Managing Touchpoints and the Customer Journey PDF

mar keti ng.uni - gr a z.at PS Managing Touchpoints and the Customer Journey WS 2024/25 1  mar keti ng.uni - gr a z.at CUSTOMER VALUE MANAGEMENT Source: 2 Kum...

mar keti ng.uni - gr a z.at PS Managing Touchpoints and the Customer Journey WS 2024/25 1  mar keti ng.uni - gr a z.at CUSTOMER VALUE MANAGEMENT Source: 2 Kumar, V./Reinartz W. (2018). 2  mar keti ng.uni - gr a z.at Link Between CRM and Customer Value ⚫ Customer Value: ⚫ The economic value of the customer relationship to the firm ⚫ CRM: ⚫ Practice of analyzing and utilizing marketing databases and leveraging communication technologies to determine corporate practices and methods that will maximize the lifetime value of each individual customer to the firm ⚫ Adoption of CRM with customer value at its core strategy helps us define CRM from a customer value perspective Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at From Value for Customers to Value from Customers: The Satisfaction-Loyalty-Profit Chain ⚫ Satisfaction-Loyalty-Profit Chain ⚫ Increased customer satisfaction will lead to greater customer retention, which is often used as a proxy for customer loyalty, which then is expected to lead to greater profitability Product Performance Customer Retention / Revenue / Service Performance Satisfaction Loyalty Profit Employee Performance Value for the customer Value from the customer Source: “Strengthening the satisfaction-profit chain”, Adapted from: Eugene W Anderson, Vikas Mittal. Journal of Service Research, Nov 2000. Vol 3, Iss.2, p. 107. Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Direct Link between Customer Satisfaction and Profits ⚫ Direct link suggests, that as customers experience greater satisfaction with a firm’s offering, profits rise ⚫ Positive correlation between customer satisfaction and ROA ⚫ Improving customer satisfaction comes at a cost and once the cost of enhancing satisfaction is factored in, offering “excessive satisfaction” doesn’t pay ⚫ Marginal gains in satisfaction decrease, while the marginal expenses to achieve the growth in satisfaction increase ⚫ There is an optimum satisfaction level for any firm, beyond which increasing satisfaction does not pay Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Link between Satisfaction and Retention (1) Source: “Strengthening the satisfaction-profit chain”, Eugene W Anderson, Vikas Mittal. Journal of Service Research, Nov 2000. Vol 3, Iss.2, p 114 Source: Kumar, V./Reinartz W. (2018). 6  mar keti ng.uni - gr a z.at Link between Satisfaction and Retention (2) ⚫ Link between satisfaction and retention is asymmetric: ⚫ Dissatisfaction has a greater impact on retention than satisfaction ⚫ Even if the level of satisfaction is high, retention is not guaranteed ⚫ If customers are dissatisfied, other products become more enticing ⚫ The link is nonlinear in that the impact of satisfaction on retention is greater at the extremes ⚫ The flat part of the curve in the middle has also been called the “zone of indifference” ⚫ Factors like the aggressiveness of competition, degree of switching cost, and the level of perceived risk influence the shape of the curve and the position of the elbows Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Link between Satisfaction and Retention (3) Source: “Why satisfied customers defect”, Jones, Thomas O, Sasser, W Earl Jr. Harvard Business Review. Boston: Nov/Dec 1995. Vol. 73, Iss. 6 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Link between Loyalty and Profits ⚫ Reichheld’s hypotheses ⚫ Long term customers spend more per period over time ⚫ Cost less to serve per period over time ⚫ Have greater propensity to generate word-of-mouth ⚫ Pay a premium price when compared to that paid by short-term customers ⚫ Does not hold true in a non-contractual relationship ⚫ Revenue stream must be balanced by the cost of constantly sustaining the relationship and by fending off competitive attacks ⚫ Efforts at increasing customer satisfaction and retention not only consume a firm’s resources but are subject to diminishing returns Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Duration-Profitability Association ⚫ Reinartz and Kumar: Across the different firms, ⚫ There is a segment of customers that is loyal but not very profitable (due to excessive resource allocation) ⚫ There is a segment that generates very high profits although it has only a short tenure ⚫ Since these short-term customers can be very profitable, it is clear that loyalty is not the only path to profitability Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Duration-Profitability Association (2) High Lifetime Profit Low Low High Loyalty ⚫ Overall trend shows a direct correlation between loyalty and profitability ⚫ Outliers on the graph who generate high profits while not having high loyalty will outperform those customers who have a high level of loyalty but who are not very profitable Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at CUSTOMER ANALYTICS AND THE CONCEPT OF CUSTOMER LIFETIME VALUE Source: 12 Kumar, V./Reinartz W. (2018). 