How Customer Referral Programs Turn Social Capital into Economic Capital

Transcription

1 How Customer Referral Programs Turn Social Capital into Economic Capital Christophe Van den Bulte, Emanuel Bayer, Bernd Skiera, Philipp Schmitt forthcoming at Journal of Marketing Research

2 Description of Customer Referral Programs Incentivized Word-of-Mouth (WOM) Current customer (referrer) brings in new customer (referral) Fee paid out to referrer (sometimes also referral) Fee can be $ or in-kind Attractive characteristics of customer referral programs Cost Pay-for-performance, No large up-front investment No need to know customer network Simple to administer Revenue Higher, although eroding profit per customer Lower churn Attractive characteristics of referrer Referrer knows firm (i.e., has customer experience) referral Referrer often remains customer for some time after bringing in the referral 1

3 Previous Results with Respect to Profit per Customer and Churn Schmitt, Skiera & Van den Bulte (SSV) (2011, JM) Comparison of referred and non-referred customers Profit per customer Higher margin of referred customers Difference erodes over time Churn Lower churn of referred customers Difference stable over time CLV: 16-25% higher ROI: 60% Armelini, Barrot & Becker (2015, IJRM, Replication Corner) Profit per customer Lower margin of referred customers Development of difference not tested Churn Lower churn of referred customers Development of difference not tested 2

4 Aim of Presentation Testing whether mechanisms Better matching Referred customers better match with firm than non-referred customers Social enrichment Referrer strengthen social bond between referred customer and firm Lead to differences in profit per customer loyalty (churn) 3

5 Comparison of Schmitt, Skiera and Van den Bulte (2011) and this Study Previous study (Schmitt, Skiera and Van den Bulte 2011) Only data on: Referred customers (referral) Non-referred customers Only invoked but did not test possible mechanisms Better matching Social enrichment This study: Data on: Referred customers (referral) Customers who generated those referrals (referrer) Non-referred customers Testing of possible mechanisms Better matching Social enrichment 4

6 Hypotheses based upon Better Matching Better matching Referred customers better match with firm than non-referred customers Active: Referrer screens peers Passive: Homophily Presence of correlated unobservables of referrers and their referrals in margins churn Reason: Information Asymmetry between referrer and firm (but firm learns) H1 (because of passive matching): Referrer and referral have shared unobservables in their (a) contribution margin (b) churn rate H2 (because of active and passive matching): Experienced referrer refers referral with higher initial gap in (a) contribution margin (b) churn H3 (because of learning of firm): Over the referral s lifetime, initial gap erodes in (a) contribution margin (b) churn 5

7 Hypotheses based upon Social Enrichment Social enrichment Referrer strengthen social bond between referred customer and firm H4: After referrer has churned, referred customer exhibits (a) lower contribution margin (b) higher churn rate H5: Compared to a non-referred customer and after referrer has churned, disappearance in the referred customer s gap occurs in (a) contribution margin (b) churn H5 is stronger than H4 social enrichment is not simply lower (H4) but disappears (H5) 6

8 Data 7

9 Overview about Data Sets Referrers (existing customers) (DV: Referral = 0) 1799 Referrers with 5397 (= 3 x 1799) referrer-year observations Non-referred customers (DV: Referral = 0) 3663 non-referred customers with 10,989 (= 3 x 3663) customer-year observations 1788 non-referred but matched customers with 5,364 (= 3 x 1788) customer-year observations Referrals (referred customers) (DV: Referral = 1) 1799 referrals (corresponding to 1799 referrers) with 5397 (= 3 x 1799) referral-year observations 8

10 Overview about Dependent Variables Daily contribution margin per customer (DCM) Per-diem scaled variable Sum of contribution margin per customer divided by the total number of days the customer was with the bank (in observation period: ) Daily contribution margin per customer and year Time-varying version of daily contribution margin (DCM year ) Sum of contribution margin per customer in each year (2006, 2007, 2008) divided by the total number of days the customer was with the bank in the respective year Duration Total number of days the customer was with the bank in (expressed in 1000 days) 9

11 Independent Variables with Characteristics of Customers (1/2) Limited personal data Age (centered at age 40) Gender Marital status No information on nature of relation between referrer and referral Referred and non-referred customer s time of acquisition Binary variable for each month (February to October 2006) Cumulative number of days the customer has been with the bank (Customer Lifetime: CLT) Base line refers to 40-year old, single, male customer, acquired in January 2006 Referrer s experience before referring referral (two binary variables), measured by difference in acquisition dates of referrer and referred customer: is less or equal to 30 days (Le1MonthExp) between 31 and 180 days (1-6MonthsExp) more than 180 days (baseline) Referrer s time of acquisition Binary variable for year 2005, 2004, years , yeary , before

