Driving B2B Customer Acquisition Through Powerful Data Management

© 2015 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc.

Other product and company names mentioned herein are the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in

any form or manner without the prior written permission of Experian. Experian Public.

Driving business-to-business customer acquisition through powerful data management

© 2015 Experian Information Solutions, Inc. All rights reserved. Experian Public. 2

Paul Henry Experian

Introducing:

@ExperianVision | #vision2015

Follow us on Twitter:

3 © 2015 Experian Information Solutions, Inc. All rights reserved. Experian Public.

Recent studies show

Stating the importance of CRM

data integration, managers reported

needing accurate, consistent

information that is captured by

a 360 degree, single view

of the customer.

Source: Scribe, The State of Customer Data

Integration 2013


Recent studies show

90% of marketers say they

are not effective at putting data

analytics to work

Reporting doesn’t show

marketing where to go

next

Source: Forrester, B2B Marketing’s Big Data

Destiny, April 2014

“

”


Questions, so many questions

How many customers do I have?

Do they represent contacts, sites, companies, groups….?

What upsell potential do they represent?

How many prospects are there?

Which channels to use for acquisition?

How much new business potential?

How do I find them?

How many are likely to churn?


B2B marketers’ challenges

Problem Implication Solution Value

We need to acquire

new customers

Revenue targets are not

met, market share falls

Richest quality data

sources underpins

marketing activity

A closed loop in

prospect / customer life

cycle enables sales,

marketing and credit

team to activities to be

joined up

Each channel (direct,

inside, etc.) is focused

on the appropriate

opportunity

Marketing activity is

predictable, enabling

planning and allocation

of resource to be made

more effective

We need to acquire

better quality

customers

Selling resource is not

being used effectively on

the best opportunities

Custom analytical

capabilities drive

targeted activity

We need to maximize

opportunity within

our customer base

Prevent competitor

poaching, increase

revenue at lower cost than

new acquisitions

Identify highest potential

and at risk customers

and allocate to

appropriate channel

We need to minimize

exposure to bad debt

Credit decisions

undermine marketing and

sales efforts or bad debt

affects the bottom line

Identify higher risk in

advance of marketing,

focusing attention on low

risk opportunities


Applying robust data

management techniques and

analytics can answer these

questions, solve these

challenges and deliver

the value expected


A holistic approach solves inter-connected problems

B2B Data

Insights

Cleansing data

Making it speak

Applying learning

Continuity

New customers Upselling / cross selling New segments Channel strategy

Analyze footprint Opportunities Segments Risks

Cleanse Match Append Integrate

Refresh Update Refine Web-based access


Each source file is different

A manual investigation identifies specific issues

Customised data cleansing steps are often required

Common issues also resolved with ‘standard’ processes

Both business name, address and telephone details, plus contact details resolved

Establishing accurate customer details is a key stage in the data management model

Cleansing data


GSFA Customer Id Name Address Line 1 City State Post Code MainPhone#

USS93050854WL Personal 4725 Dunberry Lane Edina MN 55435 9522203733

USS93047509WL N/A 1285 sw amboy ave Port St Lucie FL 34953 5613101218

USS93051626WL self 1068 Gerard Avenue Apt 8B Bronx NY 10452 6466066930

USS93046820WL Student 39800 Fremont blvd apt 206 Fremont CA 94538 4085061610

USS93051315WL Non 8663 virgil St. Dearborn Hts MI 48127 3134851589

USS93051002WL home 6969 Collins Avenue - Apt. 1112 Miami Beach FL 33141 3057907001

