.

Calculate Program revenue = Additional revenue from members of the program. If it is more than program cost, which can be calculated from redemption value, % breakage (1-URR), admin cost, the program can be considered as successful.

We will discuss the next 3 steps in part II.

Step 5 : Product development (Loyalty as a product feature) or 3rd party integration

Step 6 : Product launch and in-app marketing

Step 6 : Monitoring the success of the program

Also, check Sephora Loyalty Program.

Wish you a whimsical and adventurous day at office 🔥💪 :) Comment in case you have any suggestions or comments for me. 👇

Leave a comment

First of all, thank you for your consistent support and for sharing the newsletter with your friends and colleagues in FMCG, e-commerce space 🤩.

We are now a community of 2600+ subscribers 🎉🎉🥂and growing every day. 30+ posts in 10 months. On average ~3 posts every month.

I’ve toyed with the idea of writing in the category management space for the last 4-5 years. But thanks to Netflix, Prime, and other entertainment opportunities, I have preferred lazing around in my free time. Pandemic was an eye-opener in many aspects of life - There is no tomorrow.

Do subscribe if you haven’t yet. 🙏

Subscribe now

now off to today’s post.

In the previous posts, I have touched upon the aspects of Trade marketing and distribution as one of the important levers of category growth.

In one of the study it was found that the impact of distribution on brand metrics such as awareness, liking etc. is 11 times larger than advertising.1

From the brand perspective, how do you ensure the effective distribution of your products in category at the level of retail stores?

Some of the metrics that can help to evaluate brand availability and effectiveness of distribution strategies are:

• Numeric Distribution

• Weighted distribution

• Share in shops handling (SISH)

As a category manager in FMCG, it’s important to know how your brands within the category are doing across distribution channels. These metrics will help to take corrective actions to improve topline and bottom-line (by reducing expense in unimportant channels or outlets)

WHAT IS NUMERIC DISTRIBUTION?

If you want to improve the distribution situation of your products, you can simply choose to be present everywhere. Numeric distribution tells you in how many stores are you present.

If you are selling Brand X belonging to the Bag category, and there are 1000 stores (or POS) selling Bags and your bags are being sold in 100 stores, the numeric distribution is 10%.

Numeric distribution = No. of stores your brand is present (and sell your brand category) / No. of total stores that carry any brand of the category in scope *100

- You can further add nuances such as stores that keep at least 10 SKUs to be considered in the equation to make it more reasonable. Or stores that sell at least 5 SKUs on weekly basis will be considered in Numerator.

- When you do a comparative analysis, if your no. is too low, it shows the poor distribution. While if the no. is too high you might spread yourself quite thin. For example, if your brand is present in two stores located close to each other, you might not be adding anything from the sale contribution perspective.

- If you have region-wise numeric distribution, you can identify barriers for market development in a particular region.

Hypothetical case : this is how the distribution for your brand (Brand X) and your competitors (y, z) looks like :

Store C and D, each keep only 1 brand. And there are only 4 stores that keep the category the brand in question belongs. Numeric distribution for Brand X is 75%, higher in comparison to other brands.

But does this tell you if you are present in “key” stores where sales are high? Presenting in most of the stores while missing from the stores where consumers goes mostly might not be a good idea. Weighed distribution is the metric that lets you evaluate this.

Also, I have published a book - Thinking in Categories. The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

It is availble in India via Kindle. Outside of India, it is available on paperback as well. This book is the only book that you need if you are aiming for Amazon, Flipkart, Walmart, Myntra or category/marketing manager roles in E-commerce.

If you get a chance to read it, I’d be truly grateful for your feedback. And if you find it useful, an honest Amazon review—even a short one—would mean a lot and help the book reach more readers.

Add to cart

WHAT IS WEIGHTED DISTRIBUTION?

Weighted distribution tells you the quality of your distribution. If the weighted distribution is low and numeric distribution is high, it means that you are available in most of the stores but you are not present in stores that matter. (In short, stores are weighted by the importance to calculate weighted distribution)

Let’s expand on the same case,

Store B is the important store from the category sale perspective and all the brands are available there. (All the values in rupees)

Brand X is present in store A, B, C where total sales is =150+240+10 = 400. Total sale is 150+240+10+20 = 420.

