This document discusses using data-driven approaches to increase customer engagement and reduce churn. It outlines using qualitative analysis, descriptive analytics, and predictive models to better understand customer expectations, behavior, and needs over time. The goal is to proactively address reasons for customer "break-ups" like misaligned expectations or unmet needs. Predictive models can score customers by attrition risk and recommend the best actions, like customized offers, to retain high value customers. Taking this holistic analytical approach can provide insights to continuously improve customer relationships.

1. Avoiding the “Break-Up”:
A Data-driven Approach to
Increasing Engagement and
Reducing Churn
Jim Foreman
Staples, Inc.

2. The Dreaded
“Leaky Bucket”

3. Why the Leaky Bucket Isn’t Really True
in a Data-Driven Company

4. Customer Behavior
• Unlike water, customer behavior is
unique and not random
• It is based on individualized attitudes,
wants, needs, and relationships
• Most companies that group
customers into generic segments or
attempt to engage customers with a
vanilla, one-size-fits-all approach will
pay a price in terms of churn

6. Understanding A Break-Up
• Whether in business or our personal lives, most
Break-Ups have two root causes:
or 2. Unmet Needs1. Misaligned Expectations

7. Misaligned Expectations
•We have implicit or
explicit expectations of
every relationship in
both our business and
personal lives
EXPECTATION Personal Business
Honesty/Trust √ √
Physical √ √
Emotional √ ?
Experiential √ √
Financial ? √
Loyalty √ ?
• Few relationships are
successful in the long
term if the majority of
each party’s expectations
are not aligned

8. Misaligned Expectations: Who’s To Blame?
•“You can’t please all of the people all of the time…” –
though people and businesses often go to great lengths
to artificially increase their attractiveness

9. Misaligned Expectations: Who’s To Blame?
•“You can’t please all of the people all of the time…” –
though people and businesses often go to great lengths
to artificially increase their attractiveness

10. Unmet Needs
• Even when initial expectations are sufficiently aligned, the
deepening of any relationship may reveal wants/needs that
are not (or no longer) being met
• All relationships evolve over time – in order to succeed,
both parties must evolve and adapt to the changing needs of
the other party
• In business, nirvana is a customer who:
• Has an emotional connection to your company or brand
• Feels like “…they really ‘get’ me!”
• Becomes a brand evangelist and tells others about it

11. The Challenge
• How can we sort through all of the “noise” to:
• Better-align with customer expectations
• More deeply engage customers by evolving
our relationships with them
• Demonstrate an ongoing understanding of
customer needs and how to best fulfill them
• Reduce the likelihood of a break-up
• Proactively identify customers who may be
on the path to a break-up

12. Qualitative
Analysis
Descriptive
Analytics
Predictive
Models
Our Approach
to hear from
customers themselves
to learn from past
customer behaviors
to anticipate future
behavior
Ante: Both the ability and willingness to
effectively communicate with your customers

13. Qualitative Analysis
“The only stupid question
is the one not asked…”
• Customers are surprisingly willing to share their
feelings (good or bad) and reasons for taking
certain actions
• Customer sentiment can be evaluated through:
• Surveys / Focus Groups
• Social Media
• Leverage “Active” vs. “Passive” break-ups

14. Descriptive Analytics
• Most companies have access to tools and large
quantities of rich but under-leveraged data that can be
mined for insights on customer engagement and
attrition risk:
• Demographics/Firmographics
• RFM / Transaction History Data
• Coupon and/or Discount Usage (Type / Frequency)
• Interaction / Promotion History and Response
• “Big Data” – Online Browsing, Click-stream, etc.
• Techniques: Visual Analytics, Deciling, Cluster
Analysis, etc.

15. Descriptive Analytics: Example
•Differential Customer Profiling
• Select key metric(s) – sales, profit, response, etc.
•Decile (or cross-decile) customers by these metric(s)
• Separately profile customers in top and bottom deciles
• Identify dimensional differences between top and
bottom performers
• Test marketing actions to incent desired behaviors
• Re-decile and profile periodically to validate approach
•Potential Trap: Correlation vs. Causation

16. Predictive Analytics
• Though there are no guarantees, a solid understanding
of the “what” and “why” of the past significantly
enhances our ability to predict the future
• Development of predictive models results in optimal
actionability based on your analytical findings
• Model-building tools and talent are readily available in
the marketplace at very reasonable costs
• Walk before you run: while large and complex models
can be extremely powerful, even relatively straight-
forward regression models can have a big impact

17. Predictive Models: Example
•Tenured Attrition Model: Scores tenured but recently
inactive customers by their likelihood of attrition
(1=Most likely to be Retained, 10=Most likely to attrite)
• Key variables:
• RFM (Recency, ∆ Frequency, ∆ Sales)
• Categories purchased
• Coupon Usage
• Web site browsing behavior
• Tenure

18. Predictive Models: Example
Model
Score
1
2
3
4
5
6
7
8
9
10
Attrition
Risk
High
LowEmail:
Lower
value offer
Lowest Risk: more likely to
redeem offer, but less likely to
drive incremental retention as
they may buy again on their own
Highest Risk: less likely to
redeem offer, “one foot out
the door” already
Moderate Risk: best chance
for incremental retention
benefit
Email: Medium-
High value offer
DM:
Low-Medium
value offer

19. Attrition
Model
Score
1
2
3
4
5
6
7
8
9
10
Attrition
Risk
High
Low
1 2 3 4 5 6 7 8 9 10
High Sales Decile Low
IGNORE
EMAIL:
Medium Value
Offer
EMAIL:
Low Value
Offer
DM:
High Value
Offer
TELESALES
w/Offer
DM:
Medium Value
Offer
EMAIL:
Low Value Offer
EMAIL:
High Value Offer

20. Predictive Models: Synergy
• Often the creation and use of one model
opens the door to other models that can
work synergistically with each other to
further drive insights and results
Attrition
Model
Revenue
Model
Lifetime
Value
Model
Next Best
Action
Model

21. My Contact Info:
Jim Foreman
Director, Analytics & Customer Insight
Staples, Inc.
james.foreman@staples.com