1. calculated across buyers and non-buyers
2. Share-of-Wallet and Market Share
2.1. Defination
2.1.1. New node
2.1.1.1. measured on a percent basis and can be computed based on unit volume, $ volume or equivalent unit volumes (grams, ounces)
2.1.2. Share-of-Wallet
2.1.2.1. New node
3. Popular Customer Based Value Metrics
3.1. Size of Wallet
3.1.1. Information source
3.1.1.1. Primary market research
3.1.2. Evaluation
3.1.2.1. assumption
3.1.2.1.1. large wallet size indicates more revenues and profits
3.1.3. defination
3.1.3.1. Size-of-wallet ($) of customer in a category
3.2. Share of Category Requirement
3.2.1. Information source
3.2.1.1. Numerator
3.2.1.1.1. volumetric sales of the focal firm - from internal records
3.2.1.2. Denominator
3.2.1.2.1. total volumetric purchases of the focal firm’s buyer base- through market and distribution panels
3.2.1.2.2. primary market research (surveys) and extrapolated to the entire buyer base
3.2.2. Evaluation
3.2.2.1. Accepted measure of customer loyalty for FMCG categories
3.2.2.2. does not indicate if a high SCR customer will generate substantial revenues or profits
3.2.2.3. controls for the total volume of segments/individuals category requirements
3.2.3. Defination
3.2.3.1. SCR (%) of firm or brand in category
3.2.4. for categories where the variance of customer expenditures is relatively small
3.3. Share of Wallet
3.3.1. Defination
3.3.1.1. Individual Share-of-Wallet (ISW) of firm to customer (%)
3.3.2. Information source
3.3.2.1. Numerator
3.3.2.1.1. From internal records
3.3.2.2. Denominator
3.3.2.2.1. primary market research (surveys)
3.3.3. New node
3.3.4. if the variance of consumer expenditures is relatively high
3.4. Aggregate Share-of-Wallet (ASW)
3.4.1. Information source
3.4.1.1. Numerator
3.4.1.1.1. From internal records
3.4.1.2. Denominator
3.4.1.2.1. market and distribution panels
3.4.1.2.2. primary market research (surveys)
3.4.1.2.3. extrapolated to the entire buyer
3.4.2. Defination
3.4.2.1. Individual Share-of-Wallet (ISW) of firm to customer (%)
3.4.2.1.1. Aggregate Share-of-Wallet of firm (%)
3.4.3. Evaluation
3.4.3.1. Important measure of customer loyalty
3.5. Transition Matrix
3.5.1. shows that the recommended strategies for different segments differ substantively
3.5.2. makes optimal resource allocation decisions only by segmenting customers along the two dimensions simultaneously
