dma07 presentation at&t use case

8/9/2019 DMA07 Presentation AT&T Use Case

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Automated Modeling and Sales Targeting:Case Study for AT&T Advertising and Publishing

Gene EdmistonAT&T Publishing and Advertising

Don KridelUniversity of Missouri-St Louis / CopperKey


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Background AT&T Advertising and Publishing is large (multi $billion) yellow page company

with 3 regions (old Southwestern Bell, old Ameritech, Old Pacific) with hundreds

of local directories base listing (SRL) is free; all other listings (or ads) are priced according to size

(and placement) Penetration rates vary by directory (size, region, and competitiveness) but # of non-

customers (SRL only) is three-to-five times larger than the # of customers

The Issue How to best utilize sales force (effectively sized for the number of customers) toincrease penetration and revenue

In other words, need to know how to target available sales resources


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Sales targeting needed Sales force focused on existing customers Finding time for sales force to acquire new

customers is difficult Internal myths to overcome Reward structure

How to target cost-effectively?


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Model development/evaluation Will it work? Determine the way forward To the extent possible, plan for implementation

Differences in regional policies and sales forces

Produce a business case to justifyrequired expenditures


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Develop and test targeting models Modeling scope

One National model Three regional models (default assumption) Many (hundreds) local models

Variable scope Internal Variables only External (from national databases) only Both internal and external variables


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Develop and test targeting models Cost considerations

$, time, and internal resources Performance metrics to evaluate results

lift as compared to business-as-usual practices ROI

Note in Yellow Pages, acquisition is similar to up-sell (or cross-sell) in other settings,since the provider has data on non-customers as well as customers.


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Model development and testing Ten test markets across the three regions

Built several models Internal, external, both internal and external National, regional, local Differing time periods

Compare actual results with scored lists Evaluate and make recommendations


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Model development and testing Models required external data to be effective


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Model development and testing National model not feasible

Too many cross-regional differences in available data (recallthat regions were different companies in recent past)

In contrast to our null hypothesis going into project and previous experience with pricingmodels, local models were quite different

Local out-performed regional in lift by about 10-15%


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Model development and testing results Manual development of many local models not cost

effective Used CopperKeys automated modeling portal to

develop local models Results from testing was used to customize the

CopperKey knowledge base for the project


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Process Customer list (1s and 0s) geo-coded and matched to

national database for variable appends Knowledge base drives model development Scored lists, profiles, maps, etc. are available via web

interface


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Process


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After successful evaluation of models (ingeneral) and the CopperKey process (inspecific), contract was signed for 2005 100 large AT&T books 25 customer profiles


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Results (samples from 2005 and 2006)


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Results (samples from 2005 and 2006) ROI of over 3000%

Increased lift Converts from lists spend nearly 50% more per

month than BAU

Local models are estimated to out-performregional model by about

3000% ROI for local models vs. estimated 2000%for regional models


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More Results (from 2007) High ROI continues

Models have been utilized in assignment process and combined w/ DM

Increased sales from a single book exceed entirecontract amount by nearly 50%

Early results from additional books suggest similar results


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Next Steps Further integration of current project

More books in existing 3 regions Extend to new Bell South books Develop response models to improve targeting

further


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Next Steps Extend to other targeting problems

New prospects DM campaigns I-YP prospecting


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Next Steps Tighter integration into sales processes

Use results with other intelligence to assist indesign of prospect-groups (e.g., gold, silver,etc.)

Ultimately offering scripts


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Conclusions Targeting works (no surprise!)

Automated analytics provides cost effectiveway to drive modeling to lower levels

As always, integrating new methods into

existing business practices requires time, effort,and a champion


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Castillo, D.G.,D.R. Dolk, and D.J. Kridel, GOST: An Active Modeling System for Costing and Planning NASA Space Programs, Journal of Management Information Systems ,Winter 1991-92, Vol. 8, No. 3, pp. 151-169.

Dolk, D.R.,

and D.J. Kridel, An Active Decision Support System for Econometrics, Decision Support Systems , 7, 1991, pp. 315-328. Dolk, D.R.,

and D.J. Kridel, Modeling Telecommunications Demand Analysis, Interfaces , March-April 1993, Vol. 23, No. 2, pp. 3-13. Dolk, D.R.,

and D.J. Kridel, Towards a Symbiotic Expert System for Econometric Modeling, Current Research in Decision Support Technology , edited by R.W. Blanningand D.R. King, IEEE Computer Society Press, 1993, Chapter 7.

Edmiston, E.,and D. Kridel, Automated Modeling and Sales Targeting in the Real World: A Case Study for AT&T Advertising and Publishing, mimeo , 2007.

Kridel, D.J.

and D.R. Dolk, An On-line Marketing Consultant for Small and Medium Businesses, Proceedings of the 32nd WDSI Conference , Lihue, HI, April 2003.

Kridel, D.J.

and D.R. Dolk, Using Intelligent Profiling to Generate Smart Lists: An Empirical Test, Proceedings of the 33rd WDSI Conference , Manzanillo, Mexico, April2004.

dma07 presentation at&t use case

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