For brand X, weighted distribution is 95%. Simply what it means is, that Brand X is sold in stores which accounts for 95% of category sales.

This shows that Brand A is present in all the stores that matter. Sale in Store C is only 20 so it doesn’t matter if Brand x is not present in Store C.

But assume that this is the case:

The weighted distribution of Brand X has gone down because it is not present in the Store C and that is an important store from category sales perspective.

On the other end, even though numeric distribution for Brand z is 50%, same as Brand x, y but its weighted distribution is higher because of its presence in store C which is an important stores from category sales perspective.

SISH is calculated by dividing the brand sales by the total category sale of stores that sell the brand in scope.

Here the denominator is the same for Brand X and Y because both the brands are available in the same place in this hypothetical case.

Brand Y SISH is higher. This shows that consumers prefer Brand Y more if both the brands are available. SISH brings consumer perspective into the equation.

You can combine all this information with other indices such as out of stock index, geographic spread, the share of shelf and get some interesting insights to improve the brand availability further.

Would love to hear your distribution strategy:

Leave a comment

Have a great week :)

Few months back, I have posted about subscription programs by marketplace companies.

Then, we have discussed how to get the pricing right - concepts of supply and demand dynamics, value-based pricing.

And we talked about using CLV, customer retention rate as a tool to identify profitable customer segments for the business.

Today’s post is going to bring all of it together.

Subscription economy has been the talk of the business town. You can now subscribe to Netflix, Prime to satiate your entertainment needs. You can get Furniture on Rent (monthly subscription) from Furlenco, you can subscribe to Zoom car or you can replenish your milk bottles daily by subscribing to Country delight. All kind of subscription business exists.

image source - google image

The Subscription Economy® has grown nearly 6x (more than 435%) over the last 9 years according to Zuora Report (US market)1. A trend that is getting trickle down to other emerging economies as well.

A few days back, I was talking to a friend who was contemplating between buying furniture and renting furniture. In the total amount he pays for 12 months for the monthly subscription for furniture on rent, he can own the furniture instead. It doesn’t make sense from the financial calculation perspective.

Then there are stories like these:

Blue Apron’s CAC sat around $60 — less than the CAC of its main competitor, HelloFresh, which sat around the $80 mark. But even though it had an initial cost advantage over HelloFresh, Blue Apron struggled to hold onto its new customers, who were happy to take the free or discounted boxes that were so heavily promoted but often canceled after their first shipment. A staggering 75% of customers canceled their subscription within six months, meaning that Blue Apron failed to make a profit on them.

Still why so many subscription business models- Answer is Recurring payments. If you can keep your churn rate lower (Ideally ~5 %), you can get it going with lower CAC.

So, How to crack the subscription business?

My 2 cents:

• Target a niche segment for the services which are otherwise difficult to procure and equally niche

• Get your pricing right

So let’s start with a fictional business case.

You are running a subscription business for organic beauty products. Your target market is actor / actress / models / wealthy individuals / performance artists for whom looks are necessity. You sell only organic products with the promise of no chemicals, no skin irritation. It’s a premium product line. You have carefully curated the product from the supplier base that don’t sell in bulk /masses difficult to find them otherwise. It’s a monthly subscription and the package quantity is carefully designed to last only for a month based on the survey / research you have conducted with your target segment makeup artists etc.

Comment below what products or services could be sold under subscription based business model that are not currently being sold as a subscription to consumer. And why?

Leave a comment

Now the question is how do you price it?

Your effort to find these products should be factored into it - You are reducing the search cost and making these products accessible for your niche segment who otherwise have to order from multiple sources for their beauty needs.

Note : This is a very basic model. So in case you are advanced marketer, it might not be interesting to you.

