Inspire 2013 - Money matters- Enhancing Database Marketing with Relevant Economic Information -...

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Money Matters Enhancing database marketing with

relevant economic information

Mike Jacobs

SVP & General Manager, Segmentation Solutions

Equifax

@Equifax

Business Leadership track

March 7, 2013

11:00 – 11:45 am

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Money matters Your most valuable data resides within your customer database --- transaction history, customer interactions etc.

Economically based segmentation provides a composite framework, and forms a bridge between internal and external views of the customer

It is highly beneficial to append demographic and economic data to customer records

This provides a limited view; the customer may have minimal dealings with your company, yet have significant financial / economic capacity overall

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Economic Cohorts IXI Services collects detailed financial data from around 100 of the USA’s largest financial institutions

Classification variables include:

Income (wages + investments)

Discretionary spending

Credit (# accounts, balance, adverse)

Home ownership and mortgage

Lifestage demographics

Geography

Economic Cohorts classifies households into 71 distinct clusters with similar

economic characteristics

Publicly available data sources are paired with factors derived from this proprietary data foundation

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Economic Cohorts: 16 groups, 71 clusters

Young

<35

Working Years

35-54

Pre-Retirement

55-64

Retired

65+

Income

Low

$0k-$50k

Moderate

$50k-$100k

High

$100k-$200k

Elite

$200k+

Clusters 1-6

Clusters 7-12

Clusters 13-17

Clusters 18-21

Clusters 22-27

Clusters 28-33

Clusters 34-38

Clusters 39-42

Clusters 43-48

Clusters 49-54

Clusters 55-59

Clusters 60-63

Clusters 64

Clusters 65-67

Clusters 68-69

Clusters 70-71

A E I M

B F J N

C G K O

D H L P

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SimCo --- simulated telecommunications SimCo sells a subscription product, which normally has a 2-year initial contract

There are three product packages:

Basic: 49.2% of sales, average value $40 per month

Enhanced: 27.2% of sales, average value $75 per month

Premium: 23.6% of sales, average value $120 per month

SimCo has approximately 1.65 million past and present customers

Subscribers may also have additional features in their contracts.

Basic: 18.7% subscribe to additional features

Enhanced: 29.7% subscribe to additional features

Premium: 13.9% subscribe to additional features

There are also additional usage charges.

#

Penetration index: Active subscribers

141

70

181

87

198

60

150

58

98

42

184

66

43

51

105

40

29

41

42

20

47

0 100 200 300 400

A01: Tough Start: Young Single Parents

A02: Tough Start: Young Singles

A03: Starting Small: Small-Town Families

A04: Starting Small: Small-Town Singles

A05: Living on Loans: Young Urban Single…

A06: Living on Loans: Young Urban Singles

B07: Mid-Life Strugglers: Families

B08: Mid-Life Strugglers: Singles

B09: Getting By: Small-Town Families

B10: Getting By: Small-Town Singles and Couples

B11: Credit Crunched: City Families

B12: Credit Crunched: City Singles

C13: Retiring on Empty: Singles

C14: Burdened by Debt: Singles

C15: Sensible Spenders: Families

C16: Sensible Spenders: Small-Town Empty…

C17: Sensible Spenders: Urban Pre-Retirement…

D18: Relying On Aid: Retired Singles

D19: Rough Retirement: Small-Town and Rural…

D20: Struggling Elders: Singles

D21: Modest Means: Urban Retirees

< 3

5

35 -

54

55 -

64

65+

LOWER INCOME <$50,000

199

112

157

54

135

98

188

62

198

72

208

91

10

121

78

111

61

60

27

11

54

0 100 200 300 400

E22: Credit City: Young Families

E23: Credit City: Young Singles

E24: Midscale Mainstream: Small-Town Families

E25: Midscale Mainstream: Small-Town Singles…

E26: Getting Ahead: Young City Families

E27: Getting Ahead: Young City Singles

F28: Living Simply: Small-Town Families

F29: Living Simply: Small-Town Singles and…

F30: Credit Rules: Urban Families

F31: Credit Rules: Urban Singles

F32: Suburban Stability: Families

F33: Suburban Stability: Singles and Couples

G34: Committed to Credit: Small-Town Couples

G35: Striving for Balance: Urban Pre-…

G36: Conservative Consumers: Small-Town…

G37: Conservative Consumers: Suburban…

G38: Solid Foundation: Suburban Empty Nesters

H39: Retired on Credit: City Singles and Couples

H40: Safety Net Seniors: Small-Town Retired…

H41: Nest Egg Elders: Older Retirees

H42: Comfortable Retirement: Suburban Couples

< 3

5

35 -

54

55 -

64

65+

MODERATE INCOME $50,000-$100,000

* Penetration index = 100 * [customer household %] / [market household %]