4. Strategic Customer Based Value Metrics
4.1. Past Customer Value
4.1.1. Equation
4.1.1.1. New node
4.1.2. Transactions have to be adjusted for the time value of money
4.1.3. Limitations: Does not consider whether a customer is going to be active in the future.
4.2. Lifetime Value
4.2.1. Equation
4.3. Customer Equity
4.3.1. New node
4.4. RFM
4.4.1. Recency
4.4.2. Frequency
4.4.3. Monetary value
4.4.4. Limitations
4.4.4.1. Independently links customer response data with R, F and M values and then groups customers, belonging to specific RFM codes (1 to 5)
4.4.4.2. May not produce equal number of customers under each RFM cell since individual metrics R, F, and M are likely to be somewhat correlated
4.4.4.3. For practical purposes, it is desirable to have exactly the same number of individuals in each RFM cell
4.4.5. Breakeven Value
4.4.5.1. Breakeven - net profit from a marketing promotion equals the cost associated with conducting the promotion
4.4.5.2. Breakeven Value (BE) = unit cost price / unit net profit
4.4.5.3. BE computes the minimum response rates required in order to offset the promotional costs involved and thereby not incur any losses
4.4.6. RFM and BEI
4.4.6.1. Customers with higher RFM values tend to have higher BEI values
4.4.6.2. Customers with a lower recency value but relatively higher F and M values tend to have positive BEI values
4.4.6.3. Customer response rate drops more rapidly for the recency metric
4.4.6.4. Customer response rate for the frequency metric drops more rapidly than that for the monetary value metric
4.4.7. RFM Computation – Regression
4.4.7.1. Regression techniques to compute the relative weights of the R, F, and M metrics
4.4.7.2. Relative weights are used to compute the cumulative points of each customer
4.4.7.3. The higher the computed score, the more profitable the customer is likely to be in the future
4.4.7.4. The pre-computed weights for R, F and M, based on a test sample are used to assign RFM scores to each customer
4.4.7.5. This method is flexible and can be tailored to each business situation
5. Customer Selection Strategies
5.1. New node
5.1.1. Used for finding the best predictors of binary dependent variable
5.1.2. New node
5.1.3. Decision tree algorithms can be used to iteratively search through the data to find out which predictor best separates the two categories of a binary target variable
5.1.4. Problem with the approach: prone to over-fitting; the model developed may not perform nearly as well on a new or separate dataset
5.2. Logistic Regression
5.2.1. Method of choice when the dependent variable is binary and assumes only two discrete values
5.2.2. By inputting values for the predictor variables for each new customer – the logistic model will yield a predicted probability
5.2.3. Customers with high ‘predicted probabilities’ may be chosen to receive an offer since they seem more likely to respond positively
5.2.4. Linear and Logistic Regressions
6. Risks of e-CRM
6.1. Taking on more than can be delivered
6.2. Getting over budget & behind schedule
6.3. Poor user adoption
6.4. Expensive maintenance & support
6.5. Isolation
6.6. Garbage in-garbage out
6.7. Failure to measure success
7. marketing Metrics
7.1. Traditional
7.1.1. Market Share
7.1.2. Sales Growth
7.2. Primary Customer-based
7.2.1. Customer Acquisition
7.2.2. Customer Activity
7.3. Popular Customer-based
7.4. Strategic Customer-based
8. Traditional and Customer-Based Marketing Metrics
8.1. Traditional Marketing Metrics
8.1.1. Market share
8.1.1.1. Share of a firm’s sales relative to the sales of all firms – across all customers in the given market
8.1.1.2. Market Share (%) (Equation refer to ppt)
8.1.1.3. Evaluation
8.1.1.4. Information source
8.1.1.4.1. Numerator
8.1.1.4.2. Denominator
8.1.2. Sales Growth
8.1.2.1. Compares increase or decrease in sales volume or sales value in a given period to sales volume or value in the previous period
8.1.2.2. Sales growth in period t (%) (equation refer to ppt)
8.1.2.3. Information source
8.1.2.3.1. Numerator and denominator: from internal records
8.1.2.4. Evaluation
8.1.2.4.1. Quick indicator of current health of a firm
8.1.2.4.2. Does not give information on which customers grew or which ones did not
8.2. ошршршршр
8.2.1. Acquisition rate
8.2.1.1. Acquisition defined as first purchase or purchasing in the first predefined period
