EP #4: Personalization-led Growth Strategies from BigBasket

EP #4: Personalization-led Growth Strategies from BigBasket

About this Podcast

As part of our #PersonalizationThursdays series where we discuss the impact of personalization and predictive recommendations across key industries, we interacted with Anand Bhaskaran, Head of Digital Marketing at India’s online supermarket: BigBasket! Anand highlights the following:

  • Key user engagement and retention challenges specific to the online grocery space
  • The similarities and differences in online shopping across Tier 1 and Tier 2 cities
  • Addressing these challenges with data-driven personalization
  • The use of AI to provide contextual product recommendations at BigBasket
  • The value of integrating conventional marketing, tech solutions, and human experimentation to drive future scale in the online grocery industry

Tune in to learn how digital grocery platforms can uplift conversions and marketing ROI using a well-conceived personalization strategy!

Episode Transcripts


Pradyut Hande (Host): Hi guys, welcome to another episode of The Martechno Beat. A specially curated podcast series powered by Network Smartech and as I mentioned previously also the next few episodes strongly revolve around the central theme of The Power of personalization. The power of how businesses are leveraging data-driven strategies to drive user engagement at scale and today I have The pleasure of having Mr. Anand Bhaskaran, the head of digital marketing at Big basket. Join me for a candid and insightful conversation. Welcome, Anand.

Anand: Hi Pradyut! Pleasure to be here.

Pradyut: So Anand Has been the head of digital ad big basket for almost 4 years has been at the company for almost five years and before that, he was in the finance industry for almost five years. So a solid track record that he brings to the table and I’m sure we’re going to have a very insightful chat. I’m your host Pradyut Hande once again and without further Ado. Let me just, you know kickstart this podcast. This is this episode revolves around you know, how a brand like big basket is leveraging the power of personalization to solve key user data engagement and retention challenges across the board, and just for context the big basket happens to be India’s largest online supermarket and you know with the digital Grocery Market slated to grow to almost 10.5 billion dollars by 2023. It’s a massive opportunity for new players and existing players to tighten their belts. So my first question to you Anand is this, “could you walk us through the Genesis and the growth story of big basket so far!”

Anand: Sure! So Big Basket was started around 8 years back, by a group of entrepreneurs who had been in the grocery space in the late 90s and early 2000. So a group of five entrepreneurs kind of realized that somewhere around 2011 and 2012 there was a market opportunity for online groceries in India.

Pradyut: Right!

Anand: We started small, in fact, some differences between groceries and any other horizontal e-commerce company. One is you are dealing with perishables.

Pradyut: Correct!

Anand: You are dealing with fruits and vegetables, you are dealing with meat and that requires a very robust supply chain.

Pradyut: Correct!

Anand: So that is one point of difference. The second point of difference is that unlike most other common scenarios in grocery, customers are ordering maybe eight to ten items at a time. Right! and if a customer orders ten items. It’s necessary to be able to deliver all of those 10 items, you know, to the customer.

Pradyut: Correct!

Anand: Because for example the customer orders 10 items and they are not able to deliver one or two items, then the customer has to step out, right!

Pradyut: Correct!

Anand: Which isn’t the purpose of shopping online. So not only do you have to deliver perishable items like you need to have a supply chain for that but also you will need to have the ability to deliver all the items that the customer has ordered.

Pradyut: Right!

Anand: So, the first you know, three or four years but then tuning and time tuning the supply chain and delivering models, we were initially for the first three or four years we were just present in three cities.

Pradyut: Ok!

Anand: And once we had perfected the delivery model and the supply chain, then you know we were able to roll out operations very quickly to 25 cities which is our source of operations today. So briefly in the history of Big Basket, one of the interesting things that happened over the last two years is that having built a backbone, you know the supply chain backbone. Over the last two or three years, we were able to offer different services based on the same backbone.

Pradyut: Ok!

Anand: So for instance, we have something called “ BB Daily”, which is a morning fresh delivery model.

Pradyut: Right!

Anand: So the idea is you can place an order by 9 PM on a day and you get delivered by 6:30 or 7 AM the next day. In that some of the things we built for that are new and some of the things backs on the grocery backbone that we built.

Pradyut: Right!

