chapter 6: customer analytics part ii. 2 v. kumar and w. reinartz – customer relationship...

Chapter 6:

Customer Analytics Part II

2V. Kumar and W. Reinartz – Customer Relationship Management

Overview

Topics discussed:

Strategic customer-based value metrics

Popular customer selection strategies

Techniques to evaluate alternative customer selection strategies


Customer-based Value Metrics

Customer based Value

Metrics

Popular Strategic

RFMPast

Customer Value

LTV Metrics

SizeOf

Wallet

Transition Matrix

Share ofCategory

Reqt.

Share of Wallet

Customer Equity


Strategic Customer-based Value Metrics

RFM Value

Past Customer Value

Lifetime Value Metrics

Customer Equity


RFM Method

• Elapsed time since a customer last placed an order with the company

Recency

• Number of times a customer orders from the company in a certain defined period

Frequency

• Amount that a customer spends on an average transaction

Monetary value

RFM Value

Technique to evaluate customer behavior and customer value

Often used in practice

Tracks customer behavior over time in a state-space


RFM Method

Example

Customer base: 400,000 customers

Sample size: 40,000 customers

Firm’s marketing mailer campaign: $150 discount coupon

Response rate: 808 customers (2.02%)


RFM Method

Recency coding

Test group of 40,000 customers is sorted in descending order based on the criterion of

‘most recent purchase date’

The earliest purchasers are listed on the top and the oldest are listed at the bottom

The sorted data is divided into five equally sized groups (20% in each group)

The top-most group is assigned a recency code of 1, the next group

a code of 2 until the bottom-most group is assigned a code of 5

Analysis of customer response data shows that the mailer campaign got the highest

response from those customers grouped in recency code 1 followed by those in code 2

etc.


RFM Method: Response and Recency

Graph depicts the distribution of relative frequencies of customer groups assigned to recency codes 1 to 5

Highest response rate (4.5%) for the campaign was from customers in the test group who belonged to the highest recency quintile (recency code =1)

4.50%

2.80%

1.50%

1.05%

0.25%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

1 2 3 4 5

Recency Code (1 - 5)

Cu

sto

mer

Res

po

nse

%


RFM Method: Response and Frequency

Graph depicts the response rate of each of the frequency-based sorted quintiles

The highest response rate (2.45%) for the campaign was from customers in the test group belonging to the highest frequency quintile (frequency code =1)

2.45% 2.22%

2.08%

1.67% 1.68%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

1 2 3 4 5

Frequency Code 1-5

Res

po

nse

Rat

e %


RFM Method: Response and Monetary Value

Customer data is sorted, grouped and coded with a value from 1-5

The highest response rate (2.35%) for the campaign was from those customers in the test group who belonged to the highest monetary value quintile (monetary value code =1)

2.35%

2.05% 1.95% 1.90% 1.85%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

1 2 3 4 5

Monetary Value Code (1-5)

Res

po

nse

Rat

e %


RFM Method: RFM procedure

Step 1

. . .

. . .

Last purchase: 1 day ago

Last purchase: 320 days ago

. . .

R=1

R=1

R=1

R=2

R=2

R=2

R=5

R=5

R=5

Group 1

Group 2

Step 2 Step 3

averageresponse rate



Group 3


RFM Method: Limitations

RFM method 1 independently links customer response data with R, F and M values and

then groups customers belonging to specific RFM codes

May not produce equal number of customers under each RFM cell since individual metrics

R, F, and M are likely to be somewhat correlated

For example, a person spending above average (high M) is also likely to spend more

frequently (high F)

For practical purposes, it is desirable to have exactly the same number of individuals in

each RFM cell


RFM Method: Cell Sorting Technique

A list of 40,000 test group of customers is first sorted for recency and then grouped into 5

groups of 8,000 customers each

The 8,000 customers in each group are sorted based on frequency and divided into five

equal groups of 1,600 customers each - at the end of this stage, there will be RF codes

starting from 11 to 55 with each group including 1,600 customers

In the last stage, each of the RF groups is further sorted based on monetary value and

divided into five equal groups of 320 customers each

RFM codes starting from 111 to 555 each including 320 customers

Considering each RFM code as a cell, there will be 125 cells (5 recency divisions * 5

frequency divisions * 5 monetary value divisions = 125 RFM Codes)