Pricing model :

(1) Calculate COGS & Fixed cost

(2) Add gross margin into it. You can roughly estimate the value of search cost that you are reducing for your customers. (The effort that has gone into finding the right suppliers, convenience cost for your target segment). You can place $ for each value differentiator / feature to calculate profit margin. For example - you can ask “how much will my target segment to get their hand on organic beauty products?” “If they have to themselves source from different suppliers, how much time it is going to cost to them?”

Sorry to interrupt you while you are reading so ardently If you like what you are reading, please subscribe to my newsletter. You will directly get these bi-weekly posts straight in your inbox.

Subscribe now

You can also connect me at LinkedIn:

LinkedIn

Let’s say you have rounded it to 10000 Rs. We are providing the premium service, hence we can go higher on our profit margin.

If some customers are dropping from the subscription service, you can always give some discounts (I wouldn’t suggest this in this particular case) to bring them back so have that cushion in your profit margin. Secondly, have your pricing right because later on if you want to increase your price, it would be difficult to convince your customers to pay higher who are accustomed to your previous pricing model.

(3) Let’s assume marketing cost here is = Customer acquisition cost (To keep it simple)

CLTV of the customer who stays with you for 12 months (8% churn rate):

= 3289 (Net profit for the first month)+ 4089 (Retained profit)*11 (for the next 11 months, you will be saving on customer acquisition cost. So 4089 (3289+1000) is your retained profit)

If you renting something, you can use PMT to calculate periodic payment / subscription fee. <Don’t divide annual cost by 12 to calculate monthly EMI, seen people doing that mistake>

As you scale, your CAC goes up. As you grow, CAC should stay stable to generate more value out of your business.

(4) Churn Rate Calculation

For above subscription database, churn rate of Jan month = 1/5 = 20% (Monthly churn rate)

Customer 1876 cancelled his subscription. And 1864, 1860,1859 - two new customers were added but for month Jan they will not be factored into the calculation.

Churn rate is important for subscription business. If Churn rate is higher, your investment into acquiring customer is the lost dollars.

If you run a subscription business and a customer has churned out from the business but after some gap - 6 months or so, she has again started your subscription services, Marketing efforts towards bringing her back to the business would be considered in CAC or retention budget? Would it be considered a fresh start for the customer - a new customer? IS this distinction important from data collection point of view? Leave a comment below.

Leave a comment

Cohort analytics can help you to understand the behavioral characteristics of users who higher churn rate. For example - May be you find from your dataset that users with annual subscription stays longer with you compared to monthly subscription. Once you know the reason, you can design better churn rate reduction interventions like pricing strategies that makes annual subscription more lucrative for users.

In case you are interested in more complex models - cohort analytics, customer retention analytics, annual recurring revenue (ARR), ARPU, comment. This way I would know that you are really interested and It’s worth investing my time into writing about these.

Leave a comment

Till then, Happy Weekend.

If you are looking for something funny and light to binge upon, watch Nathan for you for all the crazy/extreme marketing ideas/gimmicks.

A very warm welcome to my newsletter : Thinking category. These are some of the popular posts on this newsletter that will give you some ideas on why “Thinking category”.

1. Story of category building from 0 to 1:

Thinking category

Taking a new consumer category from 0 to 1 - Smart'O Bite

The trend of smoothies can be traced back to 1930 when the blender has been invented by Steve Kuhnau. And the earliest consumption of oats is dated as far back as 32,000 years ago. Smart'O Bite, an entrepreneurial venture in food Industry, has brought these 2 things together and launched a new product category - oats smoothie - the smooth mixture of oat…

Read more

6 years ago · 2 comments · Neetu Barmecha

2. On CLV, CAC

Thinking category

Customer Lifetime value - 80% focus to 20% of your customers

Hello Readers of Thinking Category, I hope you got vaccinated, are healthy and Life is coming back to normalcy for you. Last few months were intense so I took a pause. To reflect. But for now I am back with another post. If you just landed to this newsletter, I would encourage you to explore some of the early posts…

Read more

5 years ago · 1 like · Neetu Barmecha

3. And the adventurous story of SKUs from store room to shelf

   Thinking category

   The journey of SKUs from storeroom to shelf - a true tale of adventure

   Dear Reader, If you have just subscribed to Thinking category and wondering what we are? Well, for the background story, read this and then read on.For Old folks, these are few gems based on the most visited/commented (LinkedIn) / received DM articles, in case you missed…

   Read more

   5 years ago · Neetu Barmecha

Now off to the this week post,

Dear Readers,

If history has taught us anything, it is that every world crisis has changed the way we think, we work, our relationship with money or the way we live : Black death led to the improvement in agriculture technology, Industrialization.