Anand: Likewise we have something called the “BB Instant”. 

Pradyut:  Okay!

Anand: Which is an unmanned kiosk. So for example, you could have any and this is quite popular in the Western countries and also in countries like Japan. Where you have kiosks in apartments as well as you know in your office. So we have that machine called BB Instant.

Pradyut: Okay!

Anand: In fact, we also have a B to B business. So all of these are right on the expertise and the backbone that we’ve built in the last few years.

Pradyut: Right! No, that sounds fascinating and it also goes to show how this entire space is, you know, a life for Innovation for someone who can get their supply chain backbone, right? There are minor Pockets that you can get into that cater to niche audiences.

Anand: Yeah!

Pradyut: Alright! That makes sense. Now. I mentioned this at the beginning of the episode. I just want to pick your thoughts on you know, how you see this industry shaping up. Where do you see this going over the next five years in terms of competition in terms of rising demand? What are your thoughts on that?

Anand: Sure, so with respect to online groceries, one of the key things to note is that globally, it’s one of the least penetrated categories.

Pradyut: Right!

Anand: Right, online. So I mean, in fact, two things which in combination makes for, you know a very interesting observation. One is that the market is very big. 

Pradyut: Correct!

Anand: Particularly, in most markets, groceries is the single biggest retail market right now. Number two, it also tends to be the least penetrated category online. So that means out of a hundred people who buy any, everyone buys groceries, you know, very few people buy groceries online. And that’s the thing you know globally itself except for a few countries like South Korea, for instance, has slightly higher penetration than India or US  But in general, it is that online grocery has low penetration. And that has to do with maybe a couple of times. One is, not a trust is required to buy food and perishables and the barrier for that is quite high, number one. Number two, I also think there is a habit element to this, people tend to buy groceries and so they continue to do it for a long time.

Pradyut: Also it’s a touch and feels sensitive sort of category where consumers would, you know rather you know, feel it physically before making a purchase with certain items.

Anand: Exactly, I think that’s again what you are saying is true for rather perishable categories.

Pradyut: Right, perishables.

Anand: For example, if you are buying detergent then I might touch and feel. So for perishables, touch and feel are still important for lots of people. So, because of these two reasons, it’s traditional and it takes time to penetrate online. That’s number one. And India is also no different in that regard. If you look at the number of people who transact online in India For Physical goods that are on you know, 10 to 20 million in a month. That’s my best estimate. Let us look at the number of people who can transact for groceries online. I would imagine that number is, you know, so 2 million or 3 million and not more than that.

Pradyut: All right!

Anand: Right, so it’s so to that extent, you know, even in India, its career is going fast, but still a long way to go, number one. Number two, in terms of competition as a result right. And so there are two ways to look at it. Like one is to look at the competitors within the online space and within the online space you have, you know horizontal players are there and now and you have companies like grofers and there are a bunch of them in online space but really here, I mean given the penetration is low, we can take the opportunity really in growing the market itself nor will get more people to adopt online grocery and especially given that there is marketing leader. We can’t efficiently think of the key things in marketing. How do we grow the market? So that’s all we’re thinking of in the competitive landscape and a couple of Interesting observations here. Right! One is the observation that we have BB Daily, for instance, right? I mean it’s been going really fast over the last year or so and we think that most people and a lot of people tend to buy milk delivered.

Pradyut: Right!

Anand: Right, they don’t go out to buy milk. And it seems like the resistance to find milk plus something else online is probably lesser. So that’s the one way I think to expand the market. Besides that, I think a lot of it has to do with how you increase the level of trust, the brand. I mean, for example, Flipkart. This is quite good in e-commerce when presenting cash on delivery, hassle-free transactions, etc.. Part of it is figuring out what we do to enhance trust in our rank. So for example, I mean I’ll give you a couple of examples.

Pradyut: Sure.

Anand: So how we have done this, from the beginning we have the no questions asked return policy, if you are a customer and you ordered something and I mean you don’t like it or you don’t want it or ordered by mistake, you can return it and we don’t ask any questions, number one.

Pradyut: Right.

Anand: Number two is even more interesting, is that if we are late. So one of the important things about groceries is that you want things on time.

Pradyut: Correct, on-time delivery is paramount.