8.2.1.2. Acquisition rate (%) = 100 * Number of prospects acquired / Number of prospects targeted
8.2.1.3. Information source
8.2.1.3.1. Numerator: From internal records
8.2.1.3.2. Denominator: Prospect database and/or market research data
8.2.1.4. Evaluation
8.2.2. Acquisition cost
8.2.2.1. Acquisition cost ($) = Acquisition spending ($) / Number of prospects acquired
8.2.2.2. Precision depends on communication channel Direct mail vs. Broadcast
8.2.2.3. Information Source
8.2.2.4. Evaluation:
8.2.2.4.1. Important metric about cost efficiency of a campaign
8.2.2.4.2. Difficult to monitor on a customer by customer basis
8.2.3. Average Inter-Purchase Time (AIT)
8.2.3.1. Easy to calculate, useful for industries where customers make frequent purchases
8.2.3.2. Firm intervention might be warranted anytime customers fall considerably below their AIT
8.2.4. Retention rate
8.2.4.1. Retention rate (%) = 100 * Number of customers buying in (t) and in (t-1) / Number of customers buying in (t-1)
8.2.4.2. Defection rate (%) = 1 – Retention rate
8.2.4.3. Retention rate (%) = 1 – Defection rate
8.2.4.4. Lifetime duration = 1 / (1- Retention rate)
8.2.4.5. Retention rate (%) = 1 – (1 / Lifetime duration)
8.2.4.6. Assuming constant retention rates, number of retained customers in any arbitrary period (t+n) = Number of acquired customers in cohort * Retention rate (t+n)
8.2.5. Numerator & denominator: from internal records
8.2.6. Survival rate
8.2.6.1. Measured for cohorts of customers
8.2.6.2. Survival ratet (%) = 100 * Retention ratet * Survival ratet-1
8.2.6.3. Number of Survivors for period 1 = Survival Rate for Period 1 * number of customers at the beginning
8.2.7. P (Active)
8.2.7.1. Probability of a customer being active in time t
8.2.7.2. P(Active) = T n
8.2.7.2.1. n is the number of purchases in a given period
8.2.7.2.2. T is the time of the last purchase
8.2.7.3. Non-contractual case
8.2.8. Projecting Retention Rates
8.2.8.1. Rrt = Rrc * (1 - exp(-rt) )
8.2.8.1.1. Rrt is predicted retention rate for a given future period,
8.2.8.1.2. Rrc is the retention rate ceiling
8.2.8.1.3. r is the coefficient of retention
8.2.8.1.4. r = (1/t) * (ln(Rrc) – ln(Rrc – Rrt ))
8.2.9. Lifetime Duration
8.2.9.1. Average Lifetime duration = Customers retainedt * Number of periods / N
8.2.9.1.1. N = cohort size
8.2.9.1.2. t = time period
8.2.9.2. Differentiate between complete and incomplete information on customer
8.2.9.2.1. Complete information - customer’s first and last purchases are assumed to be known
8.2.9.2.2. Incomplete information- either the time of first purchase, or the time of the last purchase, or both are unknown
8.2.9.3. Customer relationships
8.2.9.3.1. Contractual (“lost-for-good”): Lifetime duration is time from the start of the relationship until the end of the relationship
8.2.9.3.2. Noncontractual (“always-a-share”): Whether customer is active at a given point in time
8.2.9.3.3. One-off purchases
8.2.10. Win-back rate
8.2.10.1. Part of a acquisition process
8.2.10.2. Applicable to both Contractual and non-contractual situations
8.2.10.3. Proportion of the acquired customers in a period who are customers lost in an earlier period
8.2.10.4. Indicates either a successful communication of an important change in the product offering or service or a change in the customer needs
8.3. Popular Customer Based metrics
8.3.1. Share of Category Requirement
8.3.2. Size of Wallet
8.3.3. Share of Wallet
8.3.4. Expected share of wallet
8.4. Strategic Customer Based metrics
8.4.1. Past Customer Value
8.4.2. RFM value (Recency, Frequency and Monetary)
8.4.3. Customer Lifetime Value
8.4.4. Customer Equity
9. What is CRM
9.1. acquire & retain profitable customers
9.2. long-term and sustainable customer relationships - Add value
9.3. interdisciplinary field
9.4. maximizing customer satisfaction
10. CRM Business Strategy
10.1. Customers is core business
10.2. Effectively managing CR means Success
10.3. select & manage customers for Long Term
10.4. effective sales, marketing &services processes supported by customer-centric business philosophy & culture
10.5. Simple idea - treat different customers differently
11. Classification of CRM Applications
11.1. facing
11.1.1. customers interaction
11.2. touching
11.2.1. interact with the applications
11.3. centric intelligence
11.3.1. analyze results
11.4. Online networking
11.4.1. build personal relationships