Modeling and Segmentation
Telecommunications Industry 2007
GSU-MGS8040
Presentation Subtopics
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Telecom History
Scope of Presentation
Modeling
Scoring & Tracking
Segmentation
What’s Next?
1
Telecom History
Telecom History
• Pre-divestiture AT&T
– Little innovation
– No competition
– No price pressure
• Divestiture 1974-1982
– USDoJ split AT&T in return for entry into computers
– AT&T split into 7 Regional Bell Operating Companies (RBOC)
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Ameritech Corporation
Bell Atlantic Corporation
BellSouth Corporation
NYNEX Corporation
Pacific Telesis Group
Southwestern Bell Corporation
U S West, Inc.
3
History (continued)
• Divestiture 1974-1982 (continued)
– Surge in long distance competition
• Sprint, MCI, AT&T, BellSouth, Verizon, Quest
• LD prices drop
– Local monopolies remained
• local prices rise/static
• Telecommunications Act 1996
– State-by-state  Uniform national law
– Meant to promote competition
– Incumbent Local Exchange Carriers (ILECs) made network
elements available to Competitive LECs (CLECs) at cost plus
regulated wholesale
– LECs gained ability to provide LD services
– Lead to consolidation of major media companies (80 > 5)
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Evolution of Telecom Companies
From Wikipedia
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New Competitive Challenges
• New Technologies - Convergence
– Cellular Phone – Messaging, E-mail, Ring Tones,
TV/Video feeds
– Wireless Communication/Data
– VoIP
– Internet Access
– ISDN, DSL, T1
– Cable
– Cable/Wireless partnerships
– Television/Video (new)
– Bundle strategies
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Presentation Scope
Presentation Scope
• Single ILEC providing B2B landline products and
services
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~1.2M business customers, ~ 2.4M lines
1 - 200 employees
1 - 50 lines
1 - 10 locations
Top 5 industries: Retail, Wholesale, Business
Services, Manufacturing, Healthcare
– ILEC uses a three channel approach to the market
including Inbound centers, Outbound sales and Sales
Agents.
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Modeling
Why Model
• Increase Profitability
– Ameliorate line losses
• CLEC competition
• Cellular
– Sales targeting: outbound and Inbound, based on
customer behavior/attributes
– New product development and advertising strategies
– Efficient use of marketing and sales resources
– Segmentation Strategies: Identify groups of
customers based on predictions of their possible
business needs
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Line Loss History
# Lines Lost
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
Jan
Feb
Mar
Apr
60,000
May
Jun
Jul
50,000
Aug
Sep
40,000
Oct
Nov
Dec
30,000
Jan
Feb
Mar
20,000
Apr
May
10,000
Jun
Jul
Aug
0
Sep
Oct Jan-00
Nov
Dec
21,164
Jan-00
21,738
Feb-00
25,736
Mar-00
24,613
Apr-00
26,798
May-00
29,116
Jun-00
30,848
Jul-00
38,264
Aug-00
32,600
Sep-00
35,156
Oct-00
34,744
Nov-00
31,481
Dec-00
37,699
Jan-01
33,393
Feb-01
41,828
Mar-01
38,389
Apr-01
42,138
May-01
46,963
Jun-01
45,912
Jul-01
48,386
Aug-01
37,835
Sep-01
43,826 Jan-01 Oct-01
37,795
Nov-01
39,086
Dec-01
36526
36557
36586
36617
36647
36678
36708
36739
36770
36800
36831
36861
36892
36923
36951
36982
37012
37043
37073
37104
37135
Jan-02
37165
37196
37226
Competitive Line Loss
Jan-03
Jan-04
Jan-05
Jan-06
Month
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Line Loss History
Competitive Line Loss
50000
45000
40000
# Lines Lost
35000
30000
25000
20000
15000
10000
5000
0
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Month
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Telecom Modeling
• Statistical propensity modeling is the backbone of
telecom segmentation and offer strategy
• Every customer is scored by each model (probability and
L, M, H score)
• Models have been built and continuously updated for all
key products (Bundles, DSL, Lines, Line Add-ons, LD,
T1, Direct Internet Access, complex data, complex voice,
wireless, hosting, inert customers, customer
vulnerability/churn, and growth index)
• Predominantly logistic regression models - 70 variables
initially, with 5-10 in the final model