Anand: Exactly,  so if you are ordering today you want to know that it’s coming in a particular time slot. So one of the things we do from the beginning is we weren’t able to deliver by the promised time. We would pay a certain amount and today, we pay 5% above the occurred value, which means that we also have skin in the game, right? I mean, so we make a mistake we pay for it and a lot of those things and when customers observe that happening and he makes our promises like ten customers to realize that we are in it for the long run and that you know, we mean it when we say that we want to earn the customer’s trust because those are the things that we have done, but if you tends to continue up the journey, I mean what tends to happen in any Market is initially, there are people who really need the service. And when you try to penetrate the mass market, you need to figure out what the concerns of mass markets are and try to evolve your offerings and promises to a larger segment of composition.

Pradyut: True, I think there is the convergence of promise and solving pinpoints and yeah, that’s I think something that has stood you guys in good stead and will continue to in the future as well. Now you mentioned how critical it is to win the trust and loyalty of your users and second this backdrop. I wanted to ask you what would be some of the keys, you know, user data or user engagement or attention-based challenges that big basket faces, especially with regards to its web and app platform. 


Anand: Yeah, so I think the central challenge that we’ve been trying to solve for the last two years, I mean I’ll just give you a sense of the customer behavior. So then we started this with this one.

Pradyut: Sure.

Anand: So what you observe is that customers who shop four or five times, tend to be very sticky. And then you can predict how often they shop, how their basket value grows etc. So the second challenge has always been on how you get customers to the products.

Pradyut: So, that becomes your Tipping Point.

Anand:  Exactly. Yeah. I think we’ve done a bunch of things towards that, I  mean, I think I can list down a few experiments to show the initiative that we tried like we introduce the idea of a hundred percent cash back for a few orders. The essence is that you first place an order for thousand rupees and when you do that, you get cashback which you can spend over, you know, in the next two orders.

Pradyut: Right.

Anand: With the idea that increases the proficiency of the customer to shop rationally two or three times, so I think we have been trying that. The other thing we have been trying is a loyalty program called BB Stars. One of the key points of BB Stars is that it reduces the order value threshold for customers.

Pradyut: Okay

Anand: So to be specific for regular customers if they saw for thousand two hundred rupees and above the delivery is free, for BB star value is 600 rupees. Okay, and therefore I mean clearly reduce the Barrier to transact because you know many customers may not be able to build a basket for 1200 rupees. So one of the things we’ve done therefore is to try, you know giving BB Stars free to new customers. I mean, I think it is a good initiative but I think it’s an ongoing challenge but the customer behavior also keeps changing. 

Pradyut: Yeah, that’s true.

Anand: Yeah, and we constantly try to experiment with ways to improve the proficiency of customers to place two or five orders.

Pradyut: Correct, and also you may have witnessed and changed or modified behavior when it comes to urban and semi-urban areas, you know, Tier 1 tier 2 cities our customers react differently every time they are the items that they add to cart would be dissimilar. I mean, that’s something that you guys I’m sure are tracking at the back end.

Anand: Well, what if things with respect to customer behavior are two things one is you know, I like you say that the customer behavior in terms of the market composition for Tier 1 and Tier 2 cities right there to be fairly similar it’s more similar than different. All right, that’s part one but two. However, in Tier 2 cities, one thing to notice is our value proposition and what is the value proposition of an online grocery delivery platform? A lot of it is really convenient, right!

Pradyut: Correct!

Anand: Convenience and it has also arranged good prices at the top but a lot of it is convenience direct and convenience and in the value proposition of convenience becomes higher if you have lesser time on your hands. What you observed is in tier 2 cities and they’re both people have a little bit more time than in Tier 1 cities and thus the convenience proposition alone is not sufficient. So as a result, you know we have to be more competitive on price.  We have to Showcase reels that they can get otherwise. So I think that’s really the main difference that we see. The market composition is quite similar,  but the convenience population is probably a little more valuable than the human species and that will have to play on other parameters like rains and price for it.

Pradyut: Because at the end of the day the average Indian consumer, especially in Tier 2 City would be far more price-conscious.

Anand: I mean, everyone says that.

Pradyut: I mean in comparison to perhaps Tier 1 city dwellers.

