Firms have tracked consumers’ shopping behavior in their stores for decades. Back in the days, most businesses were neighborhood stores. Employees greeted each customer by name and knew what each customer liked. These employees used consumers’ information to tailor interactions on an individual level across sales, marketing, and customer service. Nowadays, customer interaction often takes place online so firms rely on online customer data. Therefore, customers benefit by receiving products and services that match their personal preferences. Firms, however, can benefit as well, by charging higher prices for the recommended products as they provide better service (Chen et al. 2001). Therefore, the use of information for personalization sounds like a win-win proposition for firms and customers. However, customers can see it as a double-edged sword, which on one hand enhances consumer utility and at the same time cause privacy violation (Malhotra et al. 2004).
Recently, customers are becoming increasingly aware of the amount of data that firms collect and what risks are involved. We call this phenomenon overpersonalization (Bleier and Eisenbeiss 2015). Because of the trade-off between enhanced consumer utility and privacy concerns, Wattal, Telang, Mukhopadhyay and Boatwright try to find out how consumers respond to firms’ use of two types of information: product preferences and name.
These two types of information can for instance be used in an e-mail campaign targeting customers. Firms can send you such recommendations via e-mail in two ways: explicitly and implicitly. Where explicitly means that the company discloses that they based the recommendation on your preferences, whereas implicitly means they do not. Imagine that you receive an e-mail of a company recommending you the product that you have always wanted. You visited the website the day before for the first time and the sites’ algorithm already learned about your preferences (Johan, Mookerjee & Sarkar 2014). The e-mail does not explicitly state that the recommendation was based on your browsing behaviour. So, this is an example of implicit personalization.
These two ways of giving recommendations can lead to different levels of effectiveness. Imagine that a week later you wake up and the firm took their personalization a step further and begins the recommendation e-mail with a personalized greeting. You are not really familiar with the website as you only visited it for the first time last week. At this point you might start to become wary of the e-mail, as in recent years your awareness of the data you provide to retailers on the internet has increased. As the recommendation helps saving time, the data usage might lead to increasing concerns by customers (Tsekouras 2019). Companies start to take the negative effects of explicitly mentioning the use of personal information into account. Therefore, it might be more interesting for firms not mentioning the recommendations explicitly, but implicitly.
To find out how customers react to these different forms of personalization and how familiarity moderates this effect, the researchers collected data of approximately 20.000 customers from a web-based firm that is a distributor for many products varying from phone services to mortgage lending.
To research implicit personalization, the researchers studied the product-based personalization emails that the firm sent to customers and the customer’s reactions. The customers were divided into pools and when a customer in the pool “long-distance” received an email about long-distance phone services, it was classified as product-based personalization. When a non-related pool of customers received the email about long-distance phone services, it was classified as non-personalized. To research the explicit personalization, the researchers looked at personalized greetings that the firm used. The firm used these personalized greeting randomly, so some customers were greeted with their name while others were not and therefore the researchers could measure the differences between them.
To assess whether a consumer was familiar with a company, the researchers looked at prior purchases. If a consumer already bought something at the firm, the customer was deemed familiar with the firm.
The customers went through two decision phases. Imagine when you receive an email. First, you need to decide whether you open the email. Once you choose to open the email, you can choose several actions: unsubscribe, do nothing, click through but buy nothing and purchase the product. When the customers decided click through but buy nothing or purchase the product, their reaction was branded positive.
The researchers found that consumers respond positively when product-based personalization is used in the email. Contrary to this, they discovered that consumers respond negatively when a personalized greeting was used in the email. Furthermore, they found that familiarity moderates the negative effect of a personalized greeting. Customers who already made a purchase at the firm responded less negatively to the firm using their name.
A main strength of this study is that this study examines personalized emails that are directly sent by the merchant to consumers, whereas prior work only examined the personalized content made available on merchants’ websites or in controlled experiments. The biggest advantage of this research design is that it resembles real world answers the most; it incorporates people’s real reactions as they have to respond to personalized offers with real monetary risks. Controlled experiments can cause various biases. For instance, knowing the nature of a study can make consumers behave differently and subconsciously give response that they think that the researcher wants to hear, also known as the research bias. Using real world data can to a great extent omit these biases.
An important implication that you could take away from this article as a business owner is that personalization of an e-mail to your consumers might not always yield the positive responses you hoped for. It rather depends on the familiarity customers experience with your firm and the type of personalization you choose to use. Like Top marketing speaker David Meerman Scott once said “Instead of one-way interruption, personalized marketing is about delivering value at just the right moment that a user needs it”. So firms need to carefully consider how they use personalization because these good intentions might have the opposite effect.
Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669-688.
Chen, Y., Narasimhan, C., & Zhang, Z. J. (2001). Individual marketing with imperfect targetability. Marketing Science, 20(1), 23-41.
Johar, M., Mookerjee, V. and Sarkar, S., 2014. Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization. Information Systems Research, 25(2), pp.285-306.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information systems research, 15(4), 336-355.
Tsekouras, D. (2019) Customer Centric Digital Commerce Lecture 2 [Lecture 2]