USS93051612WL lambert reu lui ernotte 62 b1 Bruxell PA 01170 3248546217x3

USS93051924WL residents 3008 Summerville rd Phenix City AL 36867 2703006092

USS93051150WL Student 1718 West 14th Street Erie PA 16505 8018542414

USS93050938WL No company 100 Norway Street Apt.2A Boston MA 02115 6178695887

USS93046609WL dhl 12812 23rd ave s Seattle WA 98168 2067791678

USS93047564WL Personal 8 Sequoia Court Voorhees NJ 08043 8569387004

USS93046655WL N/A 105 Belmont Dayton KY 41074 8594627440

USS93051727WL NA 17026 Darien Wing San Antonio TX 78247 2146768797

USS93052066WL personal 1371 E Lexington Ave #13 El Cajon CA 92019 6196339747

Examples help to identify data capture issues and improve practices at source

Cleansing data


Examples help to identify data capture issues and improve practices at source

awb_no rcvr_name rcvr_addr1 rcvr_city rcvr_state_cd rcvr_zip rcvr_phone

1004219274 51 FOREST AVE, # 75 OLD GREENWICH, CT, 0 88028837038 305 877 5828

1004219285 106 RAMBLING BROOK ROAD , CHAPPAQUA, NEW YORK 0 88028837038 2153378000

1029499726 INTERNATIONAL COFFEE CORPORATION 734 MARTIN BEHRMAN AVE. METAIRIE LA 5.51332E+11 8005359100

1029504350 DOLPHIN PRODUCTS INC. STE 580,4770,BISCAYNE BLVD SUITE 580 MIAMI FL 8767648192 3055768454

1029510252 PEACE CORPS 1111 20TH ST, NW WASHINGTON DC 21600155 2026921200

1029510462 OHL INTERNATIONAL INC 2255 E. 220TH STREET CARSON CA 322201429 3103780050

1029517694 BORDERFREE 11101 FRANKLIN AVE STE 400 FRANKLIN PARK IL 705 436 3515 212-299-3555

1029521710 THE PERISHABLE SPECIALIST, INC 9831 NW 58 TH STREET UNIT N131 DORAL FL 2171095 3054779906

1029524613 BORDERFREE 11101 FRANKLIN AVE STE 400 FRANKLIN PARK 0 519-841-5843 212-299-3555

1029527155 1 36-9 FORT. EVANS RD. NE. APT. # 6 LEESBURG VA 50326401843 4342471692

1029531193 BORDERFREE 11101 FRANKLIN AVE STE 400 FRANKLIN PARK 0 403-200-0490 212-299-3555

1029554131 BORDERFREE 11101 FRANKLIN AVE STE 400 FRANKLIN PARK IL 6138364101 212-299-3555

1029555074 CON. NEW YORK 801 2ND AVENUE, 6TH FLOOR NEW YORK NY 59521450911 126829441

1029556474 ZALMEN REISS & ASSOC. INC 171 47TH STREET BROOKLYN NY 14412795428 7184996900

1029557314 MARILENA MACHAEN 1092 HAMPSTEAD PL AUGUSTA GA 5.80414E+11 7707776770

1029559344 TAIRO INTERNATIONAL, INC 1111 KANE CONCOURSE BAL HARBOUR FL 14412795428 3058648933

1029560394 BORDERFREE 11101 FRANKLIN AVE STE 400 FRANKLIN PARK 0 7785333614 212-299-3555

Cleansing data


Referencing against a comprehensive external ‘universe’ adds perspective

Cleaned and normalized data

Matched to BizSourceSM companies and sites

De-duplicated headquarters and

locations

Live businesses

Business location and headquarter-level IDs applied to ALL RECORDS supplied, even if not

matching to external data providing a full single customer view backbone and identifying all duplicates

Consortium data

Trade credit

Public records

Collections

Firmographics

Corporate linkage

Utility data

Manual research

Cleansing data

Audit raw number of

records

Audit match rate across different customer types

Audit duplication rates at company

and site level

Audit non-live reason


Typical match rates in the high 80% to 90% range reflect good quality data

83%

68%

88%

27%

86% 91%

80%

Prospects Suspects Billing Leads -Online

Leads - Local CRM Overall

Match rates vary with the quality of input data

Example:

Logistics business with six distinct sources of customer and prospect information

Cleansing data


Even within a single source, an examination of match rates can reveal data management variations

Proportional

to number of

customers

Cleansing data

Example:

Business services company with a single source of customer data


Where customer records are matched to an external reference data set a perspective on the data quality can be generated