RFM Method: RFM Cell Sorting

1

2

3

4

5

41

42

43

44

45

41

42

43

44

45

Customer Database

R

F

11

12

13

14

15

11

12

13

14

15

M

Sorted Once Sorted Five Times per R Quintile

Sorted Twenty-Five Times per R Quintile

131

132

133

134

135

441

442

443

444

445

1

2

3

4

5

131

132

133

134

135


RFM Method: Breakeven Value (BE)

Breakeven = net profit from a marketing promotion equals the cost associated with

conducting the promotion

Breakeven Value (BE) = unit cost price / unit net profit

BE computes the minimum response rates required in order to offset the promotional costs

involved and thereby not incur any losses

Example

Mailing $150 discount coupons

The cost per mailing piece is $1.00

The net profit (after all costs) per used coupon is $45,

Breakeven Value (BE) = $1.00/$45 = 0.0222 or 2.22%


RFM Method: Breakeven Index

Breakeven Index (BEI) = ((Actual response rate – BE) / BE)*100

Example

If the actual response rate of a particular RFM cell was 3.5%

BE is 2.22%,

The BEI = ((3.5% - 2.22%)/2.22%) * 100 = 57.66

Positive BEI value some profit was made from the group of customers

0 BEI value the transactions just broke even

Negative BEI value the transactions resulted in a loss


RFM Method: Combining RFM codes, breakeven codes, breakeven index

Cell # RFM codes Cost per mail $ Net profit per sale ($)

Breakeven (%) Actual response (%)

Breakeven index

1 111 1 45.00 2.22 17.55 6902 112 1 45.00 2.22 17.45 6853 113 1 45.00 2.22 17.35 6814 114 1 45.00 2.22 17.25 6765 115 1 45.00 2.22 17.15 6726 121 1 45.00 2.22 17.05 6677 122 1 45.00 2.22 16.95 6638 123 1 45.00 2.22 16.85 6589 124 1 45.00 2.22 16.75 65410 125 1 45.00 2.22 16.65 64911 131 1 45.00 2.22 16.55 64512 132 1 45.00 2.22 16.45 64013 133 1 45.00 2.22 16.35 63614 134 1 45.00 2.22 16.25 63115 135 1 45.00 2.22 16.15 62716 141 1 45.00 2.22 16.05 62217 142 1 45.00 2.22 15.95 61818 143 1 45.00 2.22 15.85 61319 144 1 45.00 2.22 15.75 60920 145 1 45.00 2.22 15.65 60421 151 1 45.00 2.22 15.55 60022 152 1 45.00 2.22 15.45 59523 153 1 45.00 2.22 15.35 59124 154 1 45.00 2.22 15.25 58625 155 1 45.00 2.22 15.15 58226 211 1 45.00 2.22 15.65 60427 212 1 45.00 2.22 15.55 60028 213 1 45.00 2.22 15.45 59529 214 1 45.00 2.22 15.35 59130 215 1 45.00 2.22 15.25 58631 221 1 45.00 2.22 15.15 58232 222 1 45.00 2.22 15.05 57733 223 1 45.00 2.22 14.95 57334 224 1 45.00 2.22 14.85 56835 225 1 45.00 2.22 14.75 564


