The retail landscape is undergoing significant changes that are bringing challenges like complex customer behavior and increased competition. Retailers must leverage e-commerce and retail analytics to cut through noise and offer personalized customer experiences. Analytics use cases like sales and demand forecasting, trend identification for pricing and promotions, supply chain and inventory management, new product planning, and advanced techniques can help retailers address challenges and better understand customers.

2. The retail landscape is undergoing a
change unmatched in recent past.
This change has brought about a
number of challenges for retailers
ranging from complex customer
behaviour and stiff competition to
newer retail channels like e-
commerce.
Retailers are required to cut through
all the noise and offer their
customers a unique & personalized
experience.

3. Let us look at some e-commerce & retail analytics
use cases and why retailers must leverage them.
1. Sales-Profitability & Demand Forecasting:
The BI tools offer the following use
cases pertaining to Sales:
 Sales Analysis’ draws a complete
picture of each transaction starting
from mode of purchase till refund
status.

4.  Sales Personnel Performance & Profitability
Analysis
gives insights on how each salesperson is
performing.
 ‘Demand Forecasting Modules’ can
model scenario analysis to improve
the sales goal setting by
incorporating market data
 Market Basket Analysis uncovers
correlation between items
frequently bought together & drives
subsequent bundling opportunity.

5. 2. Trend identification to drive the
Pricing
& Promotion Plan:
Identifying customer trends is
very crucial for retailers. It
requires analysis of a huge set
of data, having a robust retail
data analytics platform boosts
the efficiency in planning and
rolling out these promotional
activities.

6. ‘What-if analysis
for costs’ &
‘Analysis of
purchase
decisions.
Price Elasticity
Analysis
User Behaviour
Analysis
Promotion Channel
Analysis
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Trend identification to drive the Pricing & Promotion Plan

7. 3. Supply Chain Analytics & Inventory Management:
1. With retail analytics
systems, retailers/ e-
decision-makers can create a
repeatable framework
Vendor Evaluation.
2. The housed Business
Intelligence (BI) systems can
gauge the Supplier
effectiveness vis-a-vis the Key
metrics identified by the
retailer.3. Inventory Optimisation with
statistical analysis of SKUs data
across outlets.
4. Combining Procurement
analytics
with supplier effectiveness for
optimum inventory sourcing
decision.

8. 4. Insights driving a New product release
plan:The supply chain is ‘pulled’ by the customers demand rather
than the traditional ‘push’ strategy driving the trends.
Retailers can use ‘Sentiment Analysis’ to capture this
demand and accordingly plan the product pipeline in
collaboration with their suppliers.
Measure & Counter cannibalisation, if any,
through ‘Scenario Analysis’
With Competitors’ Analysis, retailers monitor the
impact of the competitors’ pricing & promotion
events.

9. 5. Some Advanced Retail & E-commerce Analytics Use
Cases:
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Heatmaps: The decision-makers could gauge the
behaviour of web/app visitors and A/B test what is
working for them.
Recommendation Engine: It employs Machine Learning
to understand the user behaviour over time and displays
ads and promotions to a buyer pertinent to his past
explorations.
Fraud Detection: With Deep neural networks, they not
only identify & flag the fraudulent behaviour but also
in predicts them in advance.
Customer Lifetime Value Analytics: It identifies the key
determinants of a customer churn or a converting someone
as a loyalist which helps in benchmarking an ideal customer

10. THANK YOU!
Visit: www.polestarllp.com
Email: rishabh.rai@polestarllp.com