Duplicates are quantified and linked

Live and trading businesses are flagged

New addresses for moved businesses found

Records that represent inactive businesses are identified for further investigation

Resulting in improved customer communications and reduced wastage

External perspective can identify additional data quality improvements

Cleansing data

Example:

Financial services company

test sample of customer

records


Corporate linkage data applied to a single customer and market view identifies potential exposure and opportunities

Location variation level

Raw credit data (Unique to Experian)

Parent level

Separate entity with its own data roll-up

Ultimate parent level

Top-most entity in the corporate family

Location level

Rolled up credit data from location variation level

Headquarter level

All credit data from location level

Industry standard linkage structure provides easier implementation

Cleansing data

Ultimate parent BIN 4

LID 1

LID 2

LID 3

HQ – Branch (HQ = BIN 2)

Single entity BIN 4

HQ – Branch (HQ = BIN 5)

BIN 2 BIN 5

BIN 1

BIN 5

BIN 6

BIN 2

HQ

BIN 3

Branch

BIN 4

Single entity Branch HQ Branch

Search engine matching performed at this level against hundreds of millions of variations


Additional structure across the customer portfolio and market view enables a number of questions to be answered

Cleansing data

Ultimate parents / independent HQ

Company headquarters

Unique locations

Individual customer records / accounts

Are all customers linked?

What additional areas of potential are not explored?

Are all customer locations treated equally?

Are there new locations with potential to explore?

Do we have duplicates within a single system?

Are there customers with multiple relationships?

How many customers do we have?


The unification of individual source systems into a single repository creates a powerful platform

Cleansing data

Backbone

Payload

Business

Rules

Location, headquarters and group IDs

Link all source records together Billing system

CRM platform

Marketing data

Relationship, value, product, channel

Combined and assessed from each source

Written specifically for data available

Leverage recency, frequency and accuracy by source to generate “golden” record

Creates the single market view (customer and market populations)

representing the data platform for on-going activity


Feed back into source systems

MI reports

Improve customer communication

Drive marketing activity

Understand customer churn

Evoke trigger campaigns (e.g. win back)

Improve understanding of customer value, potential (and risk)

Focus today on Analytics for Marketing

So you’ve got a shiny new database platform, what shall we do with it?

Making it speak

Single Market View (customers and prospects)


Drives revenue growth inside and outside the customer base

Opportunity based as opposed to current relationship

Applicable inside and outside the customer base leading to a smooth handoff between acquisition and relationship management

Using analytics can deliver additional value into the market database supporting a range of operational activity

Making it speak

Hit rate (penetration)

Spend rate (value)

Margins

Churn rate

Risk / default

Analysis

Up sell

Acquisition

Cross sell

Share of wallet

Retention

Channel alignment

Implementation


Opportunity models are often the first benefits to be gained from the market database

From the total market, we first identify the prospectable market, and then within that an addressable market can be created using opportunity models

Two key dimensions are modeled:

Need (or propensity)

Potential (spend)

