Chapter 6: Customer Analytics Part II

Overview Topics discussed: Strategic customer-based value metrics Popular customer selection strategies Techniques to evaluate alternative customer selection strategies

Customer-based Value Metrics Popular Strategic Size Of Wallet Share of Category Reqt. Share of Wallet Transition Matrix RFM Past Customer Value LTV Metrics Customer Equity

Strategic Customer-based Value Metrics RFM Value Past Customer Value Lifetime Value Metrics Customer Equity

RFM Method RFM Value Recency Frequency Monetary value 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 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) Customer Response % 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)

RFM Method: Response and Frequency 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 Response Rate % 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)

RFM Method: Response and Monetary Value 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) Response Rate % 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)

RFM Method: RFM procedure Step 1 . . . Last purchase: 1 day ago 320 days ago R=1 R=2 R=5 Group 1 Group 2 Step 2 Step 3 average response 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 Customer Database R F 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

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 45.00 2.22 17.55 690 2 112 17.45 685 3 113 17.35 681 4 114 17.25 676 5 115 17.15 672 6 121 17.05 667 7 122 16.95 663 8 123 16.85 658 9 124 16.75 654 10 125 16.65 649 11 131 16.55 645 12 132 16.45 640 13 133 16.35 636 14 134 16.25 631 15 135 16.15 627 16 141 16.05 622 17 142 15.95 618 18 143 15.85 613 19 144 15.75 609 20 145 15.65 604 21 151 15.55 600 22 152 15.45 595 23 153 15.35 591 24 154 15.25 586 25 155 15.15 582 26 211 27 212 28 213 29 214 30 215 31 221 32 222 15.05 577 33 223 14.95 573 34 224 14.85 568 35 225 14.75 564

RFM codes versus BEI 1400 1200 1000 800 BEI 600 Break-Even Index 400 111 132 153 224 245 321 342 413 434 455 531 552 RFM Cell Codes

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 Full customer base RFM Selection Average response rate 2.02% 15.25% # of responses 808 8,080 2,732.8 Average Net profit/Sale $45 Net Revenue $36,360 $363,600 $122,976 # of Mailers sent 40,000 400,000 17,920 Cost per mailer $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 4 10 50 3 9 15 Smith 6 5 25 Mags

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 3 6 John 2 Smith 12 Mags 4

Monetary Value Score Monetary Value: 10 percent of the $-value of purchase with 12 months; Maximum = 25 points; Relative weight = 3 Customer Purchase Number Value of purchase ($) Assigned Points Weighted Points   1 $40 4 12 John 2 $120 36 3 $60 6 18 Smith $400 25 75 $90 9 27 Mags $70 7 21 $80 8 24

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 Customer Purchase Number Total Weighted Points Cumulative Points   1 118 John 2 92 210 3 39 249 Smith 112 133 Mags 77 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

Spending Pattern of a Customer Jan Feb March April May Purchase Amount ($) 800 50 30 20 GC 240 15 9 6 Gross contribution (GC) = purchase amount X contribution margin 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

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 LTV Discount 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 Where: i = customer, t = time period,  = interest (or discount) rate, T = observation time horizon, = 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 Where: i = customer, t = time period,  = interest (or discount) rate, T = observation time horizon, = average retention rate at time t (it is possible to use an individual level retention probability but usually this is difficult to obtain), = gross contribution of customer i at time t, = 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) Where: i = customer, t = time period,  = interest (or discount) rate, T = observation time horizon, = average retention rate at time t, = gross contribution of customer i 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)

Customer Value Matrix Average CRV after 1 year Low High Average LTV after 1 year Affluents Champions Misers Advocates This table is adapted from Kumar, Petersen , and Leone (2007)

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 Where: i = customer, 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 15,006 Total customer equity 1,179 9 16 131 0.131 0.85 20 36 0.3 120 4 1,683 11 153 0.153 0.82 3 2,244 12 187 0.187 0.75 2 3,500 14 250 0.25 0.63 1 6,400 400 0.4 11. Total Disctd. Profits per Period to the Manufactu-rer 10. Discounted Profit per Customer per Period to Manufactu-rer Profit per Customer per period per Manu-facturer 8. Expected Number of Active Customer 7. Survival Rate 6. Actual Retention Rate 5. Mktg and Servicing Costs 4. Manufac-turer Gross Margin 3. Manufa-cturer Margin Sales per Customer Year from Acquis-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 E D A F Individual A Individual B Individual C Individual 1 Individual 2 Individual 3 Response Variable Internal Variables External

Binary Classification Trees 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

Binary Classification Trees The algorithm procedure To find out which of the explanatory variables Xi best explains the outcome Y, calculate the number of misclassifications Use the variable Xi with the lowest misclassification rate to separate the customer base 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 Total 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 1,600 4,000 130 2,870 1,730 6,870

Binary Classification Trees: Classification of Potential Hockey Equipment Buyers 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 2,870 did not buy hockey equipment Separation by purchase of scuba equipment 110 bought hockey equipment Bought: 2,800 2,690 did not buy hockey equipment 1,620 bought hockey equipment Did not buy: 5,800 4,180 did not buy hockey equipment

Binary Classification Trees: Classification of Potential Hockey Equipment Buyers Classification of hockey buyers by gender Step 2 Buyer Yes 1730 No 6870 Total 8600 Male Yes 1,600 No 4,000 Total 5,600 Female Yes 130 No 2,870 Total 3,000 Gender

Bought scuba equipment Binary Classification Trees: Classification of Potential Hockey Equipment Buyers Classification tree for hockey equipment buyers Step 3 Buyer Yes 1730 No 6870 Total 8600 Gender Male Buyer Yes 1,600 No 4,000 Total 5,600 Female Buyer Yes 130 No 2,870 Total 3,000 Bought scuba equipment Marital status Bought scuba Buyer Yes 60 No 1,140 Total 1,200 Not bought scuba Buyer Yes 1,540 No 2,860 Total 4,400 Married Buyer Yes 100 No 2,000 Total 2,100 Not married Buyer Yes 30 No 870 Total 900

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

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

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 2 4 6 8 3 5

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

Logistic Regression: 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% Predicted 1 0 Totals Observed 1 726 56 782 173 504 677 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 2 1,126 22.52 1.98 5.07 3 998 19.96 1.75 6.82 4 554 11.08 0.97 7.80 5 449 8.98 0.79 8.59 6 337 6.74 0.59 9.18 7 221 4.42 0.39 9.57 8 113 2.26 0.20 9.76 9 89 1.78 0.16 9.92 10 45 0.90 0.08 10.00 Total 50,000 5,691 11.38

Lift Performance Illustration 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

Lift Performance Illustration 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 Performance Illustration 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

Lift Performance Illustration 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

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