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Economics of CustomerRelationship Management

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Quantitative Benefits of CRM UsingRevenue Enhancement Metrics

Acquisition/prospect increase 27-45 percent

Expense per convert decrease 30-60percent

Renewal rate increase 5-15 percent Cross-sell/up-sell increase 3-25 percent

Share of wallet increase 3-25 percent

Churn decrease 30-80 percent

Campaign cycle time decrease 50-70percent

Campaign conversion increase 20-50 percent

Win-back increase 25-33 percent

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The Market Share Fallacy

Increasing market share should not be a companysgoal. Rather, increasing share of the right kinds ofcustomers should be the goal.

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Retail Banks Have Realized the FollowingBenefits from CRM Benefits

Increase in average products sold per customerover one year from 4.6 to 6.2

3-5 percent decrease in administrative costs

200 percent return on technology investmentthrough cost reduction over one year

83 percent decrease in average customer inforetrieval time

15 percent increase in product revenue in one year

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A Customer Focus Can Aid Retention

Annual Defection Rates

Newspaper subscriptions 66 percent

Long distance telephone 30 percent

Clothing catalogues 25 percent Internet service providers 22 percent

Griffen and Lowenstein 2001

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Which Companies Benefit Most fromCRM?

Companies serving large numbers of customersthrough complex and frequent interactions: Communications companies

Retail banks

Insurance companies

Healthcare organizations Utilities

Companies with a steep skew

Companies in lost for good markets

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Introduction to Customer Based

Marketing Metrics

Traditional Marketing Metrics

Customer based Marketing Metrics

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Marketing Metrics

Marketing Metrics

TraditionalPrimary

Customer-based

Market Share Sales GrowthCustomer

Acquisition

Customer

Activity

Popular

Customer-based

Strategic

Customer-based

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Traditional and Customer BasedMarketing Metrics

Strategic Customer Based metrics

Past Customer Value

RFM valueCustomer Lifetime Value

Customer Equity

Popular Customer Based metrics

Size of Wallet

Share of Wallet

Primary Customer Based metrics

Acquisition rate

Acquisition cost

Retention rate

Survival rate

P (Active)

Lifetime DurationWin-back rate


Market share

Sales Growth

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Market share

Sales Growth

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Market Share (MS)

Share of a firms sales relative to the sales of all firms

across all customers in the given market Measured in percentage

Calculated either on a monetary or volumetric basis

Market Share (%) of a firm (j) in a category =

Where j = firm, S = sales, Sj= sum of sales across all firms in the market

Adequate indicator o f m arketing per formance?

J

j jj

SS1

100

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Sales Growth

Compares increase or decrease in sales volume or sales

value in a given period to sales volume or value in the previous period

Measured in percentage

Indicates degree of improvement in sales performance between two or more timeperiods

Sales growth in period t (%) =

where: j = firm, Sjt = change in sales in period t from period t-1, Sjt-1= sales in

period t-1

Adequate indicator o f market ing per formance?

1100 jtjt SS

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Primary Customer Based Metrics

Customer Acquisition Measurements

Acquisition rate

Acquisition cost

Customer Activity Measurements

Average interpurchase time (AIT)

Retention rate

Defection rate

Survival rate

P (Active)

Lifetime Duration

Win-back rate

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Acquisition Rate

Acquisition defined as first purchase or purchasing in the first predefined

period

Acquisition rate (%) = 100*Number of prospects acquired / Number of

prospects targeted

Denotes average probability of acquiring a customer from a population

Always calculated for a group of customers

Typically computed on a campaign-by-campaign basis

Adequate indicator o f marketing per formance?