12  mar keti ng.uni - gr a z.at Customer Analytics ⚫ Customer value management rests on the idea of allocating resources differently to different customers. The basis of this differential resource allocation is the economic value of the customer to the firm. ⚫ Before one can start to manage customers, one must have a thorough understanding of how to compute the value contribution each customer makes to a firm. ⚫ It is necessary to define measures or metrics of marketing activities and their outcomes. ⚫ Traditional Marketing Metrics ⚫ Customer Based Metrics ⚫ Customer Acquisition Metrics ⚫ Customer Activity Metrics ⚫ Popular Customer-based Value Metrics Source: Kumar, V./Reinartz W. (2018). 13  mar keti ng.uni - gr a z.at Traditional and Customer Based Marketing Metrics Traditional Marketing Metrics Customer Acquisition Metrics Market Share Acquisition Rate Sales Growth Acquisition Cost Customer Activity Metrics Popular Customer-based Value Metrics Average Inter-Purchase Time Size of Wallet Retention & Defection Rate Share of Category Requirement Survival Rate Share of Wallet Lifetime Duration (Transition Matrix) P (Active) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Traditional Marketing Metrics ⚫ Market Share ⚫ Sales Growth Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Market Share (MS) Market Share (MS) is defined as the share of a firm’s sales relative to the sales of all firms—across all customers in the given market. MS is an aggregate measure across customers. It can be calculated either on a monetary or a volumetric basis. ⚫ Market Share (%) of a firm (j) in a category = 100 𝑥 [𝑆𝑗/ σ𝐽𝑗=1 𝑆𝑗] ⚫ Where: j = firm, S = sales,  Sj = sum of sales across all firms in the market ⚫ Information source ⚫ Numerator: Sales of the local firm available from internal records ⚫ Denominator: Category sales from market research reports or competitive intelligence ⚫ Evaluation ⚫ Common measure of marketing performance, readily computed ⚫ Does not provide any information about how sales are distributed across customers Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Sales Growth Sales growth of a brand, product, or a firm is a simple measure that compares the increase or decrease in sales volume or sales value in a given period to sales volume or value in the previous period. It is measured in percent. It indicates the degree of improvement in the sales performance between two or more time periods and acts as a flag for the management. A negative sales growth or sales growth lower than the rest of the market is normally a cause for concern. ⚫ Sales growth in period t (%) = 100 x [∆ Sjt / Sjt-1] ⚫ Where: j = firm, ∆Sjt = change in sales in period t from period t-1, Sjt-1 = sales in period t-1 ⚫ Information source ⚫ Numerator and denominator: from internal records ⚫ Evaluation ⚫ Quick indicator of current health of a firm ⚫ Does not provide any information about changes in customer size Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer Acquisition Metrics ⚫ Group of primary customer based metric = customer acquisition metric ⚫ Concepts: ⚫ Acquisition Rate ⚫ Acquisition Cost Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Acquisition Rate The acquisition rate is the proportion of prospects converted to customers. It is calculated by dividing the fraction of prospects acquired by the total number of prospects targeted. ⚫ Acquisition = 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 ⚫ Information source ⚫ Numerator: From internal records ⚫ Denominator: Prospect database and / or market research data ⚫ Evaluation ⚫ Important metric ⚫ Gives a first indication of the success of a marketing campaign ⚫ But cannot be considered in isolation Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Acquisition Cost Acquisition Costs (AC) is defined as the acquisition campaign spending divided by the number of acquired prospects. AC is measured in monetary terms. ⚫ Acquisition cost ($) = Acquisition spending ($) / Number of prospects acquired ⚫ Precise values for companies targeting prospects through direct mail ⚫ Less precise for broadcasted communication ⚫ Information source ⚫ Numerator: from internal records ⚫ Denominator: from internal records ⚫ Evaluation ⚫ Difficult to monitor on a customer by customer basis Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer Activity Metrics ⚫ Average Inter-Purchase Time ⚫ Retention & Defection Rate ⚫ Survival Rate ⚫ Lifetime Duration ⚫ P(Active) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer Activity Measurement ⚫ Objectives ⚫ Managing marketing interventions ⚫ Aligning resource allocation with actual customer behavior ⚫ Providing key input for customer valuation models such as the net-present value (NPV) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Average Inter-Purchase Time (AIT) Average Inter-Purchase Time (AIT) is the average time elapsing between purchases. It is measured in terms of specific time periods (days, weeks, months, etc.). It is computed by taking the inverse of the number of purchase incidences per time period. ⚫ 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 ⚫ 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 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Retention and Defection Rate Retention rate in period t (Rrt) is defined as the average likelihood that a customer purchases from the focal firm in a period (t), given that this customer has also purchased in the period before (t − 1). The defection rate is defined as the average likelihood that a customer defects from the focal firm in a period (t), given that the customer was purchasing up to period (t − 1). ⚫ Rrt (%) = 100*Number of customers in cohort buying in (t) | customer in (t-1) / Number of customers in cohort buying in (t- 1) ⚫ Rrt (%) = 100 – Avg. defection rate (%) ⚫ Avg. lifetime duration = [1 / (1- Rr)] ⚫ Number of retained customers in period (t+n) = number of acquired customers in cohort at time (t)*Rr ⚫ Avg. defection rate in t (%) = 100 – Rrt ⚫ Where: Rrt = Retention rate in period t, ⚫ n = Number of elapsed periods ⚫ Assuming constant retention rates, number of retained customers in any arbitrary period (t+n) = Number of acquired customers in cohort * Retention rate (t+n) ⚫ Given a retention rate of 75%, variation in defection rate with respect to customer tenure results in an average lifetime duration of four years Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Retention and Defection-Example ⚫ If the average customer lifetime duration of a group of customers is 4 years, the average retention rate is 1*(1/4) = 0.75 or 75% per year, i.e., on an average, 75% of the customers remain customers in the next period ⚫ The effect for a cohort of customers over time – out of 100 customers starting in year 1, about 32 are left at the end of the 4th year 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 customers at the end of year 4 is 100*0.75 4 = 31.64. (Number of acquired customers in cohort * Retention rate (t+n)) The defection rate is 100-75% = 25% Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Variation in Defection Rate with Respect to Customer Tenure ⚫ Plotting the entire series of customers that defect each period demonstrates variation (or heterogeneity) around the average lifetime duration of 4 years Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Projecting Retention Rates ⚫ To forecast non-linear retention rates, Rrt = Rc*(1- e-rt) ⚫ Where: Rrt = predicted retention rate for a given future period, Rc = retention (rate) ceiling, r = coefficient of retention ⚫ r = (1/t)*(ln(Rc) – ln(Rc – Rrt )) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Actual and Predicted Retention Rate for a Credit Card Company ⚫ Rc = 0.95 means that managers believe the maximum attainable retention rate is 95% ⚫ The known retention rate in period 9 is 80% while it is 82% in period 10 ⚫ The parameter r for period 9 is (1/9)*(ln(0.95)-ln(0.95-0.8)) = 0.205. The r for period 10 is (1/10)*(ln(0.95)-ln(0.95-0.82)) = 0.198 -> for both periods r approximates the value 0.2 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Survival Rate The survival rate (SR) indicates the proportion of customers who have survived (or, in other words, continued to remain as a customer) until a period t from the beginning of observing these customers. SR is measured for cohorts of customers, wherein a cohort refers to a group of customers acquired within a specified period of time. SR gives a summary measure of how many customers survived between the start of the formation of a cohort and any point in time afterward. ⚫ SRt (%) = 100 * Rrt * SRt-1 ⚫ Where: SR = Survival Rate ⚫ Number of survivors for period 1 = survival rate for period 1 * number of customers at the beginning Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at 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 ⚫ Survival rate for period 2 = 0.62*0.55 = 0.341 (=34.1%) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Duration (1) ⚫ Knowing for how long a customer remains a customer is a key ingredient in the calculation of the customer lifetime value. It has implications for churn management, customer replacement, and management of lifetime duration drivers. ⚫ Since the retention rate usually changes