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Segmentation, Profiling, Regression, Association - Analytical Techniques Throughout the Loyalty Lifecycle

5th Customer Loyalty and CRM Summit, 4th-5th June 2013
by

Olivier Maugain

on 15 July 2016

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Transcript of Segmentation, Profiling, Regression, Association - Analytical Techniques Throughout the Loyalty Lifecycle

Analytical Techniques Throughout the Loyalty Lifecycle
Segmentation, Profiling, Regression, Association
Current
challenges

Create a unique shopping experience Sustain customer satisfaction
Personalised communication
Protect customer loyalty
Increase share of wallet
Customer intimacy
What is Analytics?
"The process of discovering meaningful knowledge (e.g. correlations, patterns and trends) by sifting through large amounts of data stored in repositories, using statistical and mathematical techniques."
Helps managers to predict what’s going to happen in the
future
(as opposed to conventional Business Intelligence, which is about explaining the past and the present)
Who qualifies for what?
Selected Techniques and algorithms
Profiling
Who gets what?
Likelihood to churn: High / Medium / Low
Loyal / "itchy feet"
Will respond to campaign yes/no?
RFM Analysis
Recency, Frequency, Monetary
Segmentation
Cross-Selling
Product recommendation
Product bundling
ROI
CEO
SPSS China
Tel: +86 (0)21 6352 3586
Mobile: +86 156 1858 6003
Email: maugaino@asia-analytics.com
LinkedIn group: "
Customer Analytics in Asia Pacific
"
Weibo:
asia-analytics
Dr. Olivier Maugain
Thank you for your attention
Objectives of CRM
Challenges for Marketing and CRM
Two key questions
Who are my best
(most loyal)
customers?
How to retain customers
and make them
spend more?
Measuring customer
value and loyalty
Simple regression
Time Series Analysis
Multiple regression
Customer Lifetime Value (CLV)
CLV = Current value (CNY)
x Growth of customer value (%)
x Customer longevity (# of months, years, visits)
Artificial Neural Network
Survival Analysis
(e.g. Cox Regression)
How much is customer A worth?

How much can we expect her to spend in the future?
Increasing CLV
Decision Trees
Also used to predict
who behaves in what way
Clustering
Association Rules
Market Basket Analysis
Case Study:
Campaign Optimisation
Objective:
Reduce
the number of ads (or targets) of a marketing campaign in order to improve its
efficiency

Assumptions:
- Number of ads sent: 1'000
- Cost of each ad: 100 CNY
- Value of a positive response: 1,000 CNY
- Random selection of targets
Results
(Random Selection)
Response rate: 10.0%
-> Number of positive responses: 100
-> Revenue generated: 100'000 CNY

Cost of campaign: 100'000 CNY
->
ROI
of the campaign:
0.0%
Results
(Using Analytics)
- Number of ads sent: 600
- Response rate: 13.5%








-> Number of positive responses: 81
-> Revenue generated: 81,000 CNY

- Cost of campaign: 60,000 CNY
-
ROI
of the campaign:
35.0%

-
Cost savings
: 40'000 CNY
Full transcript