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Acquisition Cost

Measured in monetary terms

Acquisition cost (Rs) = Acquisition spending (Rs) / Number of prospects

acquired

Precise values for companies targeting prospects through direct mail

Evaluation:

Diff icu l t to mon i tor on a customer by cu stomer bas is

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Minicase: American Airlines

Leading scheduled air carrier running both passenger and

cargo services

Uses Database Marketing for efficient customer acquisition

First to implement a frequent flyer program (AAdvantage)

Purpose:

To induce current members to spend more of their flight dollars with

American Airlines

To efficiently target new prospects and convert patrons of competing

airlines

Strategy: Cooperation with the credit card company American Express

To identify attractive customers who are not American Airlines flyers

Provide attractive offers to these prospects for inducing them to try

American Airlines

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IMC Builds Brands

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Customer Activity Measurement

Objectives

When customer is a customer? Marketing interventions

Active living relationship.

Non-purchase interaction contributes to relationship.

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Average Inter-purchase Time (AIT)

Average Inter-purchase Time of a customer= 1 / Number of purchase incidences from the first purchase

till the current time period

Measured in time periods

Information from sales records

Important for industries where customers buy on a frequent basis

Information source

Sales records

Evaluation:

Easy to calculate, useful for industries where customers make frequent purchases

Firm intervention might be warranted anytime customers fall considerably below their AIT

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Retention and Defection

Retention rate (%) = 100* Number of customers in cohortbuying in (t)| buying

in (t-1) / Number of customers in cohort buying in (t-1)

Avg. retention rate (%) = [1(1/Avg. lifetime duration)]

Avg. Defection rate (%) = 1Avg. Retention rate

Avg. retention rate (%) = 1Avg. defection rate Avg. lifetime duration = [1/ (1- Avg. retention ratet)

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Retention and Defection-Example If the average customer lifetime duration of a group of customers is 4 years.

Average retention rateis

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The effect for a cohortof customers over timeout of 100

customers who start in year 1, about 32 are left at the end of year 4

Customers starting at the beginning of year 1: 100

Customers remaining at the end of year 1: 75 (0.75*100)

Customers remaining at the end of year 2: 56.25 (0.75*75)Customers remaining at the end of year 3: 42.18 (0.75*56.25)

Customers remaining at the end of year 4: 31.64 (0.75*42.18)

Assuming constant retention rates, the number of retained

customersat the end of year

The defection rateis 1-0.75 = 0.25 or 25%

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Variation in Defection rate with respect toCustomer Tenure

0

5

10

15

20

25

30

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

Customer tenure (periods)

#ofcustom

ersdefecting

Plotting entire series of customers that defect each period, shows variation (orheterogeneity) around the average lifetime duration of 4 years.

Ch i C t Lif ti D ti

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Change in Customer Lifetime Durationwith Retention Rate

0

4

8

12

16

20

50% 55% 60% 65% 70% 75% 80% 85% 90% 95%

Retention rate

AvergaeCustomerLifetime

(periods)

Note: increasing the marginal retention rate is likely to be increasingly expensive

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CRM at Work: Amazon

One of the leaders in implementing customer relationship management

programs on the web

Helped drive both customer acquisition and retention

In 1999 Amazon acquired 11 million new customers, nearly tripling its

number of customers from 1998

Greatest success in customer retention: Repeat customers during the year

accounted for 71% of all sales

Success attributed to attempt to learn about customers and their needs andthen using this information to offer value-added features to them

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How is retention different from loyalty?Is retention only about buying?

Retention measured on a period by period basis.

Loyalty has theoretical meaning.

Loyalty has an emotional and psychological dimension.

Retention may be simply due to inertia and convenience.