The role and the status of women in workforce participation changed during the world war because men were fighting away and women took on new roles - administration (Defense), healthcare services, manufacturing.
(Case in point - The imitation game - Role of Joan Clarke as a code breaker in the Enigma project)

Subscribe now

Back to 2020 : Pandemic has changed many things for all of us including the way we shop.

Many companies have shifted their attention from in-store experience to ecommerce channels (consumer web, app) in the last one year.

So we, category leaders of tomorrow, need to learn navigating the new waters of commerce : Online advertising, ecommerce marketing. The world of online is overwhelming - There are so many advertising tools and so many platform. Too many tools. Need to have a proper ecommerce marketing strategy in the place.

Within the role of category management, either you will be working with the marketing team (which manages overall category marketing spend) or you will be responsible for your own category marketing strategy or execution depending upon the structure of your organization (legacy organization, startups).

Whoever you are, it’s good to know the basics.

The way we will be doing is - we will take a problem statement and discuss the possible tools. (Unlike going through all one by one, or how to do guide; you can easily get all these information on the Internet)

Problem statement 1 :-

I am a category leader and I sell shoes and bags online. I need to Identify which of my product categories are surging in Search results. (Chrome). It will help me in understanding the demand and developing my category plan.
Tool - Google Trend (Free tool, very easy to use)

The graph shows that in the past few months, people have searched shoes more online compared to bags (google search engine) and Mizoram, Meghalaya, Manipur, UP, Rajasthan were the top states in terms of search results for keyword - shoes.
It might not mean that people are buying more shoes online, but it gives you some idea of market trends.

You can also dig deeper what people are typing and looking for. These results will help you

1/ in understanding what kind of products people are looking for - consumer preferences

2/ in campaign management - keyword ideas, target segment, geography etc. So if you know that people are using “cute” as a search term, you should include that in your keywords list / campaigns.

So now you know, Mirraggio bags are in demand. Lets type that in your chrome. depending on the geography and other factor you can get different results. But let’s assume this is what you got (below picture).

So here comes my problem no. 2.

Problem statement 2:
I am an online retailer and sell luxury bags online. When my target segment looks for bags online, first picture that they see is of my competitor. My goal is to improve conversion and ROAS while keeping my CPA (cost per acquisition) low. I want to have image ads instead of text ads.

Tool: Google shopping campaigns

Through Google shopping ads, you can show the photo of your products, price and other details like special offers.

You can check how to set google shopping campaign here.

Problem statement 3:

I am a toy manufacturer directly selling via Online marketplaces. I make stacking multicolor rings for 1-3 years old kids. As I don’t have a dedicated website so I can’t run shopping campaigns to drive traffic to my product listing. When I type toys for kids on Amazon, my product doesn’t show up in first 5 pages. Forget the buy box, it doesn’t even appear on search results.

Tool - Amazon ad

Sponsored ad will appear on the 1st page of the search result. There are automatic targeting, manual targeting options available. There are different formats available - Video ads, Audio ads, sponsored products, sponsored brands - whichever suits your budget and your marketing goals. If you are a new unknown brand, sponsored ads will help you to improve your brand / product visibility and get you a start.

So basically there are tons of tool. And we will talk about that as and when.

Wrapping this beautiful Saturday night with the quote from the most amazing but unfortunately fictional boss Jacqueline Carlyle of the bold type :

“Here's the next great mascara—to give you bigger eyes to see the world. Here's a fabulous pair of jeans. Now go climb a mountain.”