RFM codes versus BEI

0

200

400

600

800

1000

1200

1400

111

132

153

224

245

321

342

413

434

455

531

552

RFM Cell Codes

BE

I Break-Even Index


RFM and BEI

Customers with higher RFM values tend to have higher BEI values

Customers with a lower recency value but relatively highF and M values tend to have

positive BEI values

Customer response rate drops more rapidly for the recency metric

Customer response rate for the frequency metric drops more rapidly than the one for the

monetary value metric


Comparison of profits for targeting campaign test

Test Full customer base RFM Selection

Average response rate 2.02% 2.02% 15.25%

# of responses 808 8,080 2,732.8

Average Net profit/Sale $45 $45 $45

Net Revenue $36,360 $363,600 $122,976

# of Mailers sent 40,000 400,000 17,920

Cost per mailer $1.00 $1.00 $1.00

Mailing cost $40,000.00 $400,000.00 $17,920.00

Profits (-$3,640.00) ($36,400.00) $105,056.00


Relative Importance of R, F, and M

Regression techniques to compute the relative weights of the R, F, and M metrics

Relative weights are used to compute the cumulative points of each customer

The pre-computed weights for R, F and M, based on a test sample are used to assign RFM

scores to each customer

The higher the computed score, the more likely the customer will be profitable in future

This method is flexible and can be tailored to each business situation


Recency Score

20 if within past 2 months, 10 if within past 4 months, 05 if within past 6 months, 03 if within

past 9 months, 01 if within past 12 months, relative weight = 5

Customer Purchase

Number

Recency

(Month)

Assigned

Points

Weighted

Points

1 2 20 100

John 2 4 10 50

3 9 3 15

Smith 1 6 5 25

1 2 20 100

Mags 2 4 10 50

3 6 5 25

4 9 3 15


Frequency Score

Points for Frequency: 3 points for each purchase within 12 months; Maximum = 15 points;

Relative weight = 2

Customer Purchase Number

Frequency Assigned Points

Weighted Points

1 1 3 6

John 2 1 3 6

3 1 3 6

Smith 1 2 6 12

1 1 3 6

Mags 2 1 3 6

3 2 6 12

4 1 3 6


Monetary Value Score

Monetary Value: 10 percent of the $-value of purchase with 12 months; Maximum = 25

points; Relative weight = 3


Value of purchase ($)

Assigned Points

Weighted Points

1 $40 4 12

John 2 $120 12 36

3 $60 6 18

Smith 1 $400 25 75

1 $90 9 27

Mags 2 $70 7 21

3 $80 8 24

4 $40 4 12


Cumulative scores: 249 for John, 112 for Smith and 308 for Mags, indicates a potential

preference for Mags John seems to be a good prospect, but mailing to Smith might be a misdirected marketing

effort


Total Weighted Points

Cumulative Points

1 118 118 John 2 92 210

3 39 249

Smith 1 112 112

1 133 133

Mags 2 77 210

3 61 271

4 37 308


Past Customer Value

Computation of Customer Profitability (PCV)

PCV of customer i

Where: i = number representing the customer,

t = time index,

= applicable discount rate (for example 1.25% per month),

t0 = current time period,

T = number of time periods prior to current period that should be

considered, GCin = gross contribution of transaction of customer in period t

Since products / services are bought at different points in time during the customer’s lifetime,

all transactions have to be adjusted for the time value of money

Limitations

Equation does not consider whether a customer is going to be active in the future and it

does not incorporate the expected cost of maintaining the customer in the future

T

t

tnttiGC

0

)1(*0


Spending Pattern of a Customer

PCVi = 6*(1+0.0125)o +9*(1+0.0125)1 +15*(1+0.0125)2 +15*(1+0.0125)3+ 240*(1+0.0125)4

= 302.01486

The customer is worth $302.01 expressed in net present value in May dollars

Comparing the PCV of a set of customers leads to a prioritization of directing future

marketing efforts

Jan Feb March April May

Purchase Amount ($) 800 50 50 30 20

GC 240 15 15 9 6

Gross contribution (GC) = purchase amount X contribution margin


Lifetime Value Metrics

Multi-period evaluation of a customer’s value to the firm

Lifetime Value (LTV)

Recurring

Revenues

Recurring costs

Contribution margin

Lifetime of a customer

Lifetime Profit

Acquisition cost

LTVDiscount rate

Lifetime of a customer

Discount rate


Basic LTV Model

Where: i = customer,

t = time period,

= interest (or discount) rate,

= gross contribution of customer i at time t,

T = observation time horizon,

= lifetime value of an individual customer i at net present value time

t=0


Basic LTV Model

LTV is a measure of a single customer’s worth to the firm

Used for pedagogical and conceptual purposes

Information source

CM and T from managerial judgment or from actual purchase data.