over time (e.g., through customer self- selection) such a calculation would be misleading. We need to weigh in the number of survived periods. ⚫ Average lifetime duration = σ𝑇𝑡=1(𝑡 ∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑖𝑛 𝑡) / 𝑁 ⚫ Where: N = cohort size, t = time period, T = time horizon ⚫ Limitations: information is not always complete making the calculation more challenging ⚫ Differentiate between complete and incomplete information on customer ⚫ Complete information = customer’s 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 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer Lifetime Duration when the Information is Incomplete Observation Window ⚫ Buyer 1: complete information ⚫ Buyer 2 : left-censored ⚫ Buyer 3: right-censored ⚫ Buyer 4: left-and-right-censored Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Duration (2) ⚫ When talking about the concept of a customer’s lifetime duration, not all relationships are equal. We must take the type of product, which is subject to exchange into account. ⚫ Customer relationships ⚫ Contractual Relationships are those where buyers engage in a specific commitment (e.g., subscription, mobile phone contract). Lifetime duration spans from the beginning until the end of the relationship (e.g.: mobile phone contract). Also labeled as (“lost-for-good”) because a company loses the entire customer relationship once a client terminates the contract. ⚫ Noncontractual relationships are those where buyers do not commit in any way, either in duration or level of usage (e.g., purchasing at a department store). Also labeled as “always-a-share”. ⚫ One-off purchases: There is relationship between the exchange partners since it involves a once in a lifetime buy. Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Popular Customer-Based Value ⚫ Size of Wallet ⚫ Share of Category Equipment ⚫ Share of Wallet ⚫ Transition Matrix Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer-Based Value Metrics Customer-based Value Metrics Popular Strategic Size Share of Past Lifetime Share of Transition Customer Of Category RFM Customer Value Wallet Matrix Equity Wallet Reqt. Value Metrics Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Size of Wallet Size of wallet is the amount of a buyer’s total spending in a given category—or, stated differently, the category sales of all firms to that customer. The size of wallet is measured in monetary terms. 𝐽 ⚫ Size of Wallet ($) of customer i in a category = σ𝑗=1 𝑆𝑖𝑗 ⚫ Where: i = a particular customer, j = firm, J = all firms offering products in the considered category, Sj = sales value (in category) to customer i by firm j, j = 1,…,J ⚫ 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 spends on average $400 on groceries in different supermarkets per month. Thus his/her size of wallet is $400. Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at (Aggregated) Share of Category Requirement (SCR) (1) Share of Category Requirement (SCR) is defined as the proportion of category volume accounted for by a brand or focal firm within its base of buyers. This metric is often computed as an aggregate level metric, when individual purchase data are unavailable. ⚫ Aggregate SCR is calculated as follows: 𝐽 ⚫ aSCR (%) of firm (or brand) j0 in a category = σ𝐼𝑖=1 𝑉𝑖𝑗/ σ𝐼𝑖=1 σ𝑗=1 𝑉𝑖𝑗 * 100 ⚫ Where: j0 = focal firm or brand, i = customer, I = all customers buying in focal category, J = all firms or brands available in focal category, Vij = purchase volume of customer i from firm (or brand) j Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at (Aggregated) Share of Category Requirement (SCR) (2) ⚫ Example ⚫ Calculation of aSCR – purchases during a 3-month period ⚫ Brand SAMA has a MS of 33% (i.e., 8 purchases out of a total of 24) and an aSCR of 42.1% (i.e., 8 purchases out of 19, made by its two buyers) ⚫ This shows that even though SAMA‘s MS is already substantial, its aSCR is even higher Brand SAMA Brand SOMO Brand SUMU Total Customer 1 2 8 0 10 Customer 2 6 0 3 9 Customer 3 0 4 1 5 Total 8 12 4 24 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Individual Share of Category Requirement (SCR) (1) At the individual level, when such data are available, iSCR is computed by dividing the volume of sales (V) of the focal firm to a particular customer by the total category volume she buys. The metric thus indicates how much of the category requirements the focal firm satisfies of an individual customer. 