9356_CRM 2 Metrics
Transcript of 9356_CRM 2 Metrics
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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
Traditional Marketing Metrics
Market share
Sales Growth
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Traditional Marketing Metrics
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