#TDPARTNERS16 GEORGIA WORLD CONGRESS CENTER

From CRM to HRM –Vodafone’s way to Household Relationship ManagementDr. Stefan SchwarzTeradata GmbH

• Vodafone in a nutshell• Evolution from CRM to HRM• Results• High Level Methodology • Learning – DOs & DON’Ts

Agenda

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Globally at home. Infinitely active What characterizes Vodafone

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Vodafone Group: Founded in 1984 in Newbury, United KingdomStakes in mobile communications networks in 30 countries and partner companies in a further 50 countriesA total of about 450 million customers around the worldA global employer of approximately 91.200 peopleOne of the largest companiesin the worldMost valuable brand in Europe

The nightmare of a central seat

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6 Degrees of Separation

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How does Vodafone know?

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• Vodafone in a nutshell• Evolution from CRM to HRM• Results• High Level Methodology • Learning – DOs & DON’Ts

Agenda

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Evolution from CRM to HRM

House Hold View

Person View(Cross BAN)

Bundle View(BAN View)

Contract View

HH View –Household consist of one or more persons living in one place

Household – Propensity based

Person – Fact + Propensity based

BAN – (Fact) based

Contract specific tariff offers based on individual usage (tariff simulation)

BAN related Offers (e.g. Vodafone Advantage, “ARUBA Family Card“) only where link is validated by customer

Contract – Fact based

Postpay Link fact basedPostpay-DSL Link partially validated through customerPostpay-Prepay only propensity basedPotential additional BANs only propensity based

Household as community of several individuals living together only propensity based. (only Vodafone customers)

Hidden Household – SNA basedHousehold members with mobile usage of Vodafone house hold but not yet customers of Vodafone only SNA based.

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Households do not necessarily share the same account

Billing View

Social View

BAN View Mobile HH View Mobile

• HH View provides integrated view on HH needs• Allows to identify total value

• BAN View provides view on chooser, not on user

• Vodafone in a nutshell• Evolution from CRM to HRM• Results & Value• High Level Methodology • Learning – DOs & DON’Ts

Agenda

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Household Goals – enhance household value through increasing HH size and development of products

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Reduce HH Churn

HRM –Targeted HH Offers

Additional contract within household

• Additional products within household• Enhance existing product

• Increase loyalty through household bundling (all in one)• Increase loyalty through value management of existing contracts

Develop

Keep

How does household penetration support revenue increase?

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Increase revenue through…

…. Capturing contracts out of total market

…. Increasing revenue of existing household

3 Dimensions of HH penetration

Occupy all 4 Telco dimensions within household

Address all members within household

Increase value per contract

Households – Our new VF Sales Agents

Intra Household Influence Contact PotentialShop visit within14 days after outbound contact

Churn ReductionPositive advertising indicatorannualized mobile postpay products churn

rate on base

• Vodafone in a nutshell• Evolution from CRM to HRM• Results & Value• High Level Methodology • Learning – DOs & DON’Ts

Agenda

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Build HistoryMatchingBasic Normalization

Four steps to match individuals and households

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Prepay

Postpay

FixnetCable

Fixnet DSL

1. Standardize (e.g. “Steet” vs. “Str.”

2. Normalize letters (e.g. ö, oe, ss, ß, etc)

1. Explicit links (• e.g. BAN Link, • manually set customer

links)

2. Similarity of attributes• Name + Birthdate• Name + Address• Name + Bank Account• Further combinations of

name, address, bank account, dob

Data Sources

Clear rules for• household splits up• Two households

merge

What happens to the HH-ID?

Accuracy is calculated through specific distance metrics. There are individual requirements for the min. accuracy for each matching criteria

(e.g. bank account need 100%, name needs less, etc.)

This will allow to create an end-to-end household view and the respective analytics cross line of business

Households with mobile products only

XXX%

HHs with DSL

products only

XXX%

HHs with products from all 3 lines of

businessXXX%

HHs with cable

products only

XXX%

HHs with mobile & DSL products

XXX%

HHs with mobile & cable

productsXXX%

HHs with products from DSL & cable

businessXXX%

Big box represents all households

Concept of SNA based Hidden Household Members identification to improve HH view

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Friends Family

• Highest activity on weekends• Activity during both peak and off-peak

hours

• Most regular activity• Highest same home location rate• Longest average call duration• Calls during any time of the day or week

Household Detection

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Focus on the Family Links

Build Triangles of Family Links

On-net

On-net On-net

On-net

On-net Off-net

Hidden Household Member= Target

On-net family triangles 58%

Off-net family triangles42%

Which areas are effected?

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Household churn vs. contract churn

Household value vs. customer value vs. contract value

Household value development vs. transaction value

Gross add vs. bundles sales

• Vodafone in a nutshell• Evolution from CRM to HRM• Results & Value• High Level Methodology • Learning – DOs & DON’Ts

Agenda

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Household View – Dos and Don‘ts

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• Accept Error within the matching processo Matching rules cannot solve missing

or inaccurate data

• Ensure constant matching rules seto Constant rules ensure a constant

systematic error – hence month on month changes are not caused by ruleset, but by household changes

• Simplify o Each additional person

and product multiplies possible scenarios

Dos Don’ts• Don’t compare Contract and HH

KPIso Multiproduct HH Churn is by

definition lower than one contract churn – define new KPIs

• Don’t assume a household “director“o Contracts are not necessarily paid

by the same person

• Don’t target on persono Identify needs within household

or of specific contract, then identify relevant contact within the household

Chance to POC on your way home ;-)

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Dr. Stefan SchwarzDirector Business Consulting Telco, Media & EntertainmentTERADATA

M: +49-173-74-88381stefan.schwarz@teradata.com

Thank You

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stefan.schwarz@teradata.com

Session ID: 311

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