𝐽 ⚫ iSCR (%) of customer i0 that a firm x (or brand) j0 satisfies = Viojo / σ𝑗=1 𝑉𝑖0𝑗 ∗ 100 ⚫ Where: j0 = focal firm or brand, i0 = focal customer, J = all firms or brands available in focal category, Vij = purchase volume of customer i from firm (or brand) j Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Individual Share of Category Requirement (SCR) (2) ⚫ Example: Individual SCR-ratios ⚫ Customer 3 has the highest iSCR ⚫ PEAR Computers should identify high iSCR customers such as customer 3, and target more of its marketing efforts (mailers, advertisements etc.) towards such customers and their respective requirements ⚫ Also, customer 3’s size of wallet (column A), is the largest A B B/A Total requirement of Total number of notebook Share of category notebook Computers purchased requirement for computers per from PEAR Computers PEAR computers per customer in 2010 per customer in 2010 customer in 2010 Customer 1 100 20.20 Customer 2 1,000 200.20 Customer 3 2,000 500.25 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Individual Share of Category Requirement (SCR) (3) ⚫ Information source ⚫ Numerator: volumetric sales of the focal firm from internal records ⚫ Denominator: total volumetric purchases of the focal firm’s buyer base – through market and distribution panels, or primary market research (surveys) and extrapolated to the entire buyer base ⚫ Evaluation ⚫ Accepted measure of customer loyalty for FMCG categories ⚫ SCR controls for the total volume of segments / individuals category requirements ⚫ Does not indicate if a high iSCR customer will generate substantial revenues or profits ⚫ Can only be achieved by knowing the customer’s size of wallet Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Share of Wallet (SW) (1) Share of Wallet (SW) is defined as the proportion of category value accounted for by a focal brand or a focal firm within its base of buyers. It can be measured at the individual customer level or at an aggregate level (e.g., segment level or entire customer base). ⚫ Individual Share of Wallet (iSW) is defined as the proportion of category value accounted for by a focal brand or a focal firm for a buyer from all brands she purchases in that category. It indicates the degree to which a customer satisfies her needs in the category with a focal brand or firm. It is computed by dividing the value of sales (S) of the focal firm (j0) to a buyer i in a category by the size of wallet of the same customer in a predefined time period. 𝐽 ⚫ iSW (%) of firm j0 to customer i = Sij0 / σ𝑗=1 𝑆𝑖𝑗 * 100 ⚫ Where: j = firm, i = customer, Sij = sales of firm j to customer I, J = all firms who offer the category under consideration ⚫ Example: If a consumer spends $400 monthly on groceries, $300 thereof are spend at the supermarket “BINGO” Consequently “BINGO”’s iSW for this particular consumer amounts 75% Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Share of Wallet (SW) (2) ⚫ Aggregate share of wallet (aSW) is defined as the proportion of category value accounted for by a focal brand or a focal firm within its entire base of buyers. It indicates the degree to which the customers of a focal firm satisfy their needs on average, in a category with a focal firm. 𝐽 ⚫ aSW (%) of firm j0 = σ𝐼𝑖=1 𝑆𝑖𝑗/ σ𝐼𝑖=1 σ𝑗=1 𝑆𝑖𝑗 * 100 ⚫ Where: j = firm, i = customer, Sij = sales of firm j to customer I, J = all firms who offer the category under consideration, I = all customers ⚫ Example. The aSW is “BINGO”’s sales (value) in period t ($ 750,000) divided by the total grocery expenditures of “BINGO”’s customers in the same period ($1,250,000) ⚫ 750,000/1,250,000 = 60% Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Share of Wallet (SW) (3) ⚫ Information source ⚫ Numerator: From internal records ⚫ Denominator: Through market and distribution panels, or primary market research (surveys) and extrapolated to the entire buyer base ⚫ Evaluation ⚫ Important measure of customer loyalty ⚫ The iSW sheds light on how important the firm is for an individual customer in terms of his expenditures in the category ⚫ The aSW indicates how important (value wise) a specific firm is for its customer base in terms of their expenditures in the category Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Applications of SCR and SW ⚫ SCR – for categories where the variance of customer expenditures is relatively small ⚫ SW – if the variance of consumer expenditures is relatively high ⚫ Share of wallet and size of wallet simultaneously – with same share of wallet, different attractiveness as customers ⚫ Example: Share-of-Wallet Size-of-Wallet Absolute expenses with firm Buyer 1 50% $400 $200 Buyer 2 50% $50 $25 ⚫ Absolute attractiveness of Buyer 1 is eight times higher even though the SW is the same as for Buyer 2 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Segmenting Customers Along Share of Wallet and Size of Wallet ⚫ The matrix shows that the recommended strategies for various segments differ substantively ⚫ The firm makes optimal resource allocation decisions only by segmenting customers along the two dimensions simultaneously High Maintain and guard