Heading the Analytics function at bigbasket.com – India’s largest online food and grocery store, Subramanian M S comes with an experience of over 18+ years in Consulting and Analytics. Prior to bigbasket.com, he has served as a Director in the Analytics organization at Dell, co-led the Supply Chain Centre of Competence (CoC) at McKinsey, has been Principal Consultant at Infosys Technologies and Senior Consultant at Ernst & Young, PricewaterhouseCoopers and more.
Having been thoroughly involved in leading the supply chain transformation and technology engagements for clients across the US, Europe and the Middle East, he has been instrumental in advising clients across various industries with key focus on industrial manufacturing and hi-tech industries.
A Computer Science graduate from University of Madras, he is both MBA from IIM Ahmedabad and has a Master of Engineering degree from MIT.
AIM interacted with Subramanian M S, fondly called Mani, who is actively involved in promoting leadership & communication through the Toastmasters movement and loves quizzing. Here is the complete interaction where he spoke about analytics and other technologies in the space.
AIMAnalytics India Magazine- What are the factors that spurred the conception of bigbasket? How has the journey been so far in the analytics-driven online groceries space?
SMSSubramanian M S- Grocery buying is largely a habit-driven phenomenon. Customers tend to prefer buying certain products from a local shop or a particular brand. At bigbasket, right from when it started with only a thousand customers, we realized the importance of such customer preferences and the need for customizing offers based on these. And that is how bigbasket grew year on year.
The analytics team at bigbasket was set up in 2013 with the aim of increasing the customer base through the insights gathered. The idea behind customer analytics is customer retention and tracking the frequency and value of orders. The analytics team helps in identifying marketing channels and the buying patterns of customers. bigbasket has managed to increase its customer base from 0 to 4 million in just over 5 years. Our systematic expansion strategy coupled with analytics-based decision-making and constant innovation with a focus on the growing customer needs, customer retention through improved product quality, and customization based on their buying pattern is what has helped us along the way.
The Analytics team is focused on enhancing customer experience by delivering a variety of solutions – a) understanding our customers and enabling targeted customer engagement (offers, communications to/with customers) b) analyzing their buying behavior and designing their smartbaskets using products they need immediately and products they usually buy; this reduces the time-to-buy for customers and allows them to discover products that they may need or not usually buy. These are some examples where analytics is enhancing customer experience.
AIM-What are the current technological trends and challenges in the analytics sector in the country? How does bigbasket leverage those trends or tackle the impediment?
SMS- The analytics sector has seen many changes over the years. The primary challenge for any e-commerce business is to keep a healthy pace and embrace the evolving trends to be successful in the industry. Big data analysis and customer segmentation remain prominent issues.
To address these, the dedicated analytics team at bigbasket has developed a set of tools and machine learning models that analyse and break down the unstructured big data into understandable analytics which aid the company in making informed business decisions. This big data is generated through a series of customer buying pattern, preferences, their online behaviour, and transaction history.
The analytics team helps in capturing real time data which was unthinkable and not possible 10 years ago. It also helps in customizing the needs of customers at a large scale. Often, customers get a lot of messages from different companies. However, to stand out, it is important to send only relevant and customised messages to the customer based on their buying pattern and preference and our analytics team helps us do just that.
AIM- bigbasket is striving to personalize the customer experience with their small but agile and sharp analytics team. How does the firm make use of big data and analytics practices to ensure customer satisfaction?
SMS- We deal with big data, which is generated using the online behaviour of the customer and by studying their transaction records. The Analytics team is focused on enhancing customer experience by delivering a variety of solutions. Customer analytics contributes to larger sales and helps in understanding the correlation between the delivery matrix and customer loyalty. Analytics is all about customer retention. It not only enables us to better understand why the customer is happy or unhappy about but also helps us manage and address complaints. Today’s customer expects more than just offers and options on their 24×7 online shopping platform. A great shopping experience comes with faster processing, faster response time, lightening paced deliveries and a round the clock grievance redressal system.
We also analyse customer feedback which is unstructured data to understand the sentiment expressed by the customer and the areas on which they are providing feedback. Our big data environment is helping us analyse structured and unstructured data to draw key insights and drive improvements in our customer experience. We also use these findings as a Launchpad for new products.
AIM- bigbasket is leveraging analytics, big data, and machine learning in the online groceries segment. Can you describe how are these technologies integrated into bigbasket’s product ecosystem?
SMS- bigbasket has added features and tools to its website and mobile application that enable them in improving the overall shopping experience of the customers. Features like “Smartbasket” help the customers with a list of frequently bought products thus enabling quicker shopping. Also, the mobile app customises and personalises the deals and discounts by means of high-end trends visualisation and analysis. The inbuilt analytics tools embedded in the website and mobile app help in tracking the customer preferences and buying pattern enabling the team to improve and constantly update the e-commerce business to suit customer preferences.
Big data platforms and machine learning algorithms are helping analyse large volumes of data to devise point as well as scale solutions. Point solutions (smartbasket, communication, offers, etc.) help in personalising customer experience on the product while scale solutions help in business planning that enhances overall customer experience (capacity, availability, etc.).
AIM- bigbasket customises its offerings to meet the demands of varying customer preferences. How does the firm exactly customise its analytics platform?
SMS- The key to stay ahead in this market is continuous innovation. And one of the methods the company has identified as key to achieving this goal is analytics. “Smart-basket” has not only helped BigBasket formulate a method for predictive analysis of consumer needs and behaviour but has also reduced the time-to-order to below five minutes.
It has enabled us to know our customers better. Each customer is unique and Smart basket knows that. Smart Basket builds a customer profile based on the information from the customer’s previous searches and purchases. This allows us to get a step ahead of the customer, giving them what they like, and what they are looking for. The company’s aim is to populate a customer’s basket based on their most recent and frequent purchases, alert them if they have not added something they usually buy in case they have forgotten it, and inform them if an item they normally buy is about to run out. Analytics helps us to predict what the customer’s next order is. Other key focus areas in the days ahead include the fruits and vegetables supply chain and organic and private labels.
Buying grocery tends to be a monthly chore and is nowhere close to how people experience shopping for fashion. People often send their maids or drivers to pick up their grocery. In fact, customers remember the layout of a physical retail store as it helps them to pick up what they need very quickly. Some customers even leave their shopping bags at the local store and have the shopkeeper deliver it to their homes. So we have an online list that captures what the shopper buys and we call it a smart basket.
The smart basket categorizes what a customer buys frequently and throws those items to the customer again the moment they start shopping. So a customer can quickly add those products to their list. This is one of the reasons why customers stick to one particular e-commerce portal for shopping as it can be cumbersome to buy a regular list of goods from different sites.
AIM- What is the roadmap that lies ahead for Big Basket in the analytics-driven online groceries space? Any word of advice for the upcoming startups in the same space?
SMS- Our analytics team is a robust one that focuses on the overall growth of bigbasket as a company by integrating the research and findings of each and every department and team. The road ahead for the team is focused on developing solutions to drive better customer experience, improved customer engagement and enhanced customer delight. To achieve these goals the team will leverage existing and planned new data sources to improve customer experience and better understand customer behaviour.
A word of advice for a new business would be to maintain extreme caution when it comes to business expansions and new product launch. Data is very important and using data in the right way can help achieve success. Every new venture in this segment must attempt to capture as much data as possible right from the start. They must concentrate on having systems in place from the beginning to process data in the most accurate and useful ways as possible. Another very important factor in the groceries and fresh produce segment is streamlining last mile delivery operation. This is where most new comers lack perfection and hence fail to succeed as a business. Also new businesses need to optimise workplace delivery.