The interest rate, a function of a firm’s cost of capital, can be obtained from financial

accounting

Evaluation

Typically based on past customer behavior and may have limited diagnostic value for

future decision-making Caution: If the time unit is different from a yearly basis, the interest rate needs to be adjusted accordingly. !


LTV with Splitted Revenues and Costs


t = time period,



= sales value to customer i at time t,

= direct costs of products by customer i at time t,

= marketing costs directed at customer i at time t,

= lifetime value of an individual customer i at net present value

time t=0


LTV with Splitted Revenues and Costs

The cost element of this example is broken down into direct product-related costs and

marketing costs

Depending on data availability, it can be enhanced by including service-related cost,

delivery cost, or other relevant cost elements


LTV Including Customer Retention Probabilities


t = time period,



= average retention rate at time t (it is possible to use an individual

level retention probability but usually this is difficult to obtain),


= costs of acquiring customer i (acquisition costs)


LTV Including Customer Retention Probabilities

In this equation the term is actually the survival rate

The retention rate is constant over time and thus the expression can be simplified using the

identity:


LTV with Constant Retention Rate and Gross Contribution

Assuming that T and that the retention rate and the contribution margin (CM) do not vary

over time:

The margin multiplier:

How long is the Lifetime Duration?

For all practical purposes, the lifetime duration is a longer-term duration used

managerially

It is important to make an educated judgment regarding a sensible duration horizon in

the context of making decisions


LTV with Constant Retention Rate and Gross Contribution

Incorporating externalities in the LTV

The value a customer provides to a firm does not only consist of the revenue stream

that results from purchases of goods and services

Product rating websites, weblogs and the passing on of personal opinions about a

product or brand co-contribute substantially to the lifetime value of a customer

These activities are subsumed under the term word-of mouth (WOM)


Measuring and Incorporating Word-of-Mouth (WOM)


t = time period,



= average retention rate at time t,


= number of new acquisition at time t due to referrals customer i,

= average acquisition cost savings per customer gained through

referral of customer i at time t,

= costs of acquiring customer i (acquisition costs)


This table is adapted from Kumar, Petersen , and Leone (2007)

Customer Value Matrix

Average CRV after 1 year

Low High

Average LTV after 1 year

High Affluents Champions

Low Misers Advocates


LTV

Alternative ways to account for externalities

The value of a customer’s referrals can be separated from the LTV, for example by

calculating a separate customer referral value (CRV) for each customer

A joined evaluation of both metrics helps the management to select and determine

how to develop its customers

Information source

Information on sales, direct cost, and marketing cost come from internal company

records

Many firms install activity-based-costing (ABC) schemes to arrive at appropriate

allocations of customer and process-specific costs

Evaluation

LTV (or CLV) is a forward looking metric that is appropriate for long-term decision

making

It is a flexible measure that has to be adapted to the specific business context of an

industry


Customer Equity

Customer equity (CE) = Sum of the LTV of all the customers of a firm


I = all customers of a firm (or specified customer cohort or segment),

LTVi = lifetime value of customer i

Indicator of how much the firm is worth at a particular point in time as a result of the firm’s

customer management efforts

Can be seen as a link to the shareholder value of a firm


Customer Equity

Customer Equity Share (CES):