Hold on Share of Wallet Target for Low Do nothing additional selling Small Size of Wallet Large Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Share of Wallet and Market Share (MS) (1) ⚫ Difference of share of wallet to market share: ⚫ MS is calculated across buyers and non-buyers, whereas SW is calculated only among actual buyers. ⚫ The MS of a firm is the SW across all its customers in the category divided by the sales across all firms in the category in period t. ⚫ MS of firm j0 (%) = = σ𝐼𝑖=1(iSW of customer i to firm j0*Size of Wallet of customer i) / σ𝐼𝑖=1 σ𝐽𝑗=1 𝑆𝑖𝑗 ⚫ Where: j = firm, i = customer, Sij = sales of firm j to customer i, J = all firms who offer the category under consideration, I = all customers Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Share of Wallet and Market Share (MS) (2) ⚫ Example ⚫ The supermarket “Publix” has 5,000 customers with an average expense of $150 at “Publix” per month (SW*size of wallet) ⚫ The total grocery sales in “Publix”’s trade area are $5,000,000 per month “Publix”’s market share is (5,000 * $150) / $5,000,000 = 15% ⚫ Implication: although “Publix” has an overall low MS, it has a high SW for those consumers buying “Publix” ⚫ “Publix” is a niche player with very loyal customers Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Transition Matrix (1) ⚫ All of the previously discussed metrics describe only the current state and make no prediction about the future development. ⚫ A simple idea to forecast SCR or SW is to use a transition matrix. ⚫ A transition matrix is a convenient way to characterize a customer’s likelihood to buy over time or a brand’s likelihood to be bought. The assumption is that a customer moves over her lifetime through various stages of activity. Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Transition Matrix (2) ⚫ Example ⚫ The top row indicates the movements for customers who are currently brand A buyers; 70% of them will buy brand A next time, 20% will buy brand B, and 10% will buy brand C. ⚫ The diagonals (in bold) are customer-retention probabilities computed by the company. ⚫ However, we see that consumers can switch back and forth from brands. The probability that a consumer of Brand A will switch to Brand B and then come back to Brand A in the next two purchase occasions is 20*10% = 2%. ⚫ If, on average a customer purchases twice per period, the two purchases could be composed as: AA, AB, AC, BA, BB, BC, CA, CB, or CC ⚫ It is possible to compute the probability of each of these outcomes if the brand that the customer bought last is known Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at 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 detect 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 ⚫ 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 the optimal allocation of resources ⚫ Transition matrix measures the probability for a customer to purchase a particular brand providing the previous purchased brand is known Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer-Based Value Metrics Customer-based Value Metrics Popular Strategic Size Share of Past Lifetime Share of Transition Customer Of Category RFM Customer Value Wallet Matrix Equity Wallet Reqt. Value Metrics Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Strategic Customer-based Value Metrics ⚫ RFM Method ⚫ Past Customer Value ⚫ Lifetime Value Metrics ⚫ Customer Equity Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at RFM Method ⚫ Technique to evaluate customer behavior and customer value ⚫ Often used in practice ⚫ Tracks customer behavior over time in a state-space Elapsed time since a customer last placed Recency an order with the company Number of times a customer orders from Frequency the company in a certain defined period RFM Value Amount that a customer spends on an Monetary value average transaction Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Past Customer Value Past customer value (PCV) is a metric which assumes the results of past transactions are an indicator of the customer’s future contributions. The value of a customer is determined based on the total contribution (toward profits) provided by the customer in the past. This modeling technique assumes that the past performance of the customer indicates the future level of profitability. Since d at different points in time during the customer’s lifetime, all products or services are bought transactions must be adjusted for the time value of money. ⚫ Computation of Customer Profitability (PCV) Where ⚫ PCV of customer i i = number representing the customer, T t = time index, applicable discount rate (for example 1.25% per month), =  GCi (t0 −t )n * (1 +  ) t t0 = current time period, T = number of time periods prior to current period that should be t =0 considered,GCin = gross contribution of transaction of customer in period t ⚫ 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 Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Value Metrics (1) ⚫ The general term used to describe the long-term economic value of a customer is lifetime value (LTV), also referred to as the customer lifetime value (CLV). In very simple terms, it is a multi period evaluation of a customer’s value to the firm in net present value. Recurring Revenues Contribution margin Recurring costs Lifetime Lifetimeofofa customer a customer Lifetime Profit LTV Discount Discount Acquisition rate rate cost Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Lifetime Value Metrics (2) ⚫ Basic LTV Model: The lifetime value of an individual customer i is the sum of her discounted gross contribution over the respective observation horizon T. ⚫ LTV with Splitted Revenues and Costs: Break down the gross contribution into its constituting elements ⚫ LTV Including Customer Retention Probabilities: Fact that customers tend to remain in the relationship only with a certain probability approximated by the average retention rate Rr is considered. Also, the AC is now subtracted from the customer’s value. ⚫ LTV with Constant Retention Rate and Gross Contribution ⚫ Measuring and Incorporating WOM: WOM has a direct effect on new customer acquisitions through reducing (or in case of negative WOM increasing) the AC Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Extending the Concept of Customer Value (1) ⚫ Customer Lifetime Value (CLV) ⚫ „the present value of future profits generated from a customer over his or her life of business with the firm“ (Kumar/Pansari 2017) ⚫ Loyal customers usually – but not necessarily – exhibit a higher CLV ⚫ Gupta, Lehmann & Stuart confirm positive link between CLV and firm value ⚫ Kumar at. all show that optimizing resource allocation based on CLV can increase revenues by a factor of 10 ⚫ Customer Influencer Value (CIV) ⚫ well established in research and management in the form of word of mouth ⚫ stems from customers´ intrinsic motivation → free for companies ⚫ companies can profit from high CIV, as well as the CIV can be zero or negative ⚫ measuring CIV has become much easier, but it is challenging to include all various forms Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Extending the Concept of Customer Value (2) ⚫ Customer Referral Value (CRV) ⚫ similar to CIV, but instead of being intrinsical motivated, referrals are extrinsically motivated by the company ⚫ in contrast to CIV, CRV can not be negative ⚫ Customer Knowledge Value (CKV) ⚫ value of the information customer provides company with ⚫ helps to produce personalized and innovative offerings based on customers´ preferences (generated from feedback and complaints instead of market and consumer research) ⚫ customers can also be integrated in the value creation process through co-creation ⚫ CKV can increase innovation success rates as well as improve existing offerings Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Customer Equity ⚫ Customer equity (CE) = Sum of the LTV of all the customers of a firm ⚫ CE) is an indicator of how much the firm is worth at a particular point in time as a result of the firm’s customer management efforts. I CE = å LTVi Where: i = customer, i=1 I = all customers of a firm (or specified customer cohort or segment), LTVi = lifetime value of customer i ⚫ Can be seen as a link to the shareholder value of a firm ⚫ Customer Equity Share = an alternative metric to MS that takes the lifetime value of customers into account Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Summary ⚫ Strategic CRM plays a key role in profitably managing customers by reconciling both ends of the figure below: ⚫ Offering value to customers, thereby creating satisfaction and, in turn, loyalty ⚫ while managing individual customers to extract value from them ⚫ and aligning investment into the relationship and profit from the relationship ⚫ 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 ⚫ Value comes in multiple shapes and forms – CLV, CKV, CIV, and CRV ⚫ The sum of the LTV of all the customers of a firm represents the customer equity (CE) Source: Kumar, V./Reinartz W. (2018).  mar keti ng.uni - gr a z.at Impulse Reading ⚫ Kumar, V./Peterson, J.A./Leone, R.P. (2007). How Valuable is Word of Mouth? Harvard Business Review, Vol. 85 (10), pp. 139-146 ⚫ https://hbr.org/2007/10/how-valuable-is-word-of-mouth 1. Briefly describe the concept of customer referral value. 2. How would you describe the so-called "doing-saying gap"? 3. What's your take on the fact that there is such a strong focus on the telecom industry? Do you think it can be applied across industries in the same way? Source: Kumar, V./Reinartz W. (2018). 62