Modeling and SegmentationTelecommunications Industry 2007

GSU-MGS8040

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Presentation Subtopics

• Telecom History• Scope of Presentation• Modeling• Scoring & Tracking• Segmentation• What’s Next?

Telecom History

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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)

• Ameritech Corporation• Bell Atlantic Corporation• BellSouth Corporation• NYNEX Corporation• Pacific Telesis Group• Southwestern Bell Corporation• U S West, Inc.

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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

Presentation Scope

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Presentation Scope

• Single ILEC providing B2B landline products and services– ~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.

Modeling

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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

2000 Jan 21,164 Jan-00 365262000 Feb 21,738 Feb-00 365572000 Mar 25,736 Mar-00 365862000 Apr 24,613 Apr-00 366172000 May 26,798 May-00 366472000 Jun 29,116 Jun-00 366782000 Jul 30,848 Jul-00 367082000 Aug 38,264 Aug-00 367392000 Sep 32,600 Sep-00 367702000 Oct 35,156 Oct-00 368002000 Nov 34,744 Nov-00 368312000 Dec 31,481 Dec-00 368612001 Jan 37,699 Jan-01 368922001 Feb 33,393 Feb-01 369232001 Mar 41,828 Mar-01 369512001 Apr 38,389 Apr-01 369822001 May 42,138 May-01 370122001 Jun 46,963 Jun-01 370432001 Jul 45,912 Jul-01 370732001 Aug 48,386 Aug-01 371042001 Sep 37,835 Sep-01 371352001 Oct 43,826 Oct-01 371652001 Nov 37,795 Nov-01 371962001 Dec 39,086 Dec-01 37226

Competitive Line Loss

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Line Loss History

Competitive Line Loss

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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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Targeting

Automated Data Sourcing/Flow

Billing

Product Usage

Service, Maintenance

Trouble Reports

Campaign Tracking

Contracts

3rd Party - D&B, InfoUSA

• Automated Acquisition

• Unit of Analysis

• Matching

• Cleaning

• Conflict Resolution

• Business Rules

• History

• Summarize

• Calculated Variables

Modeling & Reporting Datamart

Monthly Processing

List Generation

Advertising & Sales Campaigns

New Product Strategy

Modeling & Scoring

Reporting – Scheduled, Ad hoc

Tracking

Sales Quotas and Targets

Scores,Segments

Data Views

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Modeling & Scoring Flow

Modeling & Reporting Datamart

SAS Enterprise

Miner

Store, Clean, Dummy variables, Categorize, Standardize, Calculate new variables, Summarize

Views

Refresh Models, New Models, Ad hoc Models

Score CustomersMonthly

Insert

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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– 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)

Scoring

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Automated Scoring• 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 3 Scores

Model N Scores

…

Pre-scoring Code

Post-scoring Code

Model 1 Scoring Code FileMaster File SAS

Pseudo-Code

SA

S P

roce

ssin

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low

set raw_data.cust; …

Data scores.model1;

Model 1 Scores *run;

Model 2 Scoring Code File

set raw_data.cust; …

Data scores.model2;

run;

Model 2 Scores

Modeling Platform

* %include “code.Score_Model_1.sas”;

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Probability/Propensity vs Score

Score AbbreviationProbability

RangePopulation

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

• 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

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Segmentation

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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

• B2B• Technology• Retail/Service• Small Stable

1997 2001 2006

ValueIndustry

LocationV

uln

erab

ility

Customer Size

Cus

tom

er

Com

ple

xity

VulnerabilityProduct

Targeted

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.

One Dimensional Multi Dimensional

• Seg 1• Seg 2• Seg 3• Seg 4• Seg 5• Seg 6

Hig

hL

ow

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Product Based Segmentation

A B C

D E F

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Complex

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duct

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SizeLow 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– 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)

A B C

D E F

Simple

Low High

Complex

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duct

s

Size

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Additional Dimensions

Size

Loca

tions

Low High

Simple

Complex

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nA1 B1 C1

D1 E1 F1

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Third Dimension

What Next?

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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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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 +• Product G x Revenue for G =

Customer’s Current Products and Value• Product A x Revenue for A +• Product B x Revenue for B +• Product F x Revenue for F =

Growth Potential/Index

X Current Value

Y Potential Value

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– 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