Making it speak

Field

High need & high potential

TS targets

High need, but low potential

Direct mail mass market

Medium need, low potential

Breed

Medium need, high potential

Potential

Outside addressable

market

Product need


Customers of deposit product Making it speak


Customers of cash flow product Making it speak


Sector and size highlight differences in customer portfolios

Making it speak

Deposit customer in green Cash flow in red


0

0.5

1

1.5

2

2.5

Pre 1970 1970-1979 1980-1989 1990-1994 1995-1999 2000-2004 2005+

Year of Birth

Ind

exed

pen

etr

ati

on

rate

Deposit

Cash Flow

The same two portfolios also have distinct age profiles…

Making it speak


… and financial health profiles

0

0.5

1

1.5

2

2.5

3

A B C D E

Megascore

Ind

exed

pen

etr

ati

on

rate

Deposit

Cash Flow

Credit Risk Rating

Making it speak


A typical methodology for exploiting the value from this data

This case study example had a predictive power

(Gini –coefficient) of 61%. All firms in the universe

can be scored and prioritized

While the output of a model is key –

getting to a final result requires

human decision making and multiple

iterations to ensure that the model

is effective and realistic

Profile

Client

Data Universe

Model

Validate

Match

Combining predictive variables leads to a single score

0.00

100.00

200.00

300.00

400.00

500.00

R1 M1 R2 Wh R3 B1 FI M2 PS B2 R4 OT

Co

eff

icen

t

International Sector

0.00

100.00

200.00

300.00

400.00

500.00

200+ 100-199 20-99 6-19 1-5

Co

eff

ice

nt

Employment Size

0.00

100.00

200.00

300.00

400.00

500.00

2+ 1 0

Co

eff

ice

nt

International Score

0.00

100.00

200.00

300.00

400.00

500.00

A B C D E

Co

eff

ice

nt

Future Growth Score0.00

100.00

200.00

300.00

400.00

500.00

Very LowRisk

Low Risk BelowAverage

Risk

Unknown AboveAverage

Risk

High Risk

Co

eff

ice

nt

Delphi

Making it speak


Applying the models to the full U.S. market quantifies the size of the prize at each level

Making it speak


Banking examples Cash flow finance and deposits potential

Making it speak

0

5

10

15

20

25

A B C D E F G

Ind

exe

d p

en

etr

ati

on

Bank B's Cash Flow Finance Model

The integration of two banks' customer data givesa perfect opportunity to validate the models

Indexed Bank A'sPenetration

Indexed Bank B'sPenetration

The model identified a population of companies that have 20 times the average appetite for cash Flow Finance, and 200 times the appetite of the companies

graded as the lowest potential

Volumes of prospect contacted: 320,995

New customers acquired: 3,956

Conversion rate: 1.23%

Actual additional loans value: 92M

Actual additional deposit value: 1.4B

Deposit generation campaign

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

-

500

1,000

1,500

2,000

2,500

0-25k 25k-50k 50k-100k 100k-300k300k-500k 500k-1m 1m-2m 2m-5m 5m+

Succ

ess

Rat

e

Vo

lum

e o

f N

ew C

ust

om

ers

Deposit Potential Modelled

Marketing Effectiveness - Finance

# of Company LevelSuccesses

% Company LevelSuccess

Volume of prospects contacted: 794,804

New customers acquired 8,113

Conversion rate: 1.02%

Additional potential deposit value: 4.6B

Cash flow acquisition campaign


Average revenue can also be modeled with different outcomes compared to market penetration

Making it speak

Example:

Business services company


The more granular the data available the more accurate and predictive the results

Making it speak

Example:

Business services company


Here a specific model is built to identify companies which will respond best to direct marketing for a leading healthcare insurance company

Over 18 months the model is used as the backbone for telemarketing

The proportion of customers acquired through the direct channel increased by 30%

► 8,678 New customers

► 25% generated through telemarketing activity

► Increase in revenue $21 million

► Increase in margin $7.5 million

► Cost per conversion – $108

► Profit per conversion – $3,609

Targeted propensity models in action substantially increasing margins

0%

20%

40%

60%

80%

100%

Historic customers Customers acquired inthe last year

% o

f b

ase

The proportion of customers acquired through a direct channel has increased by 30%

Direct

Broker Lead

31% 41%

Making it speak


Random

Selection

With

pH Model

Propensity model had Gini Score of 61% Total records 300,000 300,000

Client calls 300K records in an average

annual campaign Cost (Data + Calling) $319,500

Add model development $345,000

Lead generation was over 6 times higher

using the propensity model Leads generated

6,750

(2.3%)

46,574

(15.5%)

Cost-per-lead reduced by 85% Cost per lead $47 $7

A customized model was built to identify companies which have the highest propensity for a specific insurance product

Targeted propensity models in action substantially reducing costs per lead generated