To the pair of jeans, mascara and mountains !!

If you liked this article, Please share thinking category with your friends and colleague in ecommerce, FMCG space:

Share Thinking category

Sayonara. You can connect with me on LinkedIn :

Connect with me

Would be happy to know your thoughts, or your experience in FMCG, ecommerce, consumer space.

I also write about my life experiences here.

If you have just subscribed to Thinking category and wondering what we are? Well, for the background story, read this and then read on.
For Old folks, these are few gems based on the most visited/commented (LinkedIn) / received DM articles, in case you missed:

1. Taking a new consumer category from 0 to 1 - Smart'O Bite

2. How to get the pricing right - pricing strategy for consumer products

3. A Japanese concept to delight your customers]

And thank you for so much love for the last post on markdown strategy : https://thinkingcategory.substack.com/p/the-curse-of-expiry-dates-and-thank

A snapshot of the comments that I received on LinkedIn. Thank you for the kind words, help me to get going.

And now off you go about today’s post.

Pardon the long headline, I felt like a Bollywood writer today. Believe me, the story of Shelf replenishment is as thrilling as some Bollywood movie. it’s filled with battles between brands, merchandisers, negotiation, manipulations.
What goes behind the scene? Let’s see.

Do subscribe in case you are interested to know more about e-commerce, FMCG space

Subscribe now

WHAT IS REPLENISHMENT
Replenishment is the process of re-ordering stocks. So let’s say you have 10 bottles of shampoo at shelf, and you sold 7 and now when you are left with 3 bottles, you re-order 18 bottles to meet the demand for the next week/month. You are replenishing your stock. (10, 3,18 - these no. would be based on your calculation of lead time, demand, order point etc. / Inventory planning).

You have placed an order of 18 bottles and they have arrived at your warehouse. Now the process of restocking the shelf is shelf replenishment.

Dear readers of Thinking Category,

Exciting news 🥳📘🎉I published a book recently - Thinking in Categories. The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

It is availble in India via Kindle. Outside of India, it is available on paperback as well. This book is the only book that you need if you are aiming for Amazon, Flipkart, Walmart, Myntra or category/marketing manager roles in E-commerce.

If you get a chance to read it, I’d be truly grateful for your feedback. And if you find it useful, an honest Amazon review—even a short one—would mean a lot and help the book reach more readers. Thank you ❤️❤️

PLANOGRAM AND MERCHANDISING LAYOUT
The no. of SKU on the shelf would depend upon the space you have. Planogram is a tool that lets you visualize what goes where, and in how much quantity is in your store. It helps you to plan the placement of SKUs in isles, shelf placement, % of sales vis-a-via space allocation of SKUs, brands etc. It helps you to strategically allocate space to different SKUs.

SHARE OF SHELF

WHY SOS is important?

“Jo dikhta hai wo bikta hai” Loosely translated - what visible is gets sold. While shopping in a physical store, the probability that you will pick up product front-facing is higher. That’s why brand battles between each other to have a higher share of shelf. Right place, right shelf position.

FACING
I remember asking my boss about the meaning of facing when I heard this term first time. Facing is the no. of identical SKU facing front on a shelf - facing out towards the customer.

So facing for Brand 1 is 3. and the Share of Shelf for brand 1 is 60%. It doesn’t matter how many SKUs of brand 1 are on the shelf, only front-facing SKUs matter while calculating the share of shelf.

As a brand manager, trade marketing manager, you have to negotiate with your retailers for higher SOS because that will improve the high-off take. Retailers assign the higher no. of facing to brands / products which are high in sales or profit margin.

Share this post with someone interested in trade marketing or offline marketing.

Share

HOW DO YOU ENSURE PLANOGRAM COMPLIANCE OR SHELF ALLOCATION AS AGREED?

