1. SFbayACM.org Data Science Camp
Saturday, October 25, 2014
Greg Makowski
Twitter Tag #DMCAMP

2.  Customer Description – CC Company –
“Who” vs. “How” to Talk to Customers
 Hotel Price Optimization – Using
Clusters as Non-Linear Constraints
 Retail Supply Chain – Planning
Replenishment for 52 Week Demand
Curves

3.  Context:
◦ Major credit card company
◦ South American Market
◦ Repeat for Argentina, Brazil… and “dollar countries”
 Objectives or Problem:
◦ How to best manage the customer population
◦ Develop a software system, to repeat over geography
and time
◦ How to AUTOMATE understanding?
 How to automate naming the clusters?

4.  Solution, 3 projects for each customer base
◦ “WHO” to talk to…
 Customer Attrition Model – Neural Network (5 algs tested)
 Decrease in spending over time
 Basic vs. Supplemental Cards
 By 7 categories
 Challenge: Double digit inflation in some countries (90’s)
 Standardize by monthly spending
 Mining Factoid: Credit Card Digit 11 was predictive
 Billing cycles? Monthly salaries + high inflation
 Customer Profitability – Net Present Value
◦ “HOW” to talk to them…
 Cluster Analysis

5.  Consider Scalability
◦ 100k – 500k customers
◦ Some cluster methods are O(n) or O(n2)
◦ Use Kmeans to create 100 clusters O(n)
◦ Then use O(n2) methods to reduce from 100 clusters
down to 8-12 clusters
◦

6. Select the 5-15% customers
“highest” in the spike
1
4 7 10 13 16 19
Tree-Net
Random
Cumulative Profit
5% Customer
Groups
Total Profit / cell
Attrition
Profitability
83% of Attrition Profit was Lost in top 15%

7.  How to design the cluster analysis?
◦ Select top fields from neural network
 Sensitivity Analysis on the NN
 % spending by category
 Restaurant, Retail, Grocery, Hotel, Air, Auto, …
 Trend over time (slope, expected future value)
 Decide to create 8 – 12 clusters or customer segments
to communicate to marketers
◦

8.  Consider Scalability
◦ 100k – 500k customers
◦ Some cluster methods are O(n) or O(n2)
◦ Use Kmeans to create 100 clusters O(n)
◦ Then use O(n2) methods to reduce from 100 clusters
down to 8-12 clusters
◦

9.  Consider Scalability
◦ 100k – 500k customers
◦ Some cluster methods are O(n) or O(n2)
◦ Use Kmeans to create 100 clusters O(n)
◦ Then use O(n2) methods to reduce from 100 clusters
down to 8-12 clusters
◦ This uses all the data scalebly, and more
sophisticated hierarchical cluster search
◦

10. Clusters
Most customers  Least
ALL 1 2 N
Ordered
by
Importance
Fields 100% 36% 22%  5%

11. Clusters
Most customers  Least
Ordered
by
Importance
ALL 1 2 N
100% 36% Fields 22%  5% min MAX

12. Most:
Var X, Y, Z
Least
Var A, B, C
May have 12 clusters, 36 variables
Then each cluster may have 6 attributes
to use in naming
min MAX

13.  Select “WHO” with (Attrition)x(Profitability)
 Select “HOW” with Cluster Segments
◦ Given the variable selection, only a few clusters
matched most of the 15% subset of the customers to
manage
 Marketers could understand well the different
audiences and reasons for attrition – and
could better write copy for communication
 About 50 Executives walked around with the
one page cluster summary in their pocket,
frequently used to plan customer strategies

14. Analysis
Type
CRM
Behavior
Media
Message
$$$
Best
Customers
Upgrade, Downgrade
Loyal
Loyalty
Cross-Sell
Prospect
Segment
Reactivation
Attrition
Retention
Fraud

15.  Customer Description – CC Company –
“Who” vs. “How” to Talk to Customers
 Hotel Price Optimization – Using
Clusters as Non-Linear Constraints
 Retail Supply Chain – Planning
Replenishment for 52 Week Demand
Curves

16.  Objective:
◦ Optimize pricing for hotel rooms
◦ Take into account geography & use
 weekend, vacation, business, conference, …
 Seasons of the year as it relates to demand
 The hotel owns many brands (chains) focused
on different audiences
◦ Different price tiers, target audiences,…
◦ Hotel, motel, extended stay, …
◦ What “lessons learned” cross brands?

17.  Revenue Management is a general process used
to
◦ optimize profit
◦ given the remaining (plane seat or hotel room)
inventory
◦ the remaining time until the inventory is gone
 Operations Research
◦ Linear or Non-Linear Programming
 Lin or Non-Lin in either constraints or objective function
◦ Need an objective function to optimize
 Train predictive models to forecast price, given
conditions

18.  Data Mining and Operations Research Design
◦ When training predictive models, it helps to learn
behavior “in the same ball park” with the same
model.
◦ If the underlying thought process is fairly different,
subdivide the data into different subsets and train
different models. For example:
 Attrition: checking, credit card, line of credit, mortgage
 In Mortgage Bond Pricing: monthly prepayment of
none vs. 100’s vs. 1,000’s vs. a full refinance

19.  How do we group or divide individual hotels,
given all the attributes?
◦ Brand, location, % utilization weekday or weekend,
 Find bottom-up clusters, rather than top-down
assertions on the data
 For cluster variables – use best variables in
pricing predictive models (sound familiar?)

20.  Solution:
◦ 1) Build an initial predictive model predicting
pricing. Find the most important variables.
◦ 2) Create 8-16 clusters, using those variables
◦ 3) Within each cluster
 A) Train a predictive model for use as the OR objective
function
 B) Run a LINEAR OR price optimization, on the data
subset

21.  Customer Description – CC Company –
“Who” vs. “How” to Talk to Customers
 Hotel Price Optimization – Using
Clusters as Non-Linear Constraints
 Retail Supply Chain – Planning
Replenishment for 52 Week Demand
Curves

22.  The “Retail Supply Chain” is from
◦ the manufacturer to
◦ distribution center to
◦ Warehouse to
◦ Store to Consumer
 Replenishment is to re-supply products on the
shelves
◦ Minimize overstock and understock
◦ Heavy understock causes LOSS OF SALES
◦ Heavy overstock causes 30% end of season liquidation

23.  4,000 stores
 100,000 products/SKU’s (stock keeping units)
◦ 400 million store-product combinations
 52 weeks per year
◦ 20.8 billion store-product-week combinations
 Not the smallest problem in the mid-90’s
 Holidays shift in week number, from year to
year – need to adjust

25.  End up creating 2,000+ “profiles” or
centroids
 Assign new store-SKU’s to an existing profile
 If it doesn’t match (within a radius)…
◦ Re-run cluster analysis
◦ Lock existing centroids
◦ Create new centroids for data points outside
◦ Add to the “profile library”

26.  Bottom-up findings (after the fact)
◦ Buying hunting related items as the ducks migrate
north