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1. Use case problem solving
Big Data in Retail
G.DHANASEKAR
BCA-AI&DS
214031101006

2. Agenda
•What is Big Data?
•Big Data Overview?
•Why Companies Fail With Big Data?
•How is Retail Leveraging Big Data?
•Future of Retail Using Big Data?

3. What is Big Data?
■ Any voluminous amount
of structured, semi-
structured and
unstructured data that
has the potential to be
mined for information
■ Large/complex data sets
that traditional data
processing applications
cannot deal with

4. What is Big Data?
■ Often refers to the use of
■ Predictive analytics
■ User behavior analytics
■ Certain other advanced data analytics methods that
extract value from data

5. Big Data Overview

6. Why Companies Fail With Big Data
Data Management
•Sheer size of data
•Quality issues with data
•Data Complexity and interpretability
Organizational Silos
•Restricted free flow of data between
silos
•Restricted access and movement of
data limits the ability of firms to
capture data value
•360 degree view of customer data
across all products and services will
give much better insights
Capabilities
•Lack of Big Data Professionals
•Getting the right talent is the
biggest challenge

7. How is Retail Leveraging Big Data?
■ Changing how retailers approach customer
personalization and assortment planning
■ Identifying product attribute drivers
■ Collecting customer sentiment and social media data
■ Uncovering influential customers through social
media
■ Analyzing loyalty programs to identify profitable
customers
■ Creating store clusters based on customer data

8. How is Retail Leveraging Big Data?
■ Recommendation Engines
■ Based on a customer’s purchase history, what is he/she
likely to purchase next?
■ Customer 360
■ Customers expect companies to
■ Anticipate their needs
■ Have the products wanted on-hand
■ Communicate in real time (via social media)
■ Adapt to their needs as they change

9. How is Retail Leveraging Big Data?
■ Path to Purchase
■ Analyzing a purchase or the path to
purchase
■ Price Optimization
■ The right price on a product can
mean the difference between
making a sale and losing a
customer
■ Fraud Detection
■ The use of predictive capabilities to
create a baseline sales forecast at
the Stock Keeping Unit (SKU) level
■ If a product deviates noticeably
outside of that range, it could
indicate some fishy business

10. Future of Retail Using Big Data
■ Big Data and IoT
■ Stores will be enabled with
sensors that detect a nearby
shopper with the app on their
phone or tablet
■ The app will deliver timely
incentives and offers to help
■ Turn over more products
■ Introduce shoppers to new
products that they were not
aware of

11. Future of Retail Using Big Data
■ Big Data and social media
■ With better data analytics,
retailers can filter out all of
the useless noise and zero in
on real data which applies to
what they need to know
■ Their customers
■ Public perception of their
brands
■ How people respond to their
products

12. Future of Retail Using Big Data
■ Big Data and pricing
■ Big Data can also be used to establish better
pricing models like
■ Would customers pay more if the product
included X feature?
■ Would the product sell more if the price was $1
less?

13. Future of Retail Using Big Data
■ Big Data and supply chain
■ Determine the region where a
new product is going to be the
most/least popular and the
best route to get more stock
delivered there

14. Future of Retail Using Big Data
■ Big Data and store designs
■ Big Data can help with
■ Product placement
■ Display colors and styles
■ Floor plans
■ Arrangements of cash registers
■ Staffing and scheduling
■ Some stores have improved
revenue significantly by making
minor adjustments to the
layout of their retail stores