In the previous article, we have talked about Beat planning and route planning. Field executive visit retail stores and check if their products are visible in the stores. Self-reporting by retailers or field executives is unreliable. Also, it’s a time taking process.
But thank god, There are tools available in the market in the image recognition space. Field execute takes pictures, Through machine learning, accurate % of SOS can be calculated.
This will help you to identify any planogram execution gap and help you to achieve hot zone placement, on-shelf availability (OSA), and purity.

Today’s world is moving towards everything digital. Tomorrow it might be all online or a hybrid model of Offline-online. In virtual, you have unlimited shelf space. I equate Buy Box in Amazon as Shelf allocation. The items which are behind in the shelf are similar to the products that are on 2nd page, or the 3rd page of marketplace search result. (It’s like 1st first search result = virtual facing). So it is important to monitor your digital shelf as well. In future articles, I will touch upon the digital shelf and digital shelf metrics as well.

Additional points:

1. A large share of the shelf doesn't mean high take-off. If sales are not going up despite high SOS, that means you need to check other sales drivers - consumer preference, product quality, pricing, customer feedback etc.

2. Category managers within the company have to negotiate amongst themselves for shelf allocation. And that could be tricky.

Before I end this post, I have a request for my readers, do share this newsletter with your friends. Will help me to keep motivated and stay on track.

Share Thinking category

So that’s all for today. Goes without saying,

mindshare of consumers >> share of the shelf in the store

Have a great weekend :)

“What is wandering without the summer of prose, winter of leather boots, and the day of stories?”

so here is the story of the beginning of this newsletter. Then if you want to wander some more:

1. https://thinkingcategory.substack.com/p/customer-lifetime-value-80-focus

2. Taking a new consumer category from 0 to 1 - Smart'O Bite

3. How to get the pricing right - pricing strategy for consumer products

Also, this is the 25th post of thinking category. I never knew I would see this day :)

Now, off to the wandering in supermarket and the story of markdown.

This all started when I decided to take a stroll to a local supermarket. The list was ready, the purse has been cleaned, filled up and all glittered. Mask was on and so was my rainy season ballerina. Today is after all the day I will conquer the lanes of discounted items and good night snacks.

I was looking for a few specific kind of chocolate croissants but I found only 1 packet of those. I wanted more obviously. So I asked to the store helper. She couldn’t find one. I saw few boxes lying around. Being noisy and extra motivated, I started checking them out. Found at least 20 packages of croissant, but alas, the tragedy - those were closed to expiry date. I went home with 1 packet of croissant. The end.

Hence, the point of discussion - Markdown.
What is Markdown?

A markdown is the process of reduction in original price of the product to increase sales. If sell-through rate is low, and in case of fast fashion, retailers use Markdown method to get items move faster out of their store.
In case of SKUs with expiry date, Retailers uses FEFO method (First expired, first out) and stock rotation method to reduce waste because of expiry product.

Dear readers of Thinking Category,

Exciting news 🥳📘🎉I published a book recently - Thinking in Categories. The book offers practical frameworks, real-world case studies, and insights into category management, retail, marketplaces, and e-commerce. It’s written for professionals, MBA students, founders, and anyone interested in understanding how modern retail businesses work from the inside.

It is availble in India via Kindle. Outside of India, it is available on paperback as well. This book is the only book that you need if you are aiming for Amazon, Flipkart, Walmart, Myntra or category/marketing manager roles in E-commerce.

Buy on Amazon

If you get a chance to read it, I’d be truly grateful for your feedback. And if you find it useful, an honest Amazon review—even a short one—would mean a lot and help the book reach more readers. Thank you ❤️❤️

How?
Normally, retailers set markdown policies and convey those policies and frequency, timing, discount% to stores so that they can execute the process. As a category planner, I used to take markdown decision every month twice.

Assume the product expiry date is 3 months.

Markdown policy could be - At the end of 1st month - Markdown by 10%. 2nd month by 40%, 3rd month by 75-80%.

Few points to notice-

• Baby product should not go to markdown for obvious reason.

• Sometimes store owners exploit markdown policy for personal benefits. A markdown audit at regular interval will help to keep a tab on this. The frequency and quantity for markdown needs to be monitored to avoid unnecessary markdown processes.