Where: CEj = customer equity of brand j,

j = focal brand,

K = all brands a firm offers

Information source

Basically the same information as for the LTV is required

Evaluation

The CE represents the value of the customer base to a company

The metric can be seen as an indicator for the shareholder value of a firm


Customer Equity Calculation Example

15,006Total customer

equity

1,1799161310.1310.8520360.31204

1,68311161530.1530.8220360.31203

2,24412161870.1870.7520360.31202

3,50014162500.250.6320360.31201

6,40016164000.40.420360.31200

11. TotalDisctd.Profits perPeriod to theManufactu-rer

10. Discount

edProfit perCustome

r per Period to Manufact

u-rer

9Profit perCustomer per period per Manu-facturer

8. Expecte

dNumber

ofActive

Customer

7. Surviv

alRate

6.ActualRetention Rate

5. Mktg and

Servicing

Costs

4. Manufac-turerGross Margin

3.Manuf

a-cturerMargin

2Sales perCustomer

1Year fromAcquis-ition


Popular Customer Selection Strategies

Techniques

Profiling

Binary Classification Trees

Logistic regression

Techniques to Evaluate Customer Selection Strategies

Missclassification Rate

Lift Analysis

CRM at Work: Tesco

Example where a company combines analytical skills, judicious judgment, knowledge

about consumer behavior and careful targeting of customers


Profiling

The intuitive approach for customer selection is based on the assumption that the most

profitable customers share common characteristics

Based on this assumption the company should try to target customers with similar profiles

to the currently most profitable ones

Disadvantage

Only customers that are similar to existing ones are considered

Profitable customer segments that do not match the current customer base might be

missed


Profiling: Using Profiling for New Customer Acquisition

Transactions, demographics, lifestyle Demographics, lifestyle

Current

Customers

B C

Potential

Customers

E D

A

F

Individual A

Individual B

Individual C

Individual 1

Individual 2

Individual 3

Response Variable

Internal Variables

ExternalVariables



Used to identify the best predictors of a 0/1 or binary dependent variable

Useful when there is a large set of potential predictors for a model

Classification tree algorithms can be used to iteratively search the data to find out which

predictor best separates the two categories of a binary (or more generally categorical)

target variable



The algorithm procedure

1. To find out which of the explanatory variables Xi best explains the outcome Y, calculate

the number of misclassifications

2. Use the variable Xi with the lowest misclassification rate to separate the customer base

3. This process can be repeated for each sub segment until the misclassification rate

drops below a tolerable threshold or all of the predictors have been applied to the

model

Evaluation Problem with the approach: prone to over-fitting The model developed may not perform nearly as well on a new or separate dataset


Binary Classification Trees: Classification of Potential Hockey Equipment Buyers

Male

Bought hockey

Did not buy hockey

Female

Bought hockey

Did not buy hockey

Total

Bought hockey

Did not buy hockey

Bought scuba

60 1,140 50 1,550 110 2,690

Did not buy scuba

1,540 2,860 80 1,320 1,620 4,180

Total 1,600 4,000 130 2,870 1,730 6,870



Possible separations of potential hockey equipment buyers

Step 1

Separation by gender

1,600 bought hockey equipment

Male: 5,600

4,000 did not buy hockey equipment

130 bought hockey equipment

Female: 3,000


Separation by purchase of scuba

equipment 110 bought hockey equipment

Bought: 2,800


1,620 bought hockey equipment

Did not buy: 5,800




Classification of hockey buyers by gender

Step 2

Buyer

Yes 1730No 6870

Total 8600

Male

Buyer

Yes 1,600

No

4,000

Total

5,600

Female

Buyer

Yes

130

No

2,870

Total

3,000

Gender



Classification tree for hockey equipment buyers Step 3

Male Buyer Yes 1,600 No 4,000Total 5,600

Bought scubaBuyer Yes 60No 1,140

Total 1,200

Not bought scubaBuyer Yes 1,540No 2,860

Total 4,400

Female Buyer Yes 130 No 2,870Total 3,000

MarriedBuyer Yes 100No 2,000

Total 2,100

Not marriedBuyer Yes 30No 870

Total 900

Buyer Yes 1730No 6870

Total 8600

Gender

Bought scuba equipment Marital status


Logistic Regression

Method of choice when the dependent variable is binary and assumes only two discrete

values

By inputting values for the predictor variables for each new customer – the logistic model

will yield a predicted probability

Customers with high ‘predicted probabilities’ may be chosen to receive an offer since they