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Survival Rate

Measured for cohortsof customers

Provides a summary measure of how many customers survived between the

start of the formation of a cohort and any point in time afterwards

Survival ratet(%) = 100*Retention ratet* Survival ratet-1

Number of Survivors for period 1 = Survival Rate for Period 1 * number ofcustomers at the beginning

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Survival Rate Computation-Example

Number of Customers starting at the beginning of year 1: 1,000

Retention rate Survival rate Survivors

Period 1: 0.55 0.55 550

Period 2: 0.62 0.341 341

Period 3: 0.68 0.231 231

Period 4: 0.73 0.169 169

Number of Survivors for period 1 = 0.55 * 1000 = 550

Survival rate for period 2 = Retention rate of period 2 * Survival Rate of

Period 1. Therefore, Survival rate for period 2 = 0.62 * 0.55 = 0.341

(=34.1%)

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Projecting Retention Rates

To forecast non-linear retention rates,

Rrt= Rrc* (1-exp(-rt))

where: Rrtis predicted retention rate for a given future period,

Rrcis the retention rate ceiling

r is the coefficient of retention

r = (1/t) * (ln(Rrc)ln(RrcRrt))

Actual Retention Pattern of a

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Actual Retention Pattern of aDirect Marketing Firm

1.Period

since

acquisition

2.Actual

retention rate3.Predicted

retention rate4.Defection

rate%5.Survival

Rate6.Expected

Number of

active

customers

7.Number of

Active

periods

1 32.0% 68.0% 32.0% 2400 24002 49.1% 50.9% 15.7% 1178 23573 63.2% 36.8% 9.9% 745 22344 69.0% 31.0% 6.9% 514 20565 72.6% 27.4% 5.0% 373 1865

6 76.7% 23.3% 3.8% 286 17177 77.9% 22.1% 3.0% 223 15608 78.5% 21.5% 2.3% 175 14009 79.0% 21.0% 1.8% 138 124410 80.0% 20.0% 1.5% 111 110611 79.7% 20.3% 1.2% 88 96912 79.8% 20.2% 0.9% 70 844

13 79.9% 20.1% 0.7% 56 73014 79.9% 20.1% 0.6% 45 628

15 80.0% 20.0% 0.5% 36 538

Cohort of 7500 customers at the outset, maximum retention rate is 0.80 and thecoefficient of retention r is 0.5; after period 10, the company retains approximately 80% ofcustomer base

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Customer Lifetime Duration

Average Lifetime duration = Customers retainedt* Number of periods / N

Where: N = cohort size, t= time period

Differentiate between complete and incomplete information on customer

Complete information - customers first and last purchases are assumed to

be known

Incomplete information- either the time of first purchase, or the time of the

last purchase, or both are unknown

T

t 1

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Customer relationships

Contractual Lifetime duration (lost-for-good )is time from the start of the

relationship until the end of the relationship (e.g.: mobile phonecontract)

Noncontractual (always-a-share): ) Whether customer is active at a

given point in time (e.g.: department store purchase)

Customer Lifetime Duration (contd.)

Customer Lifetime Duration when the

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Customer Lifetime Duration when the

Information is Incomplete

Buyer 1

Buyer 2

Buyer 3

Buyer 4

Observation window

Buyer 1: complete information

Buyer 2 : left-censored

Buyer 3: right-censored

Buyer 4: left-and-right-censored

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Estimation of P(Active)-Example

Customer 1

Customer 2

Observation period

Month 1 Month 12Month 8 Month 18

Anx indicates that a purchase was made by a customer in that month

To compute the P(Active) of each of the two customers in the 12th month of activity

For Customer 1: T = (8/12) = 0.6667 and n = 4

P(Active)1= (0.6667)4 = 0.197

And for Customer 2: T = (8/12) = 0.6667 and n = 2

P(Active)2= (0.6667)2 = 0.444

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P (Active)

Probability of a customer being active in time t

P(Active)= (T)n

Where, nis the number of purchases in the observation period.

Tis the time the last purchase expressed as a fraction of the observationperiod..

N is the time elapsed between acquisition and the period for whichP(Active) needs to be determined.

Non-contractual case

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It assumes that when a customer terminates a relationship, he/shedoes not come back to the firm.

This approach called lost-for-good is questionable because itsystematically underestimates CLV .

To overcome this, researchers use always-a-share approach,which takes into account the possibility of a customer returning to

the supplier after a temporary dormancy in a relationship.