#

Penetration index: Active subscribers

351

112

161

74

204

53

270

90

299

91

262

110

71

156

90

128

187

44

47

29

111

0 100 200 300 400

I43: Charge-It Champs: Young Suburban Families

I44: Charge-It Champs: Young Suburban Singles

I45: Confident Futures: Young City Families

I46: Confident Futures: Young City Singles and…

I47: Material World: Urban Families

I48: Material World: Urban Singles

J49: House of Cards: Suburban Families

J50: House of Cards: Suburban Singles and…

J51: Prudent Professionals: Suburban Families

J52: Prudent Professionals: Suburban Singles…

J53: Suburban Success: Upscale Families

J54: Suburban Success: Upscale Singles and…

K55: Living for Today: Couples

K56: Planners and Savers: Suburban Families

K57: Planners and Savers: Suburban Couples

K58: Planners and Savers: City Couples

K59: Country Club Climbers: Suburban Empty…

L60: Comfortable with Credit: Upscale Retirees

L61: Rewarding Retirement: Affluent Suburbanites

L62: Affluent Elders: Older Upscale Suburbanites

L63: Established Wealth: Suburban Retirees

< 3

5

35 -

54

55 -

64

65+

HIGH INCOME $100,000-$200,000

132

172

300

184

136

148

18

46

0 100 200 300 400

M64: Big Shots: Young Upmarket Urbanites

N65: Careers First: Urbanites

N66: Executive Spenders: Suburban Families

N67: Executive Spenders: Suburban Couples

O68: Corner Offices: Executive UrbanitesO69: Champagne Tastes: Executive Empty

Nesters

P70: Flush Funds: Wealthy Urban Seniors

P71: Diamonds and Pearls: Wealthiest Retirees

< 3

5

35 -

54

55 -

64

65+

ELITE INCOME $200,000+

* Penetration index = 100 * [customer household %] / [market household %]

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Value index: Penetration vs. revenue

0 200 400 600






















< 3

5

35 -

54

55 -

64

65+

LOWER INCOME <$50,000 0 200 400 600






















< 3

5

35 -

54

55 -

64

65+


* Value index = 100 * [revenue %] / [market household %] Key: Active subscribers

Subscription revenue

#

Value index: Penetration vs. revenue

0 200 400 600






















< 3

5

35 -

54

55 -

64

65+

HIGH INCOME $100,000-$200,000 0 200 400 600






Nesters



< 3

5

35 -

54

55 -

64

65+


* Value index = 100 * [revenue %] / [market household %] Key: Active subscribers


#

Value index: By product category

0 100 200 300 400 500 600






















< 3

5

35 -

54

55 -

64

65+

LOWER INCOME <$50,000 0 100 200 300 400 500 600






















< 3

5

35 -

54

55 -

64

65+


Key: Basic package

Enhanced

Premium

* Value index = 100 * [revenue %] / [market household %]

#

Value index: By product category

0 100 200 300 400 500 600






















< 3

5

35 -

54

55 -

64

65+

HIGH INCOME $100,000-$200,000 0 100 200 300 400 500 600






Nesters



< 3

5

35 -

54

55 -

64

65+


Key: Basic package

Enhanced

Premium


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Economic trends at the micro-neighborhood level

Trend analysis

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Economic Rating We want to determine which micro-neighborhoods are improving their economic wellbeing; that is which micro-neighborhoods have an “upward” trend?

The Economic Rating is a composite of the core building blocks of Economic Cohorts

It is an integer index 1 - 20

It is a demi-decile value, i.e. 5% of USA households fall into each Economic Rating category

First, we need an appropriate measure of economic health, or prosperity: “Income” alone is insufficient as a target variable

Economic Rating uses economic data only, no demographics nor geographic data elements

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Economic Rating Micro-neighborhoods with a high Economic Rating …….