Making it speak


Looking at potential is important, but we ignore risk at our peril

Credit policy approach Minimum credit line Maximum credit line

Aggressive – Credit line higher than

average, generally at the 75th percentile $5,300 $378,500

Moderate – Credit line at the average,

generally at the 50th percentile $1,300 $118,500

Conservative – Credit line lower than

average, generally at the 25th percentile $300 $47,800

81

28

13 8 6 3.9 2.9 2.1 1.5 1

14.76%

7.40%

4.79%

3.58%

2.81% 2.27% 1.87% 1.54% 1.25% 0.98%

0%

4%

8%

12%

16%

0

30

60

90

1 2 3 4 5 6 7 8 9 10

Decile Bads Cum delinquent %

IntelliscoreSM screens

out 74% of risk in the

bottom 20% of scores

Credit guidance from

hundreds to hundreds

of thousands

Financial Stability Risk

ScoreSM captures 60%

of bads in bottom 10%

Making it speak


By developing a perspective of appetite, potential and future risk, actionable marketable segments emerge, which can be tailored to fit or to build a sales and marketing strategy

Appetite for product or service

Level of

potential

($)

Risk of future

bad debt

Strong Weak

Low

Low

High

High

Ou

tsid

e o

f ta

rge

t m

ark

et

Exclude from

marketing for

now

Lower cost

marketing

approach

Immediate

targets

(Biggest wins

and

challenges)

Tier 1 targets

(Field / Multi-

channel)

Cu

rre

nt

Cu

sto

me

rs (

nu

rtu

re)

Making it speak


Potential and risk can be evaluated at both individual company and corporate group level

Making it speak


Simple analytics can support wider strategic decisions – e.g. territory and network planning

Making it speak


Evaluating total local potential defines optimal locations for branches, sales teams and territories

21st best location

Making it speak


Constant recalculation of alive/dead, births, addresses, data validation, enhancement

Regular matching, recalculation, flagging, etc.

Completing the solution – a virtuous loop

External universe

Customer bases

Market database

Analysis: Bespoke profiling,

modelling, segmentation

Reporting: Market sizing, customer base

dynamics, market opportunities

Direct access: Counts, cross tables, data selections,

response analysis

Campaign returns

Campaign outputs

Campaign history

Applying learning


Each touch point on the journey feeds back into the market database:

► Selections made

► Campaigns sent

► Results (positive and negative)

Allowing for updates to models and selection criteria

Zooming in on the closed loop Data captured at each stage

Applying learning


There are multiple options for gaining access to the data platform

Access available via stand alone web applications or software applications (analytics or campaign management) or via integration into a CRM platform such as salesforce.com

Web/Software Service

Client submits input files from source systems

External provider processes the files and provides data back to the client

Client loads output into their platform

Batch

External providers can host all client data

External team interact dynamically with internal client team to deliver analytics and marketing campaigns

Hosted

Directly integrated with CRM or analytics platforms via API

Integration

Continuity


Answers, so many answers

I know how many customers I have

I know this at all hierarchical levels (contacts, sites, companies, groups)

I’m focusing on upselling to their potential

My target market is defined

And, I know through which channels to market to them

And, the potential it represents

I have access to lists of the best prospects

While identifying those likely to churn


In summary

By skillful processing and analyzing data, you can make informed decisions

about the right businesses and people to talk at the right time and about the right

product / need ensuring you can acquire, develop and keep the most profitable business

customers

You need to think

creatively

Go beyond using the raw data

Add value with direct human intelligence, over and above the software

You need to be B2B

data experts

Business data is not the same as consumer data

Business decision making also has different drivers

You need flexible

solutions

To maximize success, the solution should be customized to fit your data and initial objectives

When more data is available the systems must adapt

© 2015 Experian Information Solutions, Inc. All rights reserved. Experian Public. 44

[email protected]

For additional information,

please contact:

@ExperianVision | #vision2015

Follow us on Twitter:


Share your thoughts about Vision 2015!

Please take the time now to give us your feedback about this session. You can complete the survey in the mobile app or request a paper survey.

Select the breakout

session you attended

Select the Survey

button and complete 1 2

Driving B2B Customer Acquisition Through Powerful Data Management

Business

Transcript of Driving B2B Customer Acquisition Through Powerful Data Management