In this case, predicting the frequency of a customers previouspurchase is a better way of projecting future customer activity.

Any drawback of using P(Active) to predictcustomers future activity?

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Customer Based Value Metrics

Customer

based

value

metrics

Popular Strategic

RFM

Past

Customer

Value

LTV

Metrics

Size

Of

Wallet

Transition

Matrix

Share of

Category

Reqt.

Share of

Wallet

Customer

Equity

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Size-of-Wallet

Size-of-wallet ($) of customer in a category = Sj

Where: Sj = sales to the focal customer by the firmj

j= firm, = summation of value of sales made by all the Jfirms that

sell a category of products to the focal customer

Information source:Primary market research

Evaluation:

Critical measure for customer-centric organizations based on the assumption

that a large wallet size indicates more revenues and profits

Example:

A consumer might spend an average of $400 every month on groceriesacross the supermarkets she shops at. Her size-of-wallet is $400

J

j 1

J

j 1

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Share-of-Wallet (SW)

Individual Share-of-Wallet

Individual Share-of-Wallet of firm to customer (%) = Sj / Sj

Where: S = sales to the focal customer, j = firm, = summation of value of sales made by all

the Jfirms that sell a category of products to a buyer

Information source:

Numerator: From internal records

Denominator: From primary market research (surveys), administered to individual customers,

often collected for a representative sample and then extrapolated to the entire buyer base

Evaluation:

Important measure of customer loyalty; however, SW is unable to provide a clear indication of

future revenues and profits that can be expected from a customer

If a consumer spends Rs 3000/- on groceries monthly, and Rs 1000/- of her purchases are with Big

Bazaar, then BBs share of wallet of that consumer is 30%

J

j 1

J

j 1

Segmenting Customers Along

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Segmenting Customers AlongShare of Wallet and Size of Wallet

The matrix shows that the recommended strategies for different segments differ

substantively. The firm makes optimal resource allocation decisions only by

segmenting customers along the two dimensions simultaneously

High

Share-of-wallet

Low

Size-of-wallet

Hold on

Do nothingTarget for

additional selling

Maintain and guard

Small Large

Sh f W ll t d M k t Sh (MS)

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Share of Wallet and Market Share (MS)

Difference between share-of-wallet and market share:MS is calculated across buyers and non-buyers whereas SW is calculated only

among buyers

MS is measured on a percent basis and can be computed based on unit volume, $

volume or equivalent unit volumes (grams, ounces)

Example:

BINGO has 5,000 customers with an average expense at BINGO of $150 per

month (=share-of-wallet * size of wallet)

The total grocery sales in BINGOstrade area are $5,000,000 per month

BINGOsmarket share is (5,000 * $150) / $5,000,000 = 15%

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Strategic Customer Based Value Metrics

RFM

Past Customer Value

LTV Metrics

Customer Equity

RFM

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RFM

Recency, Frequency and Monetary Value-applied on historical data

Recency -how long it has been since a customer last placed an orderwith the company

Frequency-how often a customer orders from the company in a

certain defined period

Monetary value- the amount that a customer spends on an averagetransaction

Tracks customer behavior over time in a state-space

Only on historical data , not on prospect data.

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Computation of RFM

Method 1: Sorting customer data based on RFM, grouping and

analyzing results

RFM M h d 1

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RFM Method 1

Example:

Customer base: 400,000 customers

Sample size: 40,000 customers

Firms marketing mailer campaign: $150 discount coupon

Response rate: 808 customers (2.02%)

Recency coding: Analysis Test group of 40,000 customers is sorted in a 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 equal groups (20% in each group)

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

a code of 2 and so on, 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 customers grouped in recency code 1 followed by code 2 etc

R d R

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Response and Recency

Recency Vs. Response

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)

CustomerResponse%

Response %

Graph depicts the distribution of percentage of those customers who responded fellwithin the recencycode grouping of 1 through 5

