IE Business School Marketing Intelligence Project by Group F: Melody Ucros Jina Kim Andrea Blasioli Adedeji Rodemade Fergus Buckey Alex Kyalo Louis Rampignon Data Source: http://archive.ics.uci.edu/ml/datasets/online+retail

1. Creating a truly personalized
retention strategy
Using Customer Segmentation
MBD O1 – Group F
UNIK

2. Company
PROTECT REVENUE STREAMS
BY CREATING A
CUSTOM-RETENTION STRATEGY
FOR MOST VALUABLE SEGMENT
Company:UK Based Online Retailer
Product: Unique All-Occasion Gifts
Revenue (13months):9.7 Billion(84% UK)
Customers: Mainly Wholesalers
Size: 4k+ Customers
Context
UNIK
Retail

3. Importance of Retention
Better Up-Selland Cross-
SellOpportunities
IncreaseCustomer Satisfaction
More Referrals
GettingNew Customers
is Expensive
ProtectRevenueStream
Less Price Sensitive

4. Questions To Be Answered
I. What does “most valuable segment” mean?
II. Which are the existing customer segments?
III. Which segment is a retention priority?
UNIK

5. Our Suggested Approach:
Customer Segmentation
UNIK

6. What it is Why use it
UNIK
A Technique used by marketers to divide target
customers and profile them based on who they are,
what they need or how they interact with the
company, in order to service them better.
TRADITIONAL APPROACH:
• grouping customers based on explicit demographic,
behavioral, psychographic, or geographic facts
• Easy to query this information for answering specific
questions, but impossible to create a custom strategy for each.
Info is too general, and based on too many assumptions
MACHINE LEARNING APPROACH:
• discover homogenous groups that can be treated equally
within your customer base, using all the information available
about the customer at the same time & unsupervised
• Requires Technical Expertise, but gives the best outcome when
trying to segment based on a more complex unknown
parameter like, for example, their value to the company
Customer Segmentation is the most important
aspect of a marketing strategy. The insights
gathered will have an influence on:
• Branding Objectives
• Budget Allocation
• Channels Used
• Innovation Criteria
• Competitive Advantage
• ProductsOffered
• Pricing Structure
• Logistic Requirements
• Customer Service
• Programs and Incentives

7. UNIK
Ways to Define Value
Profile-Based
Product-Based
Customer LTV
Customer RFM
• Threshold segmentation method is a traditional approach of
segmenting customers. The basic idea is to partition/categorize
customers based on a single cutoff point for a variable or a
combination of two variables. The threshold is not clear cut, and
the resulting clusters don't form natural groups of customers.
• Only focuseson one aspect of value: pastand future revenue.
• Hard to measure for many businesses, but especially online retail,
as customersare usuallynot long-termlike the telecom sector.
• Traditional Approach, calculated without using machine learning.
• Traditionally, it groupscustomers based on similar patterns
regarding the recency,frequencyand revenueof purchases.
• Better done with machine learning in order to personalizethe
definitionof a valuablecustomers and include those variables.
• Allows us to create and prioritizemarketingstrategies based on
the impact these customershave on the company.

8. Our Implementation:
with RFM-based segmentation
(with a twist)
UNIK

9. UNIK
A Valuable Customer for UNIK
R
F
M
C
U
Long-Term and Recently-Acquired Customers
Customers who buy very frequently
Those who generate the most monetary value
Customers who do not cancel/return orders
Those who continuously buy large bulks of only a few unique items

10. Data Available Transformation
UNIK
For our demo, we were given 13 months of
transactional data where the following information
for each customer was available:
• Customer ID
• Invoice Number
• Stock Code
• Product Description
• Quantity
• Invoice Date
• Unit Price
• Country
Using Python, we created the following features for
each customer and used them as the input for our
clustering algorithm, segmenting based on “value”:
• Time since Last Purchase
• Number of Transactions Made
• Average Order Value per Customer
• % of Cancelled Orders
• % of Unique Products Purchased

11. Clustering Methods
UNIK
Center-based
The representative point in the
group (centroid or medoid) defines
a cluster, and each object is
assigned to the cluster that its
centroid is the most closestto.
ADV: easy to implement / interpret
DISADV: have hard time detecting
non globular clusters,cannot
detect uneven size clusters
Hierarchical Density-based
Clusters are divided into nested
subsets.In a tree like structure, the
lower nodes will be the subsets of
the higher nodes. The top end will
be the root (i.e.,all observations),
and the last leaves will be the
clusters.
DISADV: usefulfor very specific
needs (taxonomy), hard to know
when to ‘cut’ the tree
Observations are divided by the
density of space -> clusters are the
high density (many observations)
area, while the low density areas
are considered noises/errors.
ADV: detects unusual shapes
DISADV: if observations are spread
out and uneasy to find ‘low’ density
areas, then clustersaren’t formed

