email marketing

1 |

en bedrijfskunde

25-04-2018

Predicting churn based on the interplay

between usage (dynamics) and newsletter

email marketing

Thesis defense

MSc. Marketing Intelligence and Marketing Management

28 January 2019

Dick van Klaarbergen

S2749351

› Customer retention management has a large impact on firm

performance



Higher than customer acquisition

› Churn prediction to increase customer retention management

effectiveness



Churn: customer that uses another learning method than the year

before

- Contractual churn

› In B2B churn is different



Little churn literature in B2B



Balance importance satisfaction and switching costs is different

(Palmatier et al., 2006; Jones et al., 2000) - Influences churn prediction



Retention more effective in B2B

2005; Mudambi, 2002)

Predicting churn is useful

The relation of usage and newsletter emails

on churn is studied

How is the interplay between usage (dynamics) and

newsletter email marketing related to the churn probability

of a customer?

› Educational publisher data



Core courses secondary education



Analysis level

: school-section-course

› Data



Churn data: based on booklist data



Usage data: based on scores on online learning method

- Only online part of usage



Newsletter email data: based on newsletters sent

› 11,816 observations



466 (3.94%) churners

Educational publisher data is used

› Binary logistic regression model

DV: churn

› Model performance criteria

AIC: parsimonious model

TDL: predictive performance

› Advanced machine learning techniques



Decision trees

C5.0



Neural Networks



Ensemble learning

- Balanced sampled machine learning

Logistic regression is used to model churn

Introduction Theory Data Method

Results

Discussion

Significant predictors

› RFI usage

Recency  Churn (+)

Frequency  Churn (-)

Frequency moderates

Recency  Churn (+)

(see figure)

› Dynamics in usage



Monthly dips moderates Frequency  Churn (+)

Monthly variation  churn (-)

› Newsletter marketing



Newsletters  Churn (-)

› Targeting customers by

using usage data for

newsletter emails is not

effective in reducing churn

› Effective to predict churn

with usage data



TDL of 2.3 (see figure)

› Logit performs better than

advanced machine learning

techniques



Balanced sampling causes

instable performance

The model performs more than 2 times

better than a naïve model

› Theoretical implications



Effect of intensity and most dynamic factors not

approved



Effect of targeting email newsletters by usage data not

approved



Peaks indicate something else than dips

Machine learning not effective

› Managerial implications



Use churn predictions for targeted emails



Use churn prediction for prioritizing of retention efforts

-

Like account manager visits

The model can be used for targeting in

retention efforts

10 |

en bedrijfskunde

25-04-2018

References

› Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000). Switching

barriers and repurchase intentions in services. Journal of Retailing, 76(2), 259-274.

› Narayandas, D. (2005). Building loyalty in business markets. Harvard

Business Review, 83(9), 131-139.

› Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness of relationship marketing: A meta-analysis.

Journal of Marketing, 70(4), 136-153.

› Pfeifer, P. E. (2005). The optimal ratio of acquisition and retention costs.

Journal of Targeting, Measurement and Analysis for Marketing, 13(2),

179-188.

› Rauyruen, P., & Miller, K. E. (2007). Relationship quality as a predictor of B2B customer loyalty. Journal of Business Research, 60(1), 21-31.