Anand: I will put it slightly differently. If I am slapped in a city like Bangalore or Mumbai, then that’s the most important thing for me. Like anything that helps me save time and get right what I want and that’s what is important for me and I might not be particularly sensitive to price but if I am in a Tier 2 city there may be the options that would be slightly convenient.

Pradyut: Right! And that actually brings me to my next question, now we’ve seen how critical it is for a brand like the Big Basket to get your users to that Tipping Point of four to five repeat usage. Now, in a situation like that, how does big basket Leverage The Power of personalization or a recommendation engine to increase the average order value or cross-sell or upsell better? How do you guys actually look at, you know, leveraging recommendation engines? 

Anand: Yeah, I zoom out a little bit and I’ll talk about how we think about personalization in a broader sense. We have around 30 to 40,000 items in a store. Right. So it’s effectively a very large shelf space, effectively infinite. But let’s have a very large shelf space. Number one. The problem is the customer whenever visits, she’s banging pretty one too few minutes and it is no mere that a customer can see all of the 40,000 products on the app in that time. And therefore it’s very important for us to be like what we are trying to do is that in those 2 or 3 minutes of the customer staying in our app is how do we show the movies available the recommendations here.

Pradyut: Yeah, correct!

Anand: Exactly, right. So that’s the problem that we are trying to solve. All right.

Pradyut: Actually capitalize on those micro-movements that the user ends up spending on the website or app. 

Anand: I would put it a bit differently. If you have a chance to show something different to the customer right and that the universal set is 40,000 products. Right. How do you feel when your 30 products comrade the 40,000 that’s most relevant to a particular customer. So that’s the way we think and likewise, if you extrapolate this to other can. For example push notifications learning. So let’s say if I take push notifications, we send out 30 push notifications to offer to our customers in a month, right that’s like saying that I have 30 opportunities to show about a different kind of group. For each customer the most relevant set of the record. Right. So that’s what we’re really trying to solve. Okay. Now one of the interesting things to look at now is what are the international trends in this. I think I should look at Taobao, which is sort of like Alibaba. 

Pradyut: Right!

Anand: So it will look at a hundred product page views that they have and more than 60 of them come from recommendations. Right. Recommendations are supposed to search. So that’s the first data point, the second data point is to look at a company like Stitch Fix. Stitch Fix is an American company that recommends outfits to the customer. So customers can state their preferences in terms of clothing and style etc. and Stitch Fix 

Pradyut: Creates those recommendations based on those ratings.

Anand: Right, exactly, and Stitch Fix is 100% based on those recommendations that customers browse or search. There is no process of experimentation. So I mean there are multiple other data points that you could add to it. What is there is the way and the Holy Grail really is if a customer comes in right and you know, she doesn’t have to really search but instead the list of 20 or 30 items that are most relevant to him or her is the holy grail. In Fact, I have been reading something on there are these discount retailers so one of the things apparently they have maybe a thousand to two thousand. And apparently, one of the benefits of having I mean obviously to usually think about law enforcement has a negative, but apparently at some point customers don’t want that level of choice and variety of options to them.

Pradyut: Make sense, correct.

Anand: So from all those perspectives we can really think about it and we can use those three minutes of that customer standing with us in the most optimal way possible.

Pradyut: And also the fact that you know, your recommendations don’t just end on the website or the app. It’s something that needs to deliver across the entire customer journey across different touchpoints, you mentioned push notifications and I’m sure you optimize other channels as well for you know, this particular sort of Outreach whether it’s email, whether it’s SMS, it’s about finding what The Sweet Spot is for individual users. 

Anand: Exactly, and here I would add a couple of things as personal decent is way beyond just mentioning the customer’s name. It must be way beyond one saying, ‘Hi Pradyut, we have an offer for you. 

Pradyut: I think that you know that phase of personalization has gone by now. That’s Passe.

Anand: Exactly. I think the value is like if I say, ‘Hey Pradyut, you seem to like healthy foods and here is a healthy food option for you and here is a card or payment offer that you use regularly, and if they are ready to pay something like that, then that’s useful.

Pradyut: No, that’s true, because you contextualize at a very individual level. It’s not like a scattergun approach.

