Predictive Custom er Relationship M anagem ent

Transcription

Predictive C ustom er
R elationship M anagem ent
Profiting from R eally G etting to
Know YourC ustom ers
H arlan C row der
PrincipalScientist
H ew lett-Packard Laboratories
Palo Alto,C alifornia
M ay 19-21,2002
M ontréal,C anada
Agenda
What is predictive CRM?
Predictive CRM toolbox
An example:
Mail stream optimization at Fingerhut
Critical success factors for predictive CRM
Conclusion and discussion
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Technology and applications are
driving predictive CRM

Computer power: Computational explosion
(Moore’s Law in the new economy)
1963 CRM:
600 customers
+ room-sized, $1M+ computer
+0.2MB, 0.5MHz

CRM data sources
supermarket
scanners,
in-house TV
show ranking
POS, stock trades, phones,
direct mail, credit cards,
prod. and service registrations,
Internet transactions . . .
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Hans Moravic, “When will computer hardware match the human brain?,”
Journal of Evolution and Technology 1 (1998)
Technology and applications (cont.)

Product and service customization
Mass production
Henry Ford (1920)
“Any color as long as it’s black”
Mass customization
IBM (1972)
One box, many uses
Individual customization BMW (2002)
90% build-to-order
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Predictive CRM
What do customers want? Can we identify new opportunities?
And how do you use information to market and sell?
Three problems:

Classification of customers into groups based on similar
behavior toward a given set of marketing and sales actions -customer segmentation

Describing customer behavior by building a behavior model
and estimating its parameters

Deciding which marketing actions to take for each segment
and then allocating scarce resources to segments in order to
meet specific business objectives
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Predictive CRM toolbox

Dynamic segmentation
Product affinity and bundling
Adaptive testing
Optimization of resources
Dynamic pricing
For more information, see
H. Crowder, et al., “Predictive Customer Relationship Management: Gaining
Insights About Customers in the Electronic Economy,” DM Review,
February 2001.
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Dynamic segmentation

Historical basis: homogeneous clustering;
static, infrequent updates
On-line environment has changed the process:

Immediate feedback,
testing
Constantly shifting segment boundries
Seg. criteria, rules evolve
Discrete process is now
continuous
Observe/
measure
Segment
Test
Actions
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Product affinity and bundling

“beer and diapers”
Customer choices can suggest affinity of
products and services
Fast data mining methods allow sophisticated
processing of large data sets; identification of
sequential affinities
Mobile devices plus pervasive computing
infrastructures opens opportunities for
“location-aware” product and service bundles,
and new branding mechanisms
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Adaptive testing

Estimating conversion rates was easier for mature
products with low-variability forecasts

For new, short-lifecycle products, a more rigorous
estimation process is needed

find the smallest sample size for which the estimate is
statistically accurate within specific parameters
At HP, some mobile computing products have 3-6 months
lifecycle; little historical preference data
Adaptive testing techniques:

dynamic sample size based on recent events
Bayesian approaches allow combination of data from various
sources
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Resource optimization

Resources include marketing budgets, product
availability and advertising space
Measures of effectiveness of business objectives:
profit, conversion rate, market share, customer
acquisition
The goal is allowing planners to analyze trade-offs for
various decisions, e.g., sensitivity and investment
analysis
Methods include mathematical optimization, dynamic
programming, meta-heuristics such as genetic
algorithms and simulated annealing
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Dynamic pricing

In many areas, the Internet has started making the
price-demand relationship more transparent
For example, recovering
historical data from online auctions such as
eBay allow computation of price-elasticity
relationships
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Mail Stream Optimization (MSO)
Helping direct mail retailers send less
junk-mail and make more money
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About Fingerhut
50 year history
2nd in consumer catalog
sales
general merchandise
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The data
65 mil. customers on file, 7 mil. active
9 terabytes of data on customers,
products and promotions
Large IBM mainframe relational databases
3000 customer attributes
purchase history
payment history
mail history
demographics
100 catalog attributes
content
presentation
mail dates
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The business problem
The business model
didn’t scale

increasing customer base
increasing mail quantities
increasing mail dates

The vertical marketing
model was wrong

inhibited profitability
ignored the customer
promotions
Bottomline:
line:
Bottom
Fingerhutwas
wasmailing
mailingtoo
toomany
many
Fingerhut
cataloguesthat
thatwere
wereending
endingup
up
catalogues
theircustomer’s
customer’strash
trashcans;
cans;
inintheir
excesscosts
costswith
withno
norevenue.
revenue.
excess
customers

time
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The solution: Horizontal Marketing -Optimize customer contacts over time
promotions
Segmentation
Segmentation
Budget
Budget Allocation
Allocation
Customer
Customer
Scores
Scores
customers
Optimization
Optimization
Saturation
Saturation
time
Refs:
H. Crowder, et. al., “Optimizing customer mail
streams at Fingerhut,” INTERFACES (31,1) 2001
System and Method for Increasing the Effectiveness
of Customer Contact Strategies, US Patent Pending
Determine which promotions
for each customer, NOT which
customers for each promotion
Optimize overall customer
value, NOT value for each
promotion
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MSO predictive CRM problems