Earn more Have greater spending capacity

Use more credit Are more credit worthy

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Total Income ($000)

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Spending Capacity ($000)

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Number of credit lines

650

700

750

800

850

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Vantage score

#


0

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Credit Limit ($000)

Have higher credit limits

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Credit balance ($000)

Have higher credit balances

0%

1%

2%

3%

4%

5%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Credit Risk

Have lower credit risk

** Percent of non-mortgage credit balance with severe derogatory

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0%

5%

10%

15%

20%

25%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Spend $2000+

Spend more on domestic travel

0%

5%

10%

15%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Spend $3000+

Spend more on foreign travel

0%

3%

6%

9%

12%

15%

18%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Taken a Cruise

Are more likely to take a cruise

0%

2%

4%

6%

8%

10%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Own timeshare

Are more likely to own timeshare

Source: MediaMark Research

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0%

2%

4%

6%

8%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Cellphone $200+ per month

Spend more on their cellphones

0%

2%

4%

6%

8%

10%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Own iPhone

Are more likely to have an iPhone

0%

20%

40%

60%

80%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Last car purchased : New

Are more likely to buy a new car

0%

5%

10%

15%

20%

25%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Last car purchased : $30,000+

Are more likely to buy an expensive car


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0%

2%

4%

6%

8%

10%

12%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

Bought Mutual Funds

Are financially active

0%

5%

10%

15%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Economic Rating

High-end retail clothing store

Shop in high-end retail stores

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Economic Trend We examined year-on-year changes in Economic Rating over 4 years

And also direct-measured micro-neighborhood economic data (the building blocks of Economic Cohorts)

It is an integer index 1 - 20

Economic Trend is the capacity of the micro-neighborhood to improve its Economic Rating, i.e. improve its economic prosperity

The Economic Trend model uses macro economic factors for larger geographies

It is a demi-decile value, i.e. 5% of USA households fall into each Economic Trend category

A micro-neighborhood forecast model for change in economic prosperity was built

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Simco application

0.0% 0.5% 1.0% 1.5%

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Ec

on

om

ic R

ati

ng

Customer penetration

Low penetration.

Approach: Low price, basic package (if anything)

SimCo subscriber base in terms of Economic Rating

Strategy is unclear ????

High penetration.

Approach: Premium target market; best opportunities

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Simco application

0 20 40 60 80 100

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Ec

on

om

ic T

ren

d

Average revenue

Economic Rating categories 9 and 10 only

0% 1% 2% 3% 4% 5% 6% 7%

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Ec

on

om

ic T

ren

d

Upgrade rate

* Average monthly revenue per customer * Percent package upgrade in past 12 months

SimCo subscriber base in terms of Economic Trend

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Improving performance: Economic Relativity

Economic Cohorts is built using

“unsupervised” clustering, i.e. it clusters

records that have similar characteristics

We superimpose a measure of

“performance”, e.g. customer value.

This is known as a “response surface” and

differs by client, even line-of business

Statistical technique of “bump hunting”

uses economic data in the underlying grid

to adjust the cluster solution. This is to

maximize identification of top

performance areas

Pe

rfo

rma

nc

e

e.g

. va

lue

#

Improving performance: Economic Relativity The response surface can be represented by a

contour map. Contours show “height”, i.e.

areas of top performance A

B

Cluster represented by red ellipse contains

records with good and poor performance. By

adjusting its position slightly, to remove “low”

records, we improve performance but retain

the essential characteristics of the cluster

Cluster represented by green ellipse is on a

steep slope. This is a good candidate for

improving performance. Clusters in the

“valleys” cannot be improved much.

We typically seeks an optimized solution that

targets say the top 30% of performance

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Value index: Economic Relativity

0 200 400 600 800






















< 3

5

35 -

54

55 -

64

65+

HIGH INCOME $100,000-$200,000 0 200 400 600 800






Nesters



< 3

5

35 -

54

55 -

64

65+



With Economic Relativity Key:


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Performance statistics

* The universe comprises households in the USA

Key: Random


With Economic Relativity

KEY PERFORMANCE STATISTICS

Value index

Economic Relativity

Maximum index 417 634

Average index top 5 clusters 358 548

Clusters with index >200 10 15

χ2 86.6 244.3 0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Re

spo

nse

pe

rce

nt

Contact percent

This is an ACQUISITION scenario, assuming that customers are

acquired in cluster order, starting with the best performing clusters.

'Response' equates to customer acquisition.