Highest response rate (4.5%) for the campaign was from customers in the test

group who fell in the highest recency quintile (recency code =1)

R d F

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Response and Frequency

Frequency Vs. Response

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

ResponseRate%

Response %

Graph depicts the distribution of what % of those customers who responded fell

within the frequencycode grouping of 1 through 5

The highest response rate (2.45%) for the campaign was from customers in the

test group who fell in the highest frequencyquintile (frequency code =1)

R d M t V l

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Response and Monetary Value

Monetary Value Vs. Response

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)

Resp

onseRate%

Response %

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

The highest response rate (2.35%) for the campaign was from those customers in the

test group who fell in the highest monetary valuequintile (monetary value code =1).

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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 more (high M) is also likely, on average,

to buy more frequently (high F)

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

individuals in each RFM cell

RFM C ll S ti

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RFM Cell Sorting

Example:

List of 40,000 test group customers is first sorted for Recency and grouped into 5

equal groups of 8000 each

The 8000 customers in each group is then sorted based on Frequency and divided

into five equal groups of 1600 each- at the end of this stage, there will be RF codes

starting from 11 through 55 with each group having 1600 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 through 555 each having 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 Cell Sorting (contd )

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RFM Cell Sorting (contd.)

1

2

3

4

5

41

42

43

44

45

CustomerDatabase

R

F

11

12

13

14

15

M

Sorted Once Sorted five timesper R quintile

Sorted twenty fivetimes per R quintile

131

132

133

134

135

441

442

443

444

445

Helps target valuable customers with high chances of purchasing

Lifetime Value metrics

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e e a ue e cs(Net Present Value models)

Multi-period evaluation of a customers value to the firm

Recurring

Revenues

Recurring

costs

Contributionmargin

Lifetime of acustomer Lifetime Profit

Acquisitioncost

LTV

Discountrate

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Calculation of Lifetime Value: Simple Definition

where LTV = lifetime value of an individual customer in $, CM = contribution margin,

= interest rate, t = time unit, = summation of contribution margins across timeperiods

LTV is a measure of a single customers 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 firms 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

tT

tt

CMLTV

1 1

1

Customer Equity

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Sum of the lifetime value of all the customers of a firm

Customer Equity,

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

result of the firms customer management efforts

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

Customer Equity Share, CESj= CEj / k,

where, CE = customer equity , j = focal brand, k = all brands

Customer Equity

tT

t

it

I

i

CMCE

11 1

1

k

CE

C t E it C l l ti E l

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15006Total

customer

equity

11799161310.1310.8520360.31204

168311161530.1530.8220360.31203

224412161870.1870.7520360.31202

350014162500.250.6320360.31201

640016164000.40.420360.31200

11.TotalDisctd.

Profits perPeriod totheManufactu-rer

10.DiscountedProfit per

Customerper Period

toManufactu-

rer

9Profit perCustomer

per periodper Manu-facturer

8.ExpectedNumber

ofActive

Customer

7.Surviva

l

Rate

6.Actual

Retention

Rate

5.Mktgand

Servicing Costs

4.Manufac

-turer

GrossMargin

3.Manufa-cturer

Margin

2Sales perCustomer

1Yearfrom

Acquis-ition

Customer Equity Calculation: Example

Summary

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Summary

Firms use different surrogate measures of customer value to prioritize their customers

and to differentially invest in them

Firms can use information about size of wallet and share of wallet together for optimal

allocation of resources

Transition matrix provides the probability that a customer will purchase a particular

brand if what brand has been purchased the last time is known

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

be, in the future

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

Summary

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Summary

In the absence of individual customer data, companies used to rely on

traditional marketing metrics like market share and sales growth

Acquisition measurement metrics measure the customer level success of

marketing efforts to acquire new customers

Customer activity metrics track customer activities after the acquisition stage

Lifetime duration is a very important metric in the calculation of the customer

lifetime value and is different in contractual and non-contractual situations

Documents

9356_CRM 2 Metrics