• Different type of product category requires different markdown policies.

• While developing category plan, retailer takes markdown budget into account to do P&L forecast.

• Markdown is a necessary evil, nevertheless. You have to make essentially 3 decisions :

  • Which product to go to markdown

  • Timing of markdown - when

  • And by how much % (reduction in prices)

Do subscribe :

Subscribe now

Method : Markdown optimization

Markdown optimization is finding the optimal timing and markdown % for products at the end of their product lifecycle.

Problem - For a product category, we have price response function from the historical data. (For every time period, we have different price response function, that is demand (d) of the product as a function of price (p). {from historical dataset)

Original stock, and original selling price

Just to see what happens without markdown, lets enter 55 rs., original selling price, into the price optimization model.

We can clearly see that for time period 1 +2, we will sell 130 items. but after that demand function is negative, means we will not be able to sell. ultimately it’s a loss for the retailer.

Let’s see how can we improve our revenue using solver.

Currently, I set 10 rs. as my selling price so I am selling at 80% discount that doesn’t make sense. With 10 rs. price, my revenue is 4000 rs. Let’s try to maximize this.

We will be feeding these constraints into the solver:

• We can't sell more than we have on inventory at the beginning of a particular time period. (Initial stock = total quantity)

• Only price reductions are allowed.

• The price in a period should be either equal to or lower than the price in previous period. (Time period 1 price <= original price, time period 2 <= time period 1 price, …..so on and so forth. )

• The price for end of period should be greater or equal to salvage value.

Let’s run the solver.

Solution:

So if set price at 55,37.5, 25,10 at each interval, we would be able to maximize our profit. Difference in profit from price 10 markdown = 9163-4000 = 5163

So simply by doing this, we have improved our revenue by 5163 Rs.

So now this is your markdown policy after markdown optimization:

I hope you don’t have to markdown but if you do, choose wisely, you must as yoda would say.
Have a great weekend and let me know how do you do in your organization, leave a comment.

Leave a comment

Spread the joy :)

Share

Image source - Google image
Video credit - Friends

@neetubarmecha

At the end of the year 2020, I have started this newsletter - thinking category in an attempt to make ecommerce, FMCG space learning more accessible. One stop portal for all the tools and perspective required in category management.

In 4 months, I have enjoyed the journey of penning down my past experiences in FMCG and connecting to people in this space. Now , I want to learn from you about your experiences / what you wanna learn next. Open to feedback . Ideas. anything that you want to share.

Here is the link .

It takes only 2 minutes but it would be really helpful for me to decide on the next step in this endeavor of mine. And to understand if I am adding any value through this newsletter.

I look forward to hear from you.

Thanks,
Neetu

1. Taking a new consumer category from 0 to 1 - Smart'O Bite

2. How to get the pricing right - pricing strategy for consumer products

3. A Japanese concept to delight your customers

if this catches your fancy, do subscribe:

Subscribe now

In case, you are interested in BEAT plan / route plan template, do write me @LinkedIn. And request to share this with your classmates, colleagues, fellow FMCG / Ecommerce / Category enthusiasts.

Share

Also, hope you had a great safe at home Holi this year.

Off to this week post,

Dear Category Leaders,

By now, I believe that you took your promotions hat out, got your campaigns running, placed the ordered with suppliers and you nailed the pricing. You have demand forecasting numbers on your desk and you put the metrics in the place.

What could go wrong? Unfortunately many things (In my experience of working in FMCG space - You receive most of the products damaged in shipping, Product quality is not what you expected, suppliers delay, marketing ROI not good, yada yada). But one problem at a time.

While buying medicines from a pharmacy, I overheard a conversation between a customers and pharmacy owner. He asked for a specific medicine whose TV commercial he has seen but it was not available with the Pharmacy. He might have looked into at other pharmacies because he seemed perplexed and quizzed the owner “why I am seeing TV commercials while there is no product to buy?”

A justified perplexment.

This led me to today’s post about trade marketing and distribution. As a category manager, you need to ensure the accessibility of your category at distribution channels.

