July 2007

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

Zeynep Akşin, Evrim Güneş, Lerzan Örmeci KoçUniversity

Hazal Özden, Koç Sistem

July 2007

Outline

Motivation and research questionBrief look at the literature Modeling frameworkBrief survey resultsModels with different forms of negative reactionConclusion

July 2007

Cross-selling

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

July 2007

Customer reactions to cross-sellingCustomer reaction

“I don’t wantanother sales pitch,just transfer themoney! ”Lost time, annoyance

“This new loan optionis exactly what I need!”+ $$€€

Try to Cross-sell?

•(+) Retention: Marple and Zimmerman, 1999•(+) Reduce churn: Kamakura et al. 2003•(-) Switch: Kamakura et al. 2003

July 2007

Our research questions

How can we model negative reactions to cross-selling attempts?Would such reactions influence optimal cross-selling policies?If yes, how?

July 2007

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

July 2007

Relation between a customer and the firm

Poisson with rate λ

Exponentiallifetimewith rate µ

P(accept)=

1-P f

P(decline)=Pf

Customer reacts:• lowers utilization of service?• quits relationship earlier?• less inclined to accept future attempts?

Assumptions of SMC 87

Like in HPZ 05

July 2007

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)?

July 2007

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 attemptsWe consider different effects the number of contacts may have on customer behavior

July 2007

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 }

July 2007

Illustration of the Model

revenue of a standard contact

Service only

Attempt

customer contact

Rv(i, j+1)+

v(i+1, j+1)+R-ca-cfv(i, j)

v(D) = 0

Customer quits the relation

Failure

Success

λ

µ

P f(i,j)

1-Pf (i,j)

ca: cost of attemptcf : one time cost of

failure

v(0, 0)+R-ca+ r

r: additional revenue from cross-selling

July 2007

Model 1: No customer reaction Pf(i,j)=Pf(0,0)=Pf

If it is optimal to attempt in one state, it will be optimal in all states.

( )( ). ifonly and ifAttempt

f

af cr

crP+−

<thresholdPf

*

July 2007

Model 2: Basic customer reaction Pf(i,j)=Pf(i)

Assume Pf(i) is an increasing function of i, as suggested by survey resultsIn 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

July 2007

Model 3: customer reaction with general goodwill effect Pf(i,j)=Pf(i), g(j)

Each customer contact is a service requestAs services accumulate (j increases) the customer derives some utilityService 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.

July 2007

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 policyNote: Number of contacts since last cross-sell have an effect on optimal policy

July 2007

Model 4: Customer reaction fully reflected in the probability of failure

Probability of failure increases with the ratio of failed attempts:

The function models a form of forgettingEffect 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

⎟⎟⎠

⎞⎜⎜⎝

⎛+

+=1

1 (0,0)),(jiPjiP ff

July 2007

Model 4: Structure of optimal policies

Numerical study shows that optimal policy is dynamic in a certain range for Pf .

Pf=0 Pf=1State-dependent policy choice

Always xs

Pfmin(0,0) Pf*(0,0)=(r-ca)/(r+cf)

Never xs

July 2007

Example: Optimal policy when Pf(0,0) = 0.3

Number of contacts

0 1 2 3 4 5 6

0 1 1 1 1 1 1 1

1 1 1 1 1 1 1

2 1 1 1 1 1

3 1 1 1 1

4 1 1 1

5 1 1

6 1

Num

ber o

f atte

mpt

s

July 2007

Example: Optimal policy when Pf(0,0) =0.35Number of contacts

0 1 2 3 4 5 6 7 8 9 10 11 12

0 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

2 0 1 1 1 1 1 1 1 1 1 1

3 0 1 1 1 1 1 1 1 1 1

4 0 1 1 1 1 1 1 1 1

5 0 1 1 1 1 1 1 1

6 0 1 1 1 1 1 1

7 0 1 1 1 1 1

8 0 1 1 1 1

9 0 0 1 1

10 0 0 1

11 0 0

12 0

Num

ber o

f atte

mpt

s

July 2007

Example: Optimal policy Pf(0,0) = 0.4

Number of contacts

0 1 2 3 4 5 6 7

0 1 1 1 1 1 1 1 1

1 0 1 1 1 1 1 1

2 0 0 1 1 1 1

3 0 0 0 1 1

4 0 0 0 1

5 0 0 0

Num

ber o

f atte

mpt

s

July 2007

Example: Optimal policy Pf(0,0) = 0.49N

umbe

r of a

ttem

pts

Number of contacts

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 0 0 0 0 1 1 1 1 1 1 1 1 1 1

2 0 0 0 0 0 0 0 0 1 1 1 1 1

3 0 0 0 0 0 0 0 0 0 0 0 1

4 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0

July 2007

Do negative reactions influence optimal cross-sell policies?

Yes!Models 2 and 3: optimal cross-sell policies will be state-dependent threshold policiesModel 4: optimal cross-sell policies can be dynamic with a complex structureImplications for managers– Understand the type and magnitude of

customer reaction present– Take impact of past interventions into

account in developing policies

July 2007

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.

July 2007

Questions ?