12 Independent Variables with Characteristics of Customers (2/2) Dummy variable Referral Referral = 0 if customer is Referrer (existing customer) Non-referred customer Referral = 1 if customer is Referral (referred customer) Binary variable describing year (2006, 2007, 2008) 11

13 Independent Variables Reflecting Social Enrichment Binary variables ReferGone: 0 as long as referrer remains with bank 1 afterwards RefalGone: 0 as long as referral (i.e., referred customer) remains with bank 1 afterwards Fractions PropReferGone: yearly ratio of number of days after the referrer churned at all days that the referral was a customer High ratio: referrer churned early in the year PropRefalGone: yearly ratio of number of days after the referral churned at all days that the referrer was a customer High ratio: referral churned early in the year 12

14 Replications of Findings of Schmitt, Skiera and Van den Bulte (2011, JM) 13

15 Replications of Findings of SSV (2011) Differences to Schmitt, Skiera and Van den Bulte (SSV) (2011) Different number of customers because of: Requiring demographic data of both, referrer and referral Selecting only referrers with one referral (to have unique dyad data) Referrers only from Jan-Oct 2006 Referred customers: 5,181 vs. 1,799 customers (SSV vs. this study) Non-referred customers: 4,633 vs. 3,663 customers (SSV vs. this study) Profit margin (DCM in ) Higher margin: 0.65 vs (SSV vs. this study) Difference erodes over time (both studies) Churn Lower churn by Oct 08: 9.7% vs. 14.5% (SSV vs. this study) Lower churn hazard of referred customer: 30% lower than non-referred customers Difference stable over time (both studies) 14

16 Mean Values of Characteristics of Customers, by Group Referrals Referrers Non-referred Non-referred All Matching N 1,799 1,799 3,663 1,788 DCM (across 33 months) Fraction churned

17 Mean Values of Characteristics of Customers, by Group (with more details) Referrals Referrers Non-referred Non-referred All Matching N 1,799 1,799 3,663 1,788 DCM (across 33 months) Fraction churned Age Female Single Married Divorced Widowed Other Acquired Jan Acquired Feb Acquired Mar Acquired Apr Acquired May Acquired June Acquired July Acquired Aug Acquired Sep Acquired Oct Acquired Acquired Acquired Acquired Acquired before Le1MonthExp MonthsExp

18 Shared Unobservables (better passive matching) in Margin (H1a) and Churn (H1b) (or social enrichment) between referrer and referral 17

19 Dyad-Specific Shared Unobservable Between Referrals and Referrals: Daily Contr. Margin (DCM) DCM Referral X d u e ijt 0 1 ij k kijt j ij ijt k 2 t: year (1) (2) Coef. z Coef. z Constant 2.067*** *** 4.11 Referral.296** PropReferGone PropRefalGone Est. z Est. z Dyad-specific variation ( d ) 1.234*** *** 7.59 Customer-specific variation ( u ) 4.128*** *** 4.91 Observation-specific variation ( e ) 2.856*** *** 5.73 LL -30, , Pseudo-R N 10,794 10,794 * p <.05, ** p <.01, *** p <.001. Significance tests for coefficients based on empirical robust standard errors. Models estimated on 10,794 customer-year observations from 1,799 referrals and 1,799 referrers. Pseudo-R 2 is the squared Pearson correlation between observed and predicted values including the random effect. i: referral: 1, else: 2 j: dyad k: control variable Random eff. model 18

20 Dyad-Specific Shared Unobservable Between Referrals and Referrals: Daily Contr. Margin (DCM) (1) (2) Coef. z Coef. z Constant 2.067*** *** 4.11 Referral.296** PropReferGone PropRefalGone Year *** *** Year *** *** Age (centered).028*** *** 4.15 Female -.551*** *** Married Divorced Widowed Other Acquired Feb Acquired Mar Acquired Apr * * Acquired May * * Acquired June * * Acquired July * * Acquired Aug * * Acquired Sep * * Acquired Oct Acquired in Acquired in Acquired in Acquired in Acquired before Est. z Est. z Dyad-specific variation ( d) 1.234*** *** 7.59 Customer-specific variation ( u) 4.128*** *** 4.91 Observation-specific variation ( e) 2.856*** *** 5.73 LL -30, , Pseudo-R N 10,794 10,794 * p <.05, ** p <.01, *** p <.001. Significance tests for coefficients based on empirical robust standard errors. Models estimated on 10,794 customer-year observations from 1,799 referrals and 1,799 referrers. Pseudo-R 2 is the squared Pearson correlation between observed and predicted values including the random effect. 19