This is the case in a metro city - Residential area. Imagine, distribution scenario in rural India.

Imagine, you have spend so much on campaigns but customers who are engaging with your campaigns not able to buy the product, that’s double whammy. Loss of customer. Waste of marketing spend.

You don’t wanna do that :)

Let’s look at this problem from 4A’s point of view. (This is the framework that I have used for a health project in Rural India)

Trade Marketing is a discipline in business ensuring accessibility of the product at wholesaler, retailer, or distributor level. Trade marketer make sure that products are being displayed in the retailer shop / supermarket.

Consider it the thought of creating a demand for your products across the channel and before it reaches the consumer. (Wikipedia)

Trade-marketing is about managing relationship with your distributors / wholesaler / retail partners.

(Image credit : google image)

Aspects of Trade marketing

• Shelf-positioning, Point of sales material, space planning

• Trade discounting

Goals : Maximize distribution, win total share of store

As a category manager, you need to make sure that your trade-marketing team is aligned with your category goals. they are the contact point for retailer / distributors, hence you need to understand their challenges and how they work. You need to take updates from them about how your category is doing, how much shelf-space is given to your category by trade partners.

Now let’s talk about some of the aspects of trade-marketing. Depending on your business, a category managers might oversee sales territory plan. At least, you should be aware of overall outlook.

1). BEAT PLANNING

How would you make sure that all areas are covered in your distribution territory. Using BEAT plan.

Beat Plan (also referred as ‘Permanent Journey Plan’) is a day level route plan made for field sales/marketing personnel to make visits to a number of stores at a pre-defined frequency. A Beat Plan defines whom to visit, when to visit, based on company’s priorities on stores category/segment. These visits can be made for the purpose of sales order collection, visual merchandising, etc.

• It includes defining territories and ensuring there is no overlap between field executives.

• Beat Plans are planned in advance. This ensure that all stores are getting covered, and there is scheduled visits from the brand.

• These visits are necessary not only to handle order collection but also for visual merchandising and most importantly from competitor analysis perspective. You would know the competitors products being displayed on Shelf and space allocation.

• The goal of optimizing the sales beats is to have the right service levels at each retail outlet. Every outlet should be serviced/visited by the right person on the right day at the right time. 1

2) ROUTE PLANNING

Your territory plan is ready. Now you need to develop an effective route map for your field executive for the entire week. Consider it like a travel itinerary for sales executives.

At what time, day you (A field executive) will go to what store on what day? Location A, location B, Location C.
While creating the route map, you also need to consider unique skills of your field executives (Location familiarity, language skills, previous retail experience - existing relationship developed with retailers / distributors etc.)

3) POS material (BTL activity)

In RB working as a marketing assistant, Point of Sales used to be the frequent purchase for brand managers. These are the advertising material / brochures that gives product / brand information to the shoppers or used as a shelf itself to keep the products within. Generally they are kept at the point of sales. There are different kind of POSM available in the market : Danglers, Display signage, wood stand. You can be as creative as you want with POSM.

(Image credit : google image)

In the world of Digital, there are so many innovative concepts available. Watch this, just one of many.

4) Quantity purchase scheme  (QPS) for the retail customers

Trade Marketing managers / Territory sales Managers needs to devise incentive programs for all the partners in the supply chains.
These trade-schemes or trade incentives can be designed in two ways:

a. Quantity based Schemes : For the specific number of SKUs (X) purchased, you can provide either one item free, or Y% discount to the retail partner.

b. Value Purchase Schemes (VPS) : For the X Rs. of order purchased, you can give Y% discount to your wholesaler / retailer / distributors.

These schemes will help to motivate partners to work with you rather than or despite your competition.

That’s about it. Hope it was informative for you. Do comment. Always love to hear from you.

Leave a comment

If you are looking to read more on what we covered today:

4A’s of marketing : https://relivingmbadays.wordpress.com/2012/11/22/the-four-as/

Ansoff Matrix : https://en.wikipedia.org/wiki/Ansoff_Matrix