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Customer lifetime value and Customer equity A customer can be in one of several states, normally expressed in terms of subscription/product type and status of contract

We can calculate customer lifetime value if we have the probabilities of transitions between states and the value (profitability) of each state

Customer lifetime value is a discounted infinite sum, but there are simple formulae to do the calculations

Customer equity is the sum of lifetime values for a group of customers and/or prospects --- this emphasizes that customers are financial assets of the company

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Customer equity Multi-state model : 2 products, 3 values

States

Prospect

A high$

A medium$

A low$

B high$

B medium$

B low$

Gone

Ni1 Vi

n11 v1

n21 v2

n31 v3

n41 v4

n51 v5

n61 v6

n71 0

Ni1 Vi

n11 * v1 =r11

n21 * v2 =r21

n31 * v3 =r31

n41 * v4 =r41

n51 * v5 =r51

n61 * v6 =r61

n71 * 0

Σ = R1

Discounted R1*(1-d)

Ni2 Vi

n12

n22

n32

n42

n52

n62

n72

R2*(1-d)2

Ni3 Vi

n13 * v1 =r13

n23 * v2 =r23

n33 * v3 =r33

n43 * v4 =r43

n53 * v5 =r53

n63 * v6 =r63

n73 * 0

Σ = R3

R3*(1-d)3

Ni2 Vi

n12 * v1 =r12

n22 * v2 =r22

n32 * v3 =r32

n42 * v4 =r42

n52 * v5 =r52

n62 * v6 =r62

n72 * 0

Σ = R2

p01

p02

#

Matrix representation of Customer equity If a group contains n customers in each state, with transition probability matrix P and v being the value/profitability of each state, then customer equity for one period is n’ P v

Total customer equity is an infinite sum of such terms, with discount factors applied to future terms to get present value. Calculations use matrix algebra.

In practice, there can be a large number of customer states, with various product combinations, contract states and value tiers

The transition probability matrix P and value/profitability vector v are determined empirically from the company’s historic data

There should be different P and v for different Economic Cohorts clusters

Clusters have different transition probabilities and values!

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Total customer equity ($million) by cluster

10.6

15.5

19.1

29.7

17.8

25.5

13.4

8.0

20.4

14.0

36.9

18.7

3.6

6.9

17.7

10.0

6.0

8.0

15.2

6.9

16.9

0 50 100






















< 3

5

35 -

54

55 -

64

65+

LOWER INCOME <$50,000

16.1

34.3

17.2

14.2

19.6

46.4

63.4

21.5

54.8

26.7

100.7

57.7

1.4

35.3

20.7

31.8

32.3

13.5

7.9

3.7

22.9

0 50 100






















< 3

5

35 -

54

55 -

64

65+


#

Total customer equity ($million) by cluster

20.1

13.4

10.6

16.6

12.5

8.8

81.9

23.5

120.9

42.0

126.8

46.8

13.8

46.6

40.3

24.8

49.1

5.5

8.7

3.9

27.0

0 50 100






















< 3

5

35 -

54

55 -

64

65+

HIGH INCOME $100,000-$200,000

6.7

13.7

73.3

26.8

9.3

17.6

0.6

2.1

0 50 100






Nesters



< 3

5

35 -

54

55 -

64

65+


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Evaluating marketing strategies The monetary effect of a proposed marketing strategy can be evaluated by altering the parameters in the customer equity model

The change can be applied to selected or all clusters. For example:

An acquisition program aims to grow new customers by 5%. Increase the probabilities in the first row of the transition matrix by 5% and re-calculate. This yields the exact increase in customer equity of the program.

An up-sell program aims to get existing customers to use more services, and hence increase their spend by an average of 5%. Increase the appropriate value vector numbers by 5% and re-calculate.

A retention program aims to reduce attrition by 5%. Reduce the probabilities in the last column of the transition matrix by 5% and re-calculate.

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Conclusions A segmentation system with a solid economic base provides an excellent framework for incorporating customer demographics and economic capacity

Economic Trend identifies customers that are improving their economic position and are thus more likely to be in the market for new products and services

Economic Relativity enables the framework to be optimized, which greatly enhances oure ability to identify top performing customers and prospects

Segmentation has many benefits in its own right, principally for identifying which customers are more likely to purchase high-value products

The Customer Equity model identifies which clusters are truly valuable to the business in the long-term; it also allows us to evaluate marketing strategies before implementation

Thank You!

#

Mike Jacobs

SVP & General Manager

Segmentation Solutions

Equifax

@Equifax

Inspire 2013 - Money matters- Enhancing Database Marketing with Relevant Economic Information -...

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