1 | 03-07-2019
The effect of email
marketing on customer
churn.
Table of contents
› Introduction
› Conceptual framework
› Data description
› Methodology
› Results
› Discussion & implications
Introduction
› Online sales exceed 12,5 trillion dollars in 2020 (Lindner,
2015).
§ Growth provokes overseas shopping (Emarketer, 2018).
- Retaining and gaining customers becomes challenging.
› Preventing customer churn is tied to profitable and value
(Neslin et al., 2006).
§ Acquire new customers may cost five times more than retaining old
customers (Bhattacherjee, 2001).
› Email is vital as a communication tool for customer retention
(Reimers et al., 2016).
§ 1.5 billion dollars spent in 2011 (Kim et al., 2011). § 2.81 billion dollars spent in 2017 (Scott, 2018).
› Not only high risk churners should be targeted with retention
programs (Ascarza, 2018).
§ Light and heavy shoppers behave differently (Spencer, 2010).
§ Price is a very influential factor in forming brand loyalty (Sohail et al.,
Data description
Measures
› Customer churn.
§ Pareto/NBD.
§ Threshold of six months.
› Email marketing.
§ Amount of emails opened per customer.
§ Amount of links in emails a customer clicked for at least one time.
Methodology
› Pareto/NBD.
§ Parameters.
- r = 2.26e-01, alpha = 5.64e+02, s = 2.41e-08, beta = 1.13e+01.
› Predicting capability.
› P(alive).
§ All customers >99.99%.
§ 0.15 repeat transactions within the first year (expectation function). § 0.02 transactions in the hold out period through a customer from the
Results
› H1: Amount of opens negatively influences customer churn.
› Higher amount of emails opened results in more sales from existing customers (Sahni, 2018).
› Positive instead of negative for cohort 1.
Results
› H2: Amount of clicks negatively influences customer churn.
§ Per-click models rely on higher sales through more clicks (Lee et al., 2018).
› Negative for all cohorts. › 1 more opens leads to a
decrease of 39,92% for cohort 1.
§ 68,19% for cohort 2.
Results
› H3: Average order quantity negatively influences customer churn.
§ High involved customer are more brand loyal (Ferreira, 2015) à Higher
transaction price leads to higher involvement (Holmes et al., 2013).
› Not significant for all cohorts.
Results
› H4: Average product price positively influences the effect of the amount of opens on customer churn.
› H5: Average product price positively influences the effect of the amount of clicks on customer churn.
› H6: Average product price has a positive effect on customer churn
§ Higher coupon redemption for products with higher relative price
(Osuna et al., 2016).
› Not significant for all cohorts.
Results
› Comparison of main effects only model and normal model.
Discussion & implications
› Opens increases customer churn.
§ Personal relevance for low frequency buyers.
§ Receiving to many messages can be bothersome, also for people who
opt-in for email (Bruner & Kumar, 2007).
§ SPAM is referred to when email is sent without reference to personal
needs or offer disproportionate benefits (Kumar & Sharma, 2014).
› Effect of opens disappears over time.
§ Loyal customers are less affected by promotions (Nagar, 2009).
› Opens does not influence customer churn like literature suggested.
§ Email marketing looses its power through new digital channels.
Customer now choose their object of interaction (Opreana & Vinerean, 2015).
› Clicks do decrease customer churn.
§ Firms should focus on email optimization to reduce customer churn.
› No effect of the average product price.
§ Price fairness do affect customer loyalty (Kaura et al., 2015).
Limitations and further research
› No ideal measurement for customer churn.
› Misfortunes in data gathering resulted in a small conceptual framework. › Effect of email marketing on customer churn in the digital market is
decreasing. The effectiveness of new channels is of interest for future research.
› Average product price is slightly insignificant for cohort 2 and significant for cohort 2 in the main effects only model