12. Selection Criteria
UNIK
Type Scalability Different Types of Atributes Noise & Outliers Interpretability & Usability
Center-Based
Hierarchical
Density-Based

13. Our Segmentation Results:
Using Density-Based Method
UNIK

14. Clusters Identified
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Clusters found with DBSCAN + PCA

15. Cluster 1: Dormants
UNIK
• Dormant customers: many are long time customers that
have not bought from the store for a long time
• Also buy unique products in small quantity – probably one
time usage
• The items they buy are on average more expensive
(higher unit prices), but because frequency and quantity is
low, revenue is subsequently low

16. UNIK
Cluster 2: Stars
• By far the most frequently purchasing cluster
• Also more recent customers, although variation is high
• Yields the most revenue, and in some cases extreme
outliers
• Usually buys the same product over and over again, but
also most likely out of the 3 to cancel products

17. UNIK
Cluster 3: Risky
• The store’s newest flock of customers with similar
patterns as cluster 1: High AOV and % of Unique Products
• Still interact with the store a lot more, but if no actions
are taken, they could become dormant.
• Buy products in large bulk a lot more than other clusters

18. Prioritization Criteria
UNIK
Clusters Current Value Risk of churn Priority Strategy Needed
Dormant Low High 3 Activation
Stars High Low 2 Reduce Cancelations
Risky Moderate Moderate 1 Retention

19. Our Recommendations:
for Risky Cluster of Customers
UNIK

20. UNIK
Retention-Strategy
Tiered Discount and ShippingRates
Drop ShippingOption for Wholesale Clients
Point System to Reward Based on Actions Taken
Personalized Analytics, Customer Service and Delivery
If these customers are comfortable with making big purchases in value and size already, incentive them to increase their current
order size with increasing discount rates based on number of items purchased, and free-shipping on their first order.
If these customers are purchasing frequently, and mainly unique items, it is because they most likely have the order from the retailer
already. Help them deliver their order faster, and look better with their client by offering to drop-ship the items directly to the retailer.
These is the strategy to get wholesalers and individuals to sign up for your loyalty program. Ask them to redeem the points at the end
of their first order by providing their email. They will earn points based on taking actions you identified essential for LT conversion.
Communication is essential for wholesale customers. Assign them a specific point of contact regarding their delivery and needs, and
and incentivize them to provide a specific point of contact in their company. Now, there is a direct person incase they fall-off.

21. Future Possibilities
UNIK
Build a Predictive Model for Inventory:
Match Customer ID with Cluster Number and Product
Description to identify patterns regarding products
purchased. Then, predict the level of inventory needed
considering the risk of churn and buying habits of
different clusters.
Personalize Front Page of Website
It would be smart to do some A/B Testing with
individuals from different clusters,in order to
identify which web UI is more effectively driving
them to make an order.
Create Custom CommunicationStrategy:
Based on the cluster each customer belong to, you can tailor
the promotions you send, when you send it and what to send.
This will be helpful to help them remember that you still
care, and that you remember your last interaction with them.
Build a Predictive Model for Churn
In order to do this, you would need data from many
other sources.But, this would be good if you want
to know which customer to target and when in
order to reduce their probability of becoming part
of the dormant cluster. A very clear activation
strategy is needed for those that are dormant.

22. Expected Results
UNIK
“Even a 5 percent increase in customer retention can lead to an increase in profits of between 25 and 95 percent.”
- Bain & Co
Current 6M Growth Rate: 22%
6M Expected Increase in Retention: 5%
Total Increase in Revenue Every 6 Months:
22% + 25% = 47%0%
2%
4%
6%
8%
10%
12%
14%
16%
$-
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
Now Q2 Q4
1 Year-Projection
Current Revenue Retention Rate
* Disclaimer: These results are assuming equal impact on all clusters,and ignoring seasonality effects
Inthousands

23. Next Steps
UNIK
Assign an Account Manager for
each Wholesaler to gather
more insight about needs, and
build stronger relationship
Offer them the discounts,and
the opportunity to join an
upcoming "pilot program" for
reward points
Calculate
discounts we can
offer
Contact the customers who
have the largest time since
last purchase first.
Launch the
pilot program!
Map the "ideal" customer journey, and
allocate the applicable points to each step, and
what the customerswill do with the points.

24. Appendix – Comparing clusters