seem more likely to respond positively


Logistic Regression: Examples

Example 1: Home ownership

Home ownership as a function of income can be modeled whereby ownership is

delineated by a 1 and non-ownership a 0

The predicted value based on the model is interpreted as the probability that the

individual is a homeowner

With a positive correlation between increasing income and increasing probability of

ownership, one can expect results as

Predicted probability of ownership is .22 for a person with an income of $35,000

Predicted probability of .95 for a person with a $250,000 income


Logistic Regression: Examples

Example 2: Credit Card Offering

Dependent Variable - whether the customer signed up for a gold card offer

Predictor Variables - other bank services the customer used plus financial and

demographic customer information

By inputting values for the predictor variables for each new customer, the logistic model

will yield a predicted probability

Customers with high ‘predicted probabilities’ may be chosen to receive the offer since

they seem more likely to respond positively


Linear and Logistic Regression

In linear regression, the effect of one unit change in the independent variable on the

dependent variable is assumed to be a constant represented by the slope of a straight line

For logistic regression the effect of a one-unit increase in the predictor variable varies along

an s-shaped curve . At the extremes, a one-unit change has very little effect, but in the

middle a one unit change has a fairly large effect


Logistic Regression: Formula

Where: y= dependent variable,

x= predictor variable,

= constant (which is estimated by linear regression and often

called intercerpt),

= the effect of x on y (also estimated by linear regression),

= error term


Logistic Regression: Transformation Steps

Step 1: If p represents the probability of an event occurring, take the ratio

Since p is a positive quantity less than 1, the range of this expression is 0 to infinity

Step 2: Take the logarithm of this ratio:

This transformation allows the range of values for this expression to lie between

negative infinity and positive infinity

p

p

1

pp

1log


Logistic Regression: Transformation Steps

Step 3: The value can be considered as the dependent variable and a linear

relationship of this value with predictor variables in the form z = can be written.

and coefficients can be estimated

Step 4: In order to obtain the predicted probability p, a back transformation is to be done:

Since = z =

Then calculate the probability p of an event occurring, the variable of interest, as

pp

1log


Logistic Regression: Interpretation of Coefficients

Interpret the coefficients carefully

If the logit regression coefficient β = 2.303

When the independent variable x increases one unit, the odds that the dependent

variable equals 1 increase by a factor of 10, keeping all other variables constant.

Sample of contract extension and sales Contract extension Number of sales calls

1 21 40 60 21 41 80 30 00 20 50 01 21 81 4


Logistic Regression: Interpretation of Coefficients Odds of Logistic Regression Example

It expects that sales calls impact the probability of contract extension. Thus, it estimates the

model:

Prob (contract extension)

The resulting estimates are: α= -1.37; β= 0.39

Odds of logistic regression example

Sales calls = 0 Sales calls = 1

Odds (exp(α+β *sales calls))

0.253 0.375

Probability of contract extension

0.202 0.273

Difference in probabilities 0.071

)(1

1callssales

e


Logistic Regression: Evaluation

Evaluation

For logistic regression the effect of a one-unit increase in the predicor variable varies

along an s-shaped curve

This means that at the extremes, a one-unit change has a very little effect, but in the

center a one-unit change has a fairly large effect


Techniques to Evaluate Alternative Customer Selection Strategies

A critical step to decide which model to use for selecting and targeting potential customers

is to evaluate alternative selection strategies

When several alternative models are available for customer selection, we need to compare

their relative predition quality

Divide the data into a training (calibration) dataset (2/3 of the data) and a holdout (test)

sample (1/3 of the data)

The models are estimated based on the training dataset

Select the model that generalizes best from the training to the data

Techniques

Misclassification Rate

Lift Analysis


Misclassification Rate

Estimate the different models on two thirds of the available data

Calculate the misclassificaion rate on the remaining third to check for model performance

Example

The number of mispredictions is the sum of the off-diagonal entries (56+173=229)