21 Dyad-Specific Shared Unobservable Between Referrals and Referrals: Churn g( h ) Referral ReferGone RefalGone X d ijt t 1 ij 2 ijt 3 ijt k kij j k 4 With log-link function: g(h) = ln(-ln(1-h)) 22 (1) (2) Coef. z Coef. z Referral Refergone 1.329*** 5.76 Refalgone 1.397*** 6.35 Est. z Est. z Dyad-specific variation d 1.290*** LL -2, , N 3,598 3,598 * p <.05, ** p <.01, *** p <.001. i: referral: 1, else: 2 j: dyad t: day k: control variable 20

22 Dyad-Specific Shared Unobservable Between Referrals and Referrals: Churn (1) (2) Coef. z Coef. z Referral Refergone 1.329*** 5.76 Refalgone 1.397*** 6.35 Age (centered) Female Married Divorced Widowed Other Acquired Feb-Mar Acquired Apr Acquired May Acquired June Acquired July Acquired Aug Acquired Sep * * 1.96 Acquired Oct ** ** 2.70 Acquired in Acquired in Acquired in Acquired in Acquired before *** *** Est. z Est. z Dyad-specific variation d 1.290*** LL -2, , N 3,598 3,598 * p <.05, ** p <.01, *** p <.001. The models are complementary log-log hazard models estimated on the churn behavior of 1,799 referrals and 1,799 referrers controlling for duration dependency non-parametrically through a piece-wise constant baseline hazard by including an intercept and separate dummies for every 30-day period since acquisition in which any customer churned. Customer-day observations from 30-day periods since acquisition in which no customer churned do not affect the model likelihood, and are excluded from the estimation. Since no referral or referrer acquired in February 2006 churns, the coefficients for Acquisition in Feb and March 2006 are set to be equal. 21

23 Test of Hypotheses H2a-H5a on Differences in Margins Between Referred and Non-Referred Customers 22

24 Summary of Hypotheses H2-H5 H2 (because of active and passive matching): Experienced referrer (MonthExp) refers referral with higher initial gap in (a) contribution margin (b) churn H3 (because of learning of firm): Over the referral s lifetime (CLT), initial gap erodes in (a) contribution margin (b) churn H4: After referrer has churned (PropReferGone), referred customer exhibits (a) lower contribution margin (b) higher churn rate H5: Compared to a non-referred customer and after referrer has churned (PropReferGone), disappearance in the referred customer s gap occurs in (a) contribution margin (b) churn 23

25 Daily Contribution Margin (DCM) of Referred and Non- Referred Customers (Random Effects Model) 7 28 DCM Referral X PropReferGone X u e it 0 1 i k kit 8 it k ki i it k 2 k 9 (1) (2) (3) Coef. z Coef. z Coef. z Constant *** 4.28 Referral.672*** *** *** 4.76 Age (centered).009** ** *** 3.95 Referral x Age.024** ** 3.15 Le1MonthExp -.821*** *** MonthsExp -.500* * CLT Referral x CLT -.559** ** Age x CLT Referral x Age x CLT -.033*** *** Le1MonthExp x CLT.690** ** MonthsExp x CLT PropReferGone Customer-specific var. u 1.485*** *** *** 7.79 Observation-specific var. e 2.916** ** ** 3.02 LL -42, , , Pseudo-R N 16,316 16,316 16,316 i: customer t: year k: control variable Random eff. mod. 24

26 Daily Contribution Margin (DCM) of Referred and Non- Referred Customers (Random Effects Model) (1) (2) (3) Coef. z Coef. z Coef. z Constant *** 4.28 Referral.672*** *** *** 4.76 Age (centered).009** ** *** 3.95 Referral x Age.024** ** 3.15 Le1MonthExp -.821*** *** MonthsExp -.500* * CLT Referral x CLT -.559** ** Age x CLT Referral x Age x CLT -.033*** *** Le1MonthExp x CLT.690** ** MonthsExp x CLT PropReferGone Year ** Year *** Female Married Divorced Widowed.747*** *** *** 3.50 Other Acquired Feb Acquired Mar Acquired Apr Acquired May Acquired June * Acquired July Acquired Aug Acquired Sep Acquired Oct Customer-specific var. u 1.485*** *** *** 7.79 Observation-specific var. e 2.916** ** ** 3.02 LL -42, , , Pseudo-R N 16,316 16,316 16,316 * p <.05, ** p <.01, *** p <.001. All tests based on empirical robust standard errors. All models estimated on 16,316 customer-year observations from 1,799 referrals and 3,663 non-referred customers. Pseudo-R 2 is the squared Pearson correlation between observed and predicted values including the random effect. To avoid very small coefficients, CLT is expressed in thousands of days. 25

