When cross-selling backfires: modeling customer reactions to sales attempts

Transcription

1 When cross-selling backfires: modeling customer reactions to sales attempts Zeynep Akşin, Evrim Güneş, Lerzan Örmeci Koç University Hazal Özden, Koç Sistem

2 Outline Motivation and research question Brief look at the literature Modeling framework Brief survey results Models with different forms of negative reaction Conclusion

3 Cross-selling Important revenue generation tool in customer relationship management The practice of selling additional products and services to existing customers According to a McKinsey report estimate (2006) cross-selling can generate as much as 10% of the revenues through a bank branch network Call centers can attempt to cross-sell to 60% of its callers (Anton, 2005)

4 Customer reactions to cross-selling Customer reaction Try to Cross-sell? This new loan option is exactly what I need! + $$ I don t want another sales pitch, just transfer the money! Lost time, annoyance (+) Retention: Marple and Zimmerman, 1999 (+) Reduce churn: Kamakura et al (-) Switch: Kamakura et al. 2003

5 Our research questions How can we model negative reactions to crossselling attempts? Would such reactions influence optimal crossselling policies? If yes, how?

6 Brief look at related literature Descriptive models of customer relationship Schmittlein, Morrison and Colombo, 1987 Schmittlein and Peterson, 1994 Netzer, 2004 Optimizing customer equity Ho, Park and Zhou, 2005 Rust, Lemon and Zeithaml, 2004 Venkatesan and Kumar, 2004 Ching, Wong, Altman, 2004; Sun and Li, 2005 Sun, Li and Zhou, 2006

7 Relation between a customer and the firm Exponential lifetime with rate µ P(accept)=1-P f P(decline)=P f Like in HPZ 05 Assumptions of SMC 87 Poisson with rate λ Customer reacts: lowers utilization of service? quits relationship earlier? less inclined to accept future attempts?

8 Which type of reaction do we focus on? Web based survey 104 respondents with prior exposure to call center cross-selling Convenience sample: average age 32.8; 43% female Is the probability of failure affected by the number of previous failures (i), and the number of previous contacts (j)? Does a failed cross-sell offer affect the rate of contacting the call center, which is a measure of λ, and the probability of leaving the bank (as a measure of µ)?

9 Survey results Increasing i (number of failures) increases the probability of failure The majority of the subjects stated that they would not change their contact rate and quit rate as a function of failed attempts We focus on modeling negative customer reactions as a lower inclination to accept future attempts We consider different effects the number of contacts may have on customer behavior

10 A model of the relation between a customer and the firm Our aim: To maximize the total discounted value generated during the lifetime of a customer relationship with the firm. Assumption: Failure has a cumulative effect while success allows starting over fresh. State: Number of customer contacts (j) Number of attempts (i) State space: S = {(i,j):0 i j}

11 Illustration of the Model v(i, j) customer contact Customer quits the relation µ λ Service only Attempt v(i, j+1)+ R Failure Success 1-P f (i,j) P f (i,j) revenue of a standard contact v(i+1, j+1)+r-c a -c f c a : cost of attempt c f : one time cost of failure v(0, 0)+R-c a + r v(d) = 0 r: additional revenue from cross-selling

12 Model 1: No customer reaction P f (i,j)=p f (0,0)=P f If it is optimal to attempt in one state, it will be optimal in all states. Attempt if and only if P f < ( r c ) a ( r + c ). f threshold P f *

13 Model 2: Basic customer reaction P f (i,j)=p f (i) Assume P f (i) is an increasing function of i, as suggested by survey results In this case we will have a threshold value that depends on i Attempt cross-selling as long as the probability of failure is below the threshold, do not attempt once the threshold is reached. Note: earlier failures have an effect on optimal policy

14 Model 3: customer reaction with general goodwill effect P f (i,j)=p f (i), g(j) Each customer contact is a service request As services accumulate (j increases) the customer derives some utility Service revenue R only captures part of the story, contacts are part of overall customer relationship We model this case by adding a positive goodwill term g(j), increasing in j, to the outcome of the case when we do service only.

15 Model 3 results: state dependent thresholds For all j, there will exist some threshold l(j) such that we will always cross-sell if i < l(j) and never cross-sell if i l(j) Note: earlier failures have an effect on optimal policy Note: Number of contacts since last cross-sell have an effect on optimal policy

16 Model 4: Customer reaction fully reflected in the probability of failure Probability of failure increases with the ratio of failed attempts: P f ( i, j) = P f (0,0) 1+ j i + 1 The function models a form of forgetting Effect of bad memories fade away if the firm does not attempt a cross-sell, but it never completely disappears unless the customer decides to buy as a result of a cross-sell attempt

17 Model 4: Structure of optimal policies Numerical study shows that optimal policy is dynamic in a certain range for P f. P f min (0,0) Pf*(0,0)=(r-c a )/(r+c f ) Pf=0 State-dependent policy choice Pf=1 Always xs Never xs

18 Example: Optimal policy when P f (0,0) = 0.3 Number of contacts Number of attempts

19 Example: Optimal policy when P f (0,0) =0.35 Number of contacts Number of attempts

20 Example: Optimal policy P f (0,0) = 0.4 Number of contacts Number of attempts

21 Example: Optimal policy P f (0,0) = 0.49 Number of contacts Number of attempts

22 Do negative reactions influence optimal cross-sell policies? Yes! Models 2 and 3: optimal cross-sell policies will be state-dependent threshold policies Model 4: optimal cross-sell policies can be dynamic with a complex structure Implications for managers Understand the type and magnitude of customer reaction present Take impact of past interventions into account in developing policies

23 Future research directions Proposed a modeling framework to account for negative customer reactions to cross-sell attempts. Possibility to explore other models within the same framework. Tried to isolate the effects of such customer reactions on cross-sell decisions. In practice this will be more complex since marketing issues and operational issues will also play a role. Preliminary empirical evidence suggests that there is an effect of past failed attempts on the probability of failure. Need for empirical studies that explore the link between cross-selling, customer satisfaction and behavioral intentions.

24 Questions?