leverages artificial intelligence to determine emotional and functional
motivators that affect consumer buying behaviour.
Leveraging artificial intelligence to analyse petabytes of data can unveil
profound insights on consumer tastes and preferences – a strategy which has
played a crucial role in Flipkart’s growth since 2007. Flipkart leverages these
insights to improve its online shopping experience, including which product it
offers as well as where it should focus its innovation efforts.
With AI For India, Flipkart is applying artificial intelligence more aggressively to
build upon service differentiation, better user experience and to automate
back-end processes.
Flipkart’s AI For India hopes to harness artificial intelligence to resolve
bottlenecks that are unique to the e-retail ecosystem in India and to improve
customer experience.
The firm has created at least 40 insights for each of its over 100 million
customers — from GPS locations and page scrolls, to preferred brands and
frequency of purchase — which has helped to dissect customers’ buying
psyche, boost brand awareness and detect fraudulent buyers.
Flipkart has set up the software giant’s artificial intelligence and machine
learning-based solutions to optimise merchandising and order placement,
particularly during large-scale events. Cracking merchandising means
achieving an optimum display of products on the website or app and thus
increasing sales.
Flipkart’s AI project known as ‘Mira’ aims to deliver an offline experience to
online shopping. The project appears to be a response to Flipkart’s reported
10-11% return rate.
This goal of Mira is to scale the in-store experience of having a sales
associate – but via artificial intelligence and through digital channels
“Say, someone comes to Flipkart searching for an air-conditioner. Because
of Project Mira, Flipkart now asks buyers about what kind of AC they want,
the tonnage, room size, brand, and such. It is a beginning in helping
customers find the exact product they need in online settings that aren’t
exactly easy to navigate
This helps the company in understanding consumer behavior, purchasing
power and socio-economic background of an individual depending on the
smartphone they use.
Flipkart has been adopting artificial intelligence for executing a series of tasks
ranging from extracting insights through the behavior of customers and their
reviews, averting transaction fraud, consumer support, logistics, and
warehousing, estimating product popularity, image speech, and text
processing, intent modeling, conversational search, discovery, forecasting,
pricing, address understanding, and contriving separate items or private labels
through AI and ML models.
1. Address issue - the team of Flipkart data scientists arrived at a robust
working address classification system that would vastly improve the
efficiency of last-mile delivery in logistics besides detecting and
eliminating address-based fraudulent practices. The solution correctly
classifies and identifies Indian address , also resolve inconsistencies,
with a 98% accuracy rate.
2. Data Science team of Flipkart plays a crucial part, for mapping query to
the Store.
it uses three ways:
a. Classic Statistical Approach
b. Supervisory Approach
c. Supervisory++ Approach
a. Classic Statistical Approach - In Layman’s language, Classic
Statistical Approach to Store Mapping helps is searching the
baseline system for the products that the users clicked on.
The aggregation on the ‘Click’ data provides a ‘Confidence Measure’
showing the number of times that users clicked on a product for the
same search query and hence the store mapping happens almost
instantly.
b. Supervisory Approach – Here they use leaf level store identification.
For example if someone searches for a thing then, the results would
contain results from multiple categories related to search
c. Supervisory++ Approach - This is used for efficient text classification.
Let us take an example where the user has searched for souls.
The user hasn’t specified anything except this word in his search but
because of store mapping method results can be generated. The
search results could be attached to about 40% of the products which
relates to the word.
3. Recommendation – once user makes some searches, the user lands on
product page or if the user again lands in home page, their opportunity
for upsell, cross sell, where Flipkart uses, Topic Modelling, Matrix
Factorization, Learning to Rank. The model is based on content based,
collabartive based and hybrid based filtering.
4. Retail and planning – Lead time for forecasting, what to buy, how much
to buy and where to buy. From past history of sales, how much quantity
it will sale in certain duration of time, lead time for these forecasting is
done using Data science. This is forecasting also checks on geolocation
based.
5. Catalog – the seller puts of their product with images and description
but these needs to be checked, if some mistake is there or any thing
that violates rules. Flipkart use Artificial Intelligence, Image
classification DL models and NLP to filter those out and make
correction automatically.
6. Checkout and Payment – recommending the user which can be his
most preferable payment option which maximizes the chances of
converting the transaction successfully
7. Fintech – Flipkart has huge data of Seller and Customer. In India less
than 3% people use Credit card, so Flipkart Uses this data to analyze
their transaction and different pattern of buying or selling. With this they
lend money on credit to customers to buy or sellers to sell.
8. Trust and Safety –
9. Logistics – Route optimization and inventory management.
Flipkart is applying artificial intelligence-based deep learning and
Machine Learning models to resolve last-mile delivery issues, which
helps to save time and resources at different stages of order
management.
10. Mira - Flipkart’s goal for Mira is to meet prospective buyers’ queries,
derive intelligent predictions and customisations, and tailoring
enhanced online experience resulting in reduced returns. The virtual
assistant has already contributed to an improvement in buyer
experience, increasing cart addition by nearly 12%.
11. Utkarsh - In another initiative named Flipkart Utkarsh (which means
‘excellence’), the e-commerce firm recently harnessed artificial
intelligence to improve on the quality of products sold by its registered
sellers.
Flipkart’s AI exploration and robotics space had led the company to
adopt a technology that enabled humans and bots to work together
seamlessly. Nearly 350 AI-powered bots – monitered Automated Guided
Vehicles (AGVs) – help operators process ‘4,500 shipments an hour at
twice the speed and with 99.9% accuracy’
these compact bots are easy to maintain and quick to deploy with a
single time battery of 7- 8 hours. these self-charging units automatically
guide themselves towards designated charging points when batteries
are down.
Using artificial intelligence to tackle pain points for customers and sellers,
Flipkart has automated and effectively addressed crucial needs. While not all
firms have the resources for extensive artificial intelligence coverage in all
areas of their business, artificial intelligence can give firms more control over
how they plug the gaps in customer needs.