Terminology

Customer segmentation

creating portfolios of investments
Saturation

mail plans and mail streams
measuring the cost of junk mail
Valuing customer contacts using optimization

What’s my ROI for a customer?
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MSO terminology
mail plan
The schedule of promotion mailings to be executed over a
specified time period. For example, Fingerhut normally
scheduled about 60 mailings in a three month period.
mail stream
For a given customer and a mail plan, a mail stream for the
customer is that subset of the promotions in the plan that
will be sent to the customer. For example, if a mail plan has
60 mailings, then a customer’s mail stream could be the 1st,
10th, 18th 33rd, and 59th promotions in the plan.
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Customer segmentation for
investment allocation
Growth invest for the future
High-value –
max. investment
Initial customer segmentation
criteria – “RFM”
•
•
•
Recency
Frequency
Monetary value
$ Profit
• Easy to obtain this data
• Provides broad investment
classes
• Precursor to more detailed
customer purchase history
segmentation
$ Revenue
“Up or out”
min. investment
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MSO saturation
Saturation is the measure of how promotions in a mail plan
adversely effects the revenue of other promotions in the plan.
S
Saturation is
caused by:
time proximity
product similarity
presentation
sales
similarity
0.25
time
Sij = fraction of promotion j’s sales lost due to promotion i
being mailed to the same customers.
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What is the value of a mail stream?
For a customer (segment) and a mail plan:
Rp
Ep
S p, p'
α
yp
expected revenue of promotion p
production and mailing cost of promotion p
saturation of promotions p and p’
control parameter
mail stream indicator switch:
1 if promotion p is in the customer’s mail stream
0 otherwise
Then the value of the mail stream indicated by {y} is
∑ (R
p
p
− E p ) y p − α ∑ Rp y p S p, p' y p'
p, p'
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Generating good mail streams within
a specified expense budget
If the amount of promotion expense for a customer or segment
is limited by a budget, then the following nonlinear binary
mathematical programming problem produces the best mail
stream for the expense budget:
Determine {y} in the expression
Maximize
∑ (R
p
p
− E p ) y p − α ∑ R p y p S p, p' y p'
p, p'
such that
∑E
p
yp ≤ B
p
where B is the expense budget limit
The Mail Stream Generation Model
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Mail stream optimization approach
Customer segmentation

primary parameters: revenue and profit
Saturation effects of mail plans

key to eliminating junk mail
Allocation of advertising resources via
portfolio optimization techniques

using profit expectations from segmentation
Optimal mail streams from MSG
Overall master mailing plan using
optimization
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The MSO solution

Run once a week

considers past 3 months and projects next 3
months
Processing time: 12 hours
The details

10,000 binary linear optimization problems (MSG)
20,000 variable linear programming problem
(master mail planning model)
IBM SP2 with 4 processors
IBM Optimization Subroutine Library, SAS, C++
3 year effort
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MSO financial impact

$3.5 million annual profit gain
6% advertising cost decrease
82% decrease in measured saturation of
mailings
21% increase in new customer response rate
First year ROI for the project
Non-financial: lot of happy trees!!
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Expense-revenue balance
Where was Fingerhut?
Revenue
“profit”
“over-mailing”
“opportunity”
Expense
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Critical success factors for predictive
CRM initiatives

It’s about designing and adopting new business
processes, not installing new technology.
It’s a destination and a journey.
It’s built on numerically-intensive methods.
Numbers mean data.
Budget accordingly.
Plan a phased rollout.
Small is beautiful (and manageable).
Monolithic predictive CRM initiatives fail.
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Conclusion

Predictive CRM draws on a variety of disciplines in
computer science -- data mining, database
management and data visualization -- economics,
operations research and mathematics. And, oh yeah,
PROGRAMMING!!
Most electronic economy businesses never see their
customers; they only see data about those customers
Predictive CRM is allowing businesses to increase the
personalization in a previously impersonal process
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Acknowledgement
Special thanks to:
Jamie Dinkelacker, HP Labs
Meichun Hsu, CommerceOne Labs
Shailendra Jain, HP Labs
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Contact information
Harlan Crowder
Principal Scientist
Hewlett-Packard Laboratories
Palo Alto, CA USA
harlan_crowder@hp.com
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