• Sales improvement from the use of models varies from
20-50%, over no targeting
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Automated Data Sourcing/Flow
Billing
Sales Quotas
and Targets
Modeling & Reporting
Datamart
List
Generation
Product
Usage
Service,
Maintenance
Trouble
Reports
Campaign
Tracking
Contracts
Monthly
Processing
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Automated Acquisition
Unit of Analysis
Matching
Cleaning
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Conflict Resolution
Business Rules
History
Summarize
Targeting
Tracking
New Product
Strategy
Reporting –
Scheduled, Ad
hoc
Data Views
• Calculated Variables
3rd Party D&B, InfoUSA
Advertising &
Sales Campaigns
Modeling &
Scoring
Scores,
Segments
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Modeling & Scoring Flow
Modeling &
Reporting
Datamart
Views
Store, Clean, Dummy
variables, Categorize,
Standardize, Calculate new
variables, Summarize
SAS
Enterprise
Miner
Refresh Models, New
Models, Ad hoc Models
Score Customers
Monthly
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Data for Modeling
• Snapshot of customer data for the most current month
• Total of 350-400 variables
– Customer history (3-6 months) for some variables
– Aggregated with summary functions (mean, min, max, etc.)
• Data cleaning
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Null, 0, Missing, Blanks
Impute
Bad values (out of range, wrong type, subjectivity)
Outliers
Transformations
Offsets
• Calculated variables
• Other pre-processing – decision trees, factor analysis, etc.
• SAS Enterprise Miner
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SAS Modeling Interface
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Dataset Drill-Down
Variable labels
intentionally
covered
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Logistic Drill-Down
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Neural Net Drill-Down
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Model Flow - Sample
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Logistic Results Drill-down (Confusion Matrix)
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Logistic Results Drill-down (T-scores)
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Cumulative Response (Lift)
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Scoring
Automated Scoring
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Score ~1.2M customers for each of ~ 25 models x 2 variants/model x 1-4 updates/refreshes per
year > 120 models/year
Customers scored with 2 values: probability (0.0-1.0) & score (L, M, H) for each model/variant
SAS code (32,354 lines ) - modularized, optimized for ease of maintenance and to some degree,
speed
– Declare global macro variables
• Date
• Product mean revenue
– Declare Libnames
• Establish OLEDB connection with remote database (SQL Server 2005)
• Connection/references to local subdirectories
– Code
– Raw Data
– Scores
– Prep for new data – delete datasets from previous month’s processing
– Retrieve data
• Connect to views and read data from remote server into local datasets
• Clean data, create calculated variables
– Launch scoring modules
• Score customers for ~50 models
– Store scores locally
– Save scores to remote server
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Scoring Process (%include files)
Model 1 Scoring
Code File
Master File SAS
Pseudo-Code
SAS Processing Flow
Pre-scoring Code
Model 1 Scores *
Data scores.model1;
set raw_data.cust;
…
run;
Model 2 Scores
Model 3 Scores
…
Model N Scores
Post-scoring Code
Model 2 Scoring
Code File
Modeling
Platform
Data scores.model2;
set raw_data.cust;
…
run;
* %include “code.Score_Model_1.sas”;
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Probability/Propensity vs Score
Score
Abbreviation
Probability
Range
Population
Size
High
H
0.50 ≤ H ≤ 1.00
~20%
Medium
M
0.25 ≤ M ≤ 0.75
~30%
Low
L
0.00 ≤ L < 0.50
~50%
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Tracking Model Effectiveness
Average
for Base
4.50
4.00
Effectiveness Index
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
A
B
C
D
E
Product
Low
High
F
G
H
• Monthly tracking with updating
as needed
• Effectiveness Index (EI): actual
sales compared to average
sales rate
• EI: multiplier showing how
effective the model is. E.g.