27 Test of Hypotheses H2b-H5b on Differences in Churn Between Referred and Non-Referred Customers 26

28 Summary of Hypotheses H2-H5 H2 (because of active and passive matching): Experienced referrer (MonthExp) refers referral with higher initial gap in (a) contribution margin (b) churn H3 (because of learning of firm): Over the referral s lifetime (CLT), initial gap erodes in (a) contribution margin (b) churn H4: After referrer has churned (PropReferGone), referred customer exhibits (a) lower contribution margin (b) higher churn rate H5: Compared to a non-referred customer and after referrer has churned (PropReferGone), disappearance in the referred customer s gap occurs in (a) contribution margin (b) churn 27

29 Churn Hazard of Referred vs. Non-Referred Customers 7 26 g( h ) Referral X Refergone X it t 1 i k kit 8 it k ki k 2 k 9 (1) (2) (3) Coef. z Coef. z Coef. z Referral *** Age (centered).038** *** ** 2.68 Referral x Age Le1MonthExp MonthsExp 2.443* CLT Referral x CLT Age x CLT -.043* * Referral x Age x CLT Le1MonthExp x CLT MonthsExp x CLT ReferGone 1.800*** *** 9.03 LL -6, , , LL vs (1) (p =.237) (p <.001) N 5,462 5,462 5,462 With log-link function: g(h) = ln(-ln(1-h)) i: customer t: day k: control variable 28

30 Churn Hazard of Referred vs. Non-Referred Customers (with more details) (1) (2) (3) Coef. z Coef. z Coef. z Referral *** Age (centered).038** *** ** 2.68 Referral x Age Le1MonthExp MonthsExp 2.443* CLT Referral x CLT Age x CLT -.043* * Referral x Age x CLT Le1MonthExp x CLT MonthsExp x CLT ReferGone 1.800*** *** 9.03 Female Married Divorced Widowed Other Acquired Feb Acquired Mar *** *** *** 3.26 Acquired Apr * * * 2.20 Acquired May Acquired June *** *** *** 3.36 Acquired July *** *** *** 3.50 Acquired Aug *** *** *** 4.26 Acquired Sep *** *** *** 4.87 Acquired Oct *** *** *** 6.40 LL -6, , , LL vs (1) (p =.237) (p <.001) N 5,462 5,462 5,462 * p <.05, ** p <.01, *** p <.001. The models are complementary log-log hazard models estimated on the churn behavior of 1,799 referrals and 3,663 non-referred customers, controlling for duration dependency non-parametrically through a piece-wise constant baseline hazard by including an intercept and separate dummies for every 30-day period since acquisition in which any customer churned. Customer-day observations from 30-day periods since acquisition in which no customer churned do not affect the model likelihood, and are therefore excluded from the estimation. To avoid very small coefficients, CLT is expressed in thousands of days. 29

31 Summary and Conclusions 30

32 Support of Hypotheses H1 (because of passive matching): Referrer and referral have shared unobservables in their (a) contribution margin (b) churn rate H2 (because of active and passive matching): Experienced referrer refers referral with higher initial gap in (a) contribution margin (b) churn H3 (because of learning of firm): Over the referral s lifetime, initial gap erodes in (a) contribution margin (b) churn H4: After referrer has churned, referred customer exhibits (a) lower contribution margin (b) higher churn rate H5: Compared to a non-referred customer and after referrer has churned, disappearance in the referred customer s gap occurs in (a) contribution margin (b) churn 31

33 Summary and Conclusions Summary Referrer-referral dyads exhibit shared unobservables in contribution margins Referrers with more extensive experience bring in higher-margin referrals Association between referrer s experience and margin gap becomes smaller over referral s lifetime Referral exhibits lower churn only as long as referrer has not churned Conclusion Better matching Consistent with patterns in margin gap across referrals, referrers, and time Social enrichment Consistent with patterns in churn gap over time and the change in referrals churn after their referrer churns 32

34 Feedback is welcome! Christophe Van den Bulte Emanuel Bayer Bernd Skiera Philipp Schmitt 33