In the E-Commerce World, High “Opening Rates” Are Poor Indicators for Brand Loyalty
(NEW YORK) – A first-of-its-kind study by Columbia Business School spotlights that companies may be at risk of shedding customers without even knowing it. The new paper – “Some Customers Would Rather Leave Without Saying Goodbye” by Columbia Business School Professors Eva Ascarza and Oded Netzer – examines how customers are ending their relationships with companies in new and unpredictable ways. The new study also concludes that while high customer “opening” rates might give some companies comfort that business will boom, research shows that high opening rates might also indicate that a customer is ready to move on, giving the company a false sense of security.
“Companies need to understand the ramifications of this paper, because in the digital age, the landscape on customer retention has changed,” said Professor Ascarza, the Daniel W. Stanton Associate Professor of Business. “Unlike the past when a consumer might have had to physically call to end a relationship with a company, today some customers are leaving without saying goodbye.”
The research shows that the rise of modern web-based business platforms providing free or “freemium” services is creating a “hybrid” dynamic in customer-company relationships. In an age dominated by web-based purchases, customer-company relationships are formed through free, opt-in subscriptions and accounts – allowing customers the choice of overtly terminating their relationship with the company or doing so silently by simply ignoring communications.
Two Types of “Goodbyes”
The authors note that two types of at-risk customers exist: those who overtly terminate; and those who silently terminate. The overt terminator ends the relationship with a company and its services with a notification, such as deleting an app or deactivating an account. The silent terminator will end a relationship without any notification – disappearing and then ignoring company communications altogether. The presence of silent terminators can cause companies to make business decisions based on unrealistic customer engagement, such as inflating staffing at customer support centers because of larger email lists or social media profiles.
“When it comes to customer retention levels, the most important thing is acknowledging that, in this new hybrid setting, there are indeed two unique types of customers who are at risk of ending their relationship with a company,” said Professor Netzer. “If businesses want to continue amassing and retaining loyal customers, they need to identify which customers belong in which bucket and then study their individual behavioral patterns so that they can take the appropriate measures to stop them before they walk out that virtual door.”
What Companies Need To Do
The authors suggest several actions companies can take to not only better understand the engagement of their existing customers, but also prevent customers from disconnecting with company brands:
- Individually study the behaviors and engagement patterns of at-risk customers in order to identify which customers are at risk of overt and silent termination;
- Tailor communications for customers in the “at risk” of overtly terminating state, providing them with relevant, personalized content in order to re-engage them;
- Regularly send customized communications and content to customers who are already engaged, reducing the likelihood they’ll silently leave;
- Know when to cut losses. Once a customer has silently departed, it is highly unlikely any communications tactics, particularly those employed in the past, will be effective in bringing them back to an engaged state.
To view the new study, click here.
To learn more about the cutting-edge research being conducted by Columbia Business School researchers, visit www.gsb.columbia.edu.
About the researchers
Eva AscarzaEva Ascarza was a Columbia Business School faculty member from 2010 to 2018.
Professor Netzer's expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain...Read more.