Product B model shows that a
customer scored “high” is ~3
times more likely to buy the
product than an average
customer
• Model differentiation: compare
High vs Low EI values. E.g. For
Products C-E, a customer
scored “high” is more than 7
times more likely to buy that
product than one scored “low”
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Model Performance Improvement - Refresh
Product X
9.00
8.00
Effective Index
7.00
6.00
H
5.00
M
4.00
L
3.00
2.00
1.00
-
Nov_06
Oct_06
Sep_06
Aug_06
Jul_06
Jun_06
May_06
Apr_06
Mar_06
Feb_06
Jan_06
Dec_05
Nov_05
Oct_05
Sept_05
Tim e Period
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Segmentation
Why Segment
• Increase Profitability
– Targeting
• efficient use of marketing and sales resources by targeting
inbound and outbound sales
– Messaging
• development of targeted marketing communications (i.e.,
Hispanic language direct mail, women owned businesses)
ensures messages reaches customers effectively
– Future Needs
• Identification of groups of customers based on their business
needs, not bound by traditional telecom products
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Segmentation Evolution
The segmentation process was continually evolved - moving from one dimensional
models to multi dimensional schemes. Along the way, predictive modeling was added
to the process to ensure the segmentation scheme was always actionable.
Seg 1
Seg 2
Seg 3
Seg 4
Seg 5
Seg 6
Customer
Complexity
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High
B2B
Technology
Retail/Service
Small Stable
Low
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Vulnerability
Value
Vulnerability
Industry
Location
One Dimensional
1997
Product
Targeted
Customer Size
Multi Dimensional
2001
2006
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Product Based Segmentation
D
E
F
A
B
C
Products
Complex
Simple
Low
Size
High
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Segment Profiles
Slide deliberately left blank.
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Segmentation with Propensity Modeling
• Add propensity modeling to the “static”
segmentation scheme
• Re-categorize customers into Segments
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Identify migrations from one segment to another
Identify customer growth areas/products
Promote stewardship for customer growth
Anticipate new needs
Develop new products
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Needs Based Segmentation (Product Migration Paths)
D
E
F
A
B
C
Products
Complex
Simple
Low
Size
High
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Additional Dimensions
D2
E2
F2
Products
Complex
D1
E1
F1
A1
B1
C1
Simple
Low
High
Third
Dimension
Size
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What Next?
What’s Next?
• Accommodate increased customer base (due to merger)
and increased geographic footprint
• More products, more new product development
– Bundles
– Television/Video
– Etc.
• Shifting competitive landscape
– Cable
– New partnerships
• Revisit segmentation complexity (product) and size axes
• Evolve segmentation strategies
– Growth Index  Lifetime value
• Other
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Growth Potential/Index
Customer’s Current Products and Value
• Product A x Revenue for A +
• Product B x Revenue for B +
Current Value
• Product F x Revenue for F =
X
Customer’s Potential Products and Value
• Product A x Revenue for A +
• Product B x Revenue for B +
• Product C x Revenue for C +
• Product F x Revenue for F +
Potential Value
• Product G x Revenue for G =
Y
Y – X = Growth Potential/Index
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Customer Lifetime Value
• CLV - value of a customer over the entire history of
customer's relationship company
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Acquisition cost
Churn rate
Discount rate
Retention cost
Time period
Periodic Revenue
Profit Margin
• Possibly include Satisfaction & Loyalty ?
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Acknowledgements
• Special thanks to Tim Barnes & Sam Massey,
AT&T - 2007
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Contact Information
David Pope, Ph.D.
Intelligent Strategies and
Information Solutions, Inc.
www.intelligentstrategies.com
770.271.9159
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