Misclassification Rate: 229/ 1,459 = 15,7%

Predicted1 0 Totals

Observed 1 726 56 782

0 173 504 677

Totals 899 560 1,459


LIFT Analysis

Lift Charts

Lifts charts show how much better the current model performs against the results

expected if no model was used (base model)

Can be used to track a model’s performance over time, or to compare a model’s

performance on different samples

Lift% = (Response rate for each decile) / (Overall response rate) *100

Cumulative lift% = (Cumulative response rate) / (Overall response rate) *100

Cumulative response rate = cumulative number of buyers / Number of customers per

decile


Lift Performance Illustration

Lift and Cumulative Lift

Number of decile

Number of customers

Number of buyers

Response rate %

Lift Cumulative lift

1 5,000 1,759 35.18 3.09 3.09

2 5,000 1,126 22.52 1.98 5.07

3 5,000 998 19.96 1.75 6.82

4 5,000 554 11.08 0.97 7.80

5 5,000 449 8.98 0.79 8.59

6 5,000 337 6.74 0.59 9.18

7 5,000 221 4.42 0.39 9.57

8 5,000 113 2.26 0.20 9.76

9 5,000 89 1.78 0.16 9.92

10 5,000 45 0.90 0.08 10.00

Total 50,000 5,691 11.38



The Decile analysis distributes customers into ten equal size groups

For a model that performs well, customers in the first decile exhibit the highest response

rate

Decile Analysis

35.18%

22.52%19.96%

11.08%8.98%

6.74%4.42%

2.26% 1.78% 0.90%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

1 2 3 4 5 6 7 8 9 10

Deciles

Re

sp

on

se

Ra

te



Lifts that exceed 1 indicate better than average performance

Less than 1 indicate a poorer than average performance

For the top decile the lift is 3.09; indicates that by targeting only these customers are

expected to yield 3.09 times the number of buyers found by randomly mailing the same

number of customers

Lift Analysis

3.09

1.981.75

0.970.79

0.590.39

0.20 0.16 0.08

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1 2 3 4 5 6 7 8 9 10

Deciles

Lift



The cumulative lifts for the model reveal what proportion of responders we can expect to

gain from targeting a specific percentage of customers using the model

Choosing the top 30 % of the customers from the top three deciles will obtain 68% of

the total responders

Cumulative Lift Analysis

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

Decile

Cu

mu

lati

ve

Lif

t



Logistic models tend to provide the best lift performance

The past customer value approach provides the next best performance

The traditional RFM approach exhibits the poorest performance

Comparison of Models

0

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0 1 2 3 4 5 6 7 8 9 10

Deciles

Cu

mu

lati

ve L

ift

Logistic

Past Customer Value

RFM

No Model


Minicase: Akzo Nobel, NV- Differentiating Customer Service According to Customer Value

One of the world's largest chemical manufacturers and paint makers

The polymer division, which serves exclusively the B-to-B market, established a “tiered

customer service policy” in the early 2000’s

Company developed a thorough list of all possible service activities that is currently offered

To formalize customer service activities, the company implemented a customer scorecard

mechanism to measure and document contribution margins per individual customer

Service allocation, differentiated as:

Services are free for all types of customers

Services are subject to negotiation for lower level customer groups

Services are subject to fees for lower level customers

Services are not available for the least valuable set of customers


Summary

The higher the computed RFM score, the more profitable the customer is expected to be in

the future

The PCV is another important metric, in which the value of a customer is determined

based on the total contribution (toward profits) provided by the customer in the past after

adjusting for the time value of money

The LTV reflects the long-term economic value of a customer

The sum of the LTV of all the customers of a firm represents the customer equity (CE)

Firms employ different customer selection strategies to target the right customers

Lift analysis, decile analysis and cumulative lift analysis are various techniques firms use to

evaluate alternative selection strategies

Logistic Regression is superior to past customer value and RFM techniques

chapter 6: customer analytics part ii. 2 v. kumar and w. reinartz – customer relationship...

Documents

customer orders

customer analytics

highest response rate

recency graph

recency codes

discount coupon response

frequency graph

statespace slide