Anand: Exactly, and I think in order to do personalization. Well, I mean, there’s a big human element that comes in here. I think you have to be really thoughtful. It starts with category understanding and product understanding. So if you are a marketer for example in groceries, you will have to know the category really well, so I have to know that there’s something called there’s a trend to healthy food right? Tell the friend, know the category in and out, you have to know people the payment offers, etc. Understanding the category is one part of this. The second part of it is, I mean, I think involved in putting together a template for a personalized conversation there. Because ultimately what you want is to relate customers would think that wow. I mean, we need to give thought to the communications as supposed to you know as her name is not related to some irrelevant thing. It’s one of value to them. We don’t want customers to think that way.

Pradyut: Correct.

Anand: That’s one thing that has a lot of art in putting together the templates Etc. So I think those two parts are applicable parts of personalization.

Pradyut: That’s true, When I think a lot of people sort of losing track of the fact that personalization isn’t just about the application of machine learning or artificial intelligence. That component of human capital that you mentioned is also critical and they go hand in hand.

Anand: Yeah, I mean Nothing will be loved. So sometimes I think it’s either the end of last year or two at the beginning of this year. Like Spotify sent out emails with you all.

Pradyut: Oh yeah! The year and wrap up.

Anand: Exactly, and it’s so beautiful, you know, it had my favorite artists, it had songs that I listened to most and they have wrapped it up in such an artistic way. Doing a few pieces of personalization like that is something that we need to consider.

Pradyut: Correct, that actually makes so much sense. So this brings me to my final question. You know, I just want to pick your brains on what you think is the future of mobile or digital marketing, the increasing dependence or application of artificial intelligence and machine learning. Where do you see the future of marketing head?

Anand:  Well, I think about marketing Venn diagrams composed of three parts. Okay, right one part is core marketing, Second for this technology,  Third part is analytics, correct, if you are in a company that has a website or app, right? It’s very important to know these three parts. So for instance marketing one has to know how consumers behave, what are the drivers of customer behavior, the basics of marketing, right. So I think that’s the marketing aspect, the second part, especially with For instance how API works now. And what is the kind of Integrations, what is possible, and what is not?

Pradyut: And what makes strategic sense for you at specific evolutionary curves of your growth story.

Anand: Exactly. Exactly. Yeah and here in technology one doesn’t have to know all the details but it’s important what is possible. I think lots of that can be picked up but today either podcast or reading online. So I think that’s the technology part and I’ll give you a simple example. If you are running a digital marketing team, it is very important to know how things work, how data gets collected, and how things are done. In the case of push notifications, how the data you collected all exactly application has been accepted by the time that select right here in the case of two certifications. How do you capture the data? And what are the implications involved with your internal database?. Understanding those basics really helps. So that’s the technology part. The third part of analytics. One thing is to work with data and I think with marketing especially as it’s often very hard, I find to figure out what is causation. When you run a campaign will really cause a positive impact on developing skills. So what has caused what I think really helps is having a skeptical mindset that will be optimistic, but then you really don’t want to be fooled by anyone. I think that skepticism is very good to have and apart from that the fluency with numbers will do, how do you know the different impact of something and how do you appropriately react to things which means it is very easy to overreact or under-react to things but how do you have an optimal level of responsiveness. I think that’s analytics as I would look at it. Depending on one’s background, one comes from a traditional marketing background, he might be good at marketing but need to learn technology and analytics.

Pradyut: Yes! You have to realize where your strength lies and where your weakness lies and align accordingly.

Anand: Exactly, it’s always good to play to your strengths but it’s also necessary to plug out your weaknesses hindering you, so it’s good to have decent skills.

Pradyut: I think you have hit the nail and I think the way you explained it with the Venn diagram makes a lot of sense. 

Anand: Yeah.

Pradyut: And that actually brings me to the end of this particular episode. Some wonderful insights were shared and some very interesting observations were made because it’s always great to hear straight from the horse’s mouth and while we are in the tech space. As you know, you can conjecture, speculate, read and learn, but it’s always important to hear from industry practitioners who are working at The Cutting Edge of marketing and technology. So, thank you so much for joining us on and it’s been an absolute pleasure to host you. 

Anand: Thank you, had a great day too. Thank you.

Pradyut: Thank you. Thank you so much.

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