Online grocers are going all out to attract modern shoppers, and supporting them in this endeavor are new-age technologies. Bigbasket is using artificial intelligence, machine learning, and also analytics to streamline strategies and improve customer experience.
Online grocery looks like an easy business of delivering vegetables and fruit, toiletries and staples to homes, saving clients the inconvenience of going to a grocery store. It is complex to get a client what he requires in time – and that is the reason every e-tailer has developed the own business model and then competing with its competitors.
Founded in 2011, Bigbasket promises to be India’s largest online food and grocery store. It’s more than 18,000 products and over a thousand brads in its catalog.
Softbank-Backed Grofers and Alibaba-backed BigBasket are the top online grocers. Walmart-Owned Flipkart and e-commerce giants Amazon are in the game, while food shipping and delivery unicorn Swiggy along with all-rounder Dunzo have more grocery to their repertoire. There is space for a small player as Satvacart that’s been serving a pocket of customers in a 5 km radius of the warehouse of its in Gurugram for 5 years. The majority of its orders are vegetables and fruit, which it buys generally from close by Delhi’s main mandi. Its USP is shipping and delivery in an hour.
Satvacart CEO and founder Rahul Hari attract inspiration offered by FreshDirect, an online grocer founded in Manhattan, 1999, which concentrated on households in a 10-mile radius. Around the same time, California based WebVan scaled across several cities with about $800 million coming from investors and an IPO. Though it went bankrupt in 2001, whereas FreshDirect is New York’s top online grocer today, in front of formidable rivals like Walmart.
“We are an e-commerce business where data is the new oil. We leverage data-driven capabilities from a number of resources. Data is collected from transactions, customer inclination, going shopping behavior, etc to create an algorithm (statistical algorithm, Deep Learning algorithm, and machine learning algorithm). These algorithms are used for various use cases,” states Subramanian M S, Head of Analytics, Bigbasket.
“We initially faced the task to control huge volumes of orders, while making sure delivery on-time. Probably the most daunting problems to conquer included traffic, manually understanding a typical route for not wasting time and using one vehicle for several deliveries, and collecting details for reverse pickup and handling it with our on-road vehicles,” Subramanian says.
Bigbasket banked on analytics and machine learning to claim on-time delivery (99% or more). The procedure for delivery now begins with identifying the typical routes and vehicle status for packing numerous sets of orders.
For a product on-time delivery, the team gets insights from different data sources like best-route, live traffic situations, the number of vehicles needed on which route, etc. With informative data channels, the ML-driven model analyzes real-time situations and customer demands. The unit helps in managing multi-level monitors of pickers, reverse pickers, regular deliveries, etc.
“We are using Redshift as our data warehousing platform and data lake, that hosts all data that gets executed on a selection of systems (transaction systems, app, and web). There are a number of sources like consumer feedback, purchasing patterns, etc to nourish ML-driven algorithms, ” Subramanian mentioned.
Analyzing consumer buying patterns has helped the business to improve the shopping experience.
Bigbasket has gone a step forward and created still another smart offering recognized as “Smart Basket.” It’s an innovative model based on machine learning and analytics systems which will help customers to avoid wasting time and explore other items, while it produces a list of products most likely to be bought on the foundation of artificial intelligence.
The systems analyses previous purchases, going shopping behavior and understands the repetition of products in subcategories and grocery. The algorithm then has helped in curating a listing of products that the buyer is expected to contribute to the shopping cart.
Smart basket has helped a huge amount of customers having an intelligent method of shopping for their items while ensuring they do not miss some daily use products.
“Besides smart basket, we’ve created a recommendation engine made using a bunch of analytical streams like FP development, affinity analysis development, and then collaborative filtering. The recommendation algorithm helps consumers to check out new items at the same time as keeping their personal preferences while offering suggestions,” avers Subramanian.
Bigbasket has additionally invested in IoT backed applications to ensure freshness and quality of things like fruits, other grocery items, and vegetables.
While supplying fresh goods from the warehouse to the customer’s doorsteps, many problems, such as segregating hot and frozen objects and managing the proper temperature need to be conquered.
“We have applied IoT to recognize the state of product packaging whether it’s frozen, hot or cold. Post presentation into the container, we’ve to keep the proper temperature for a number of different items, ” Subramanian states.
The IoT applications analyze the data about the product to be loaded in the correct container and balance different conditions for every person item throughout the voyage – from the factory to final delivery point.
“While ensuring the source of fresh items, there are bottlenecks such as coping with the availability of items, maintaining an eye on company revenue, and ensuring reduced wastage. We’ve implemented machine learning to hit a balance between all these points. The process manages the availability of perishable items vis-a-vis total clients in a community at a time,” Subramanian describes.
Artificial intelligence is still another technology that Bigbasket has adopted.
“We have invested in artificial intelligence and deep learning to think of smart kiosks recognized as BB instant. These smart machines connected up through an app. 1000 machines across 500 locations are been installed. The numbers are anticipated to boost to 3000 machines in the near future, ” Subramanian states.
A buyer comes to the machine and places the order through the app. The buyer walks away with things from the cart instantly. Billing is automated through a cash-less domain, where the buyer receives the expenses on the app with a number of payment options.
The artificial intelligence system has an in-built recognition system, which recognizes the purchased items with image scanning concepts with innovative deep learning solutions. The program analyses and drops the correct product to the consumer and maintains the cycle of billing and availability.
On the future plans, “In 2020, Subramanian says, we intend to invest in more AI-driven and IoT innovations in the company.”
The business is leveraging data science in creating a route optimization model. Bigbasket has more than 3000+ delivery bikes and vans. On a certain day, around 5000 routes are covered across different cities in India with the moment spent on-road being approximately 18 hours.
These examples Rahul Hari has learned from. “I figured that rapid growth in this business can lead to failure. One first needs a practical business model in the same domain,” he says.
He watched better-funded contemporaries as Peppertap and LocalBanya succeed and collapse, while Satvacart stayed seated in Gurugram with angel funding. He hopes to flip cash positive and increase next year. But increasing a sequence A round to scale bigger will not be easy, with numerous big fishes in the pool.
One of them is Grofers, created in Gurugram only a year before Satvacart. It’s raised more than $500 million.
Venture capital loves a fast scaling, asset-light, marketplace model, connecting buyers and suppliers without holding inventory. Which was the promise of Grofers in 2015 when Tiger Global and SoftBank sponsored $165 million into it. Though it changed tack to an inventory led model, reading through the tea leaves on time.
At this point, it has a twin strategy. One is developing private labels for staples and home products that could be lower-cost solutions to FMCG brands. Bigbasket and amazon also do that.
The next strategy is to cut back on vegetables and fruits and restrict their range of items to those that are even more in need, making it much easier to handle delivery and inventory.
“Grofers is focused on the requirements of mass-market India, reducing costs through a blend of private label products, a cost-efficient supply chain and restricting our assortment,” affirms Abheek Anand, principal at Sequoia Capital India, the first institutional investor in the startup.