1. RETAIL
Customer Intelligence-Identifying most valuable Customer
C U S T O M E R
I N T E L L I G E N C E –
C U S T O M E R
P R O F I L I N G
T E C H N O L O G Y
ETL – Ideata
Data warehouse – Google
BigQuery
Reporting – Ideata
Models – Customer Profiling,
Customer Segmentation
O V E R V I E W
A US-based high-end luxury retailer in the space of apparels and
accessories has several stores in keychain across the US and sell
luxury apparels and related accessories. They want to identify
the most valuable customers within their existing customers
based on relationship and purchase patterns for proactive
relationship management purpose. They also wanted to assess
the profile of such best customers and catch them young in their
lifecycle.
A P P R O A C H
Customer segmentation based on relationship quotient and
identification of demographic and behavioral sweet spot for most
valuable customers.
1. Relationship segmentation
– Analyzing various transaction patterns, cycles, mix, etc.
– Design segmentation scheme based on Recency, Frequency and
Monetary factors
– Identify the most valuable customer segment covering 80% of
the business value with 20% customers
2. Segment Drill down
Understanding behavioral and demographic characteristics of the
best customers against rest
3. Identify Look Alikes
Create scoring model to identify customers who look like the
most valuable customers, however not yet reaching the status.
O U T C O M E
The Study not only identified the sweet spot of the business, but
also created detailed understanding of the best customers profile
and characteristics and helped the retailer to create effective
CRM program
2. RETAIL
Product Affinity Analysis and Customer Segmentation for a Pharmacy Retail Chain
P R O D U C T
A F F I N I T Y
A N A L Y S I S &
C U S T O M E R
S E G M E N T A T I O N
T E C H N O L O G Y
ETL – Ideata
Data warehouse – Amazon
Redshift
Reporting – Ideata
Models – Clustering model
O B J E C T I V E
Develop segmentation strategy for targeted campaigns to
ensure long-term active engagement and regular loyalty
card usage leading to improved revenues and profitability.
Understand product affinity to arrive at product bundling
opportunities.
A P P R O A C H
Segmented customers based on RFME and other product
purchase attributes to create 10 distinct customer segments
for designing targeted promotions.
Analysed transaction data based on product mix, bundling or
co-selling, category profit margin, store location and
redemption of store promotions and offers.
Designed campaign with targeted offers for each customer
segment using a variety of marketing channels such as SMS,
call back, direct mail etc.
O U T C O M E
Substantial improvement in renewal of Loyalty Cards.
Increase Ticket size, better customer retention % and
improvement in sales of high margin and private label
products
3. RETAIL
Customer Segmentation and Targeted Campaign Management for a Global QSR
C U S T O M E R
S E G M E N T A T I O N
& T A R G E T E D
C A M P A I G N
M A N A G E M E N T
T E C H N O L O G Y
ETL – Ideata
Data warehouse – Amazon
Redshift
Reporting – Ideata
Models – Clustering model
O B J E C T I V E
The client aimed to expand its home delivery business by
developing an intelligent target marketing framework based
on customer clustering and product affinities.
A P P R O A C H
Segmented customers based on RFME model to create 12
distinct customer segments for designing targeted
promotions.
Analyzed the transaction behavior of the customers around
six core areas – customer segmentation, product mix &
location, day and time of purchase, top co-selling products
and response to promotions/offers.
Designed campaigns with targeted offers for each customer
segment using a variety of marketing channels such as SMS,
call back, direct mail etc.
Used campaign response data to further improve customer
targeting.
O U T C O M E
Increased Average ticket size and improved retention rates.
Migration of large chunk of lapsed customer segment into
an active customer segment.