How many of us spare barely a second or two to read an ad on our mobiles? Tuck S. Chung, Prof. of Marketing at ESSEC Business School, Asia-Pacific, shares research into how big data can be used to improve product personalization and overcome the challenge of the short attention span.
Unlocking Customer Value through Data-driven Personalization by Tom Gamble and Tuck S. Chung. Related research: Adaptive Mobile News Personalization Using Social Networks, Journal of the Academy of Marketing Science, 44 (1), pp. 66-87.
In the age of big data, a firm who can harness the power of advanced analytic techniques is better able to secure the loyalty of the customers and to win customers over from the competitors. Indeed, customer data is enabling companies to adapt their offerings and better meet their customers’ needs on an unprecedented scale. Nevertheless, many companies haven’t seized this opportunity—opting instead to let their customers personalize services manually. Through his research, Professor Tuck S. Chung of ESSEC Business School, Asia-Pacific argues that this is ineffective. Instead, he proposes a new approach to personalization that leverages customer data and algorithms to identify and adapt to customer preferences in real-time—creating substantial value for consumers and companies in the process.
Data-driven personalization: Revealing real preferences
With more and more of business revolving around information services, the cost of changing products over time is less and less of an obstacle. The challenge for companies today is instead determining how their products should change to better fit their customers’ needs. Currently, the predominant approach used on the internet is to let customers personalize the product themselves. However, research has demonstrated that this self-customization approach is ineffective as people are surprisingly incapable of determining what they want.
Instead, Prof. Chung argues that an “adaptive personalization” system that, over time, adapts the product based on observed customer behaviour can produce a better outcome as it learns more about the customer. The benefits of this approach are that it is automatic—requiring no proactive effort on the part of the customer—and that it learns about the customer’s preferences in real-time. What’s more, this adaptive personalization can be accomplished at a low cost by using automatic algorithms because customer behaviour data on information products can be easily collected.
In addition to the customer’s own data, Prof. Chung argues that by incorporating information from their social network, the system’s performance will further improve. Indeed, prior research on social networks has extensively documented the influence of peers on the adoption and usage of products. This is because peers—as opposed to experts—exert a greater influence on choices for products that involve personal taste. As people often rely on their friends’ choices to make their own, including this data from social networks can help the personalization system better discern the customer’s preferences.
A newsworthy application
In order to test this approach, Professor Chung and his fellow researchers decided to implement the adaptive data-driven personalization system in a mobile news application they developed. As mobile devices are more integrated into people’s personal lives they represent a more natural way in which digital services are consumed. This makes them an ideal setting to test the consumption of digital services. What’s more, mobile devices are hampered by people’s shorter attention spans—making the personalization of the content they display more crucial to meeting customers’ needs.
News, on the other hand, has traditionally been mass-produced with television and print media catering to mass audiences. However, as people increasingly turn to the internet and social media to get their information, audiences are becoming smaller and more targeted. Combined with the attention-constraining pressure of mobile usage, this has created a need for highly personalized news content—eliminating the burden of sifting through dozens of articles to find one worth reading.
The problem is that today, the vast majority of news websites and applications only allow the user to personalize their news manually. This is where Professor Chung’s proposal comes in. The researchers’ mobile news application used an adaptive algorithm that captured the user’s change in reading preference and subsequently presented them with articles based on this observation. In addition, the application employed the user’s social network for cases in which their preference for a news article was deemed to be ambiguous—maximizing the potential for peer influence.
Finding a better read: Data-driven personalization
To measure the success of their application, the researchers conducted two field studies: the first to collect data on participants’ reading behaviour on mobile devices and the second to report changes to this behaviour resulting from the implementation of the adaptive personalization system. Several dozen undergraduate students from a major university were recruited for the study and lent mobile devices on which they could use the application. The findings from both these studies provided clear evidence that the researchers’ proposal was a better system for personalization than alternatives.
Indeed, by implementing the algorithm in the mobile news application, the studies found that users were more accurately and consistently shown articles that they preferred than when they self-customized themselves. Moreover, the predictive performance of the algorithm increased when users’ social networks were used in the system. This eased the process for users to find articles to read and even increased the time they spent reading them—evidence for both initial and sustained interest in these personalized articles. Most impressively, the second field experience showed that the adaptive data-driven personalization system improved over time as it learned more about the user’s preferences.
Although the collection and use of users’ data in Professor Chung’s proposed adaptive personalization system may seem to erode privacy to a degree—even if much of the information can be shared anonymously—the practice may be a win-win for both customers and companies alike. Customers, on the one hand, gain from better-personalized products, while the service providers gain from being able to better satisfy their customers’ needs—and presumably from monetizing some of that added value. The proposed system exemplifies the benefits that can be drawn from using big data and technology to provide improved service to customers on an individual scale.
A unique path forward
As companies increasingly have access to individual customer data, they can use that information to personalize their offerings in a way that better serves their customers’ needs. Although it has long been held that personalization can most effectively be done by people themselves, Prof. Chung’s research demonstrates that leveraging technology may yield better results and perpetually improve personalization over time. This new system for automatically adapting services in a cost-effective manner may be a golden opportunity for companies to bring value to their customers and themselves in a truly unique way.
- Link up with Tuck S. Chung on LinkedIn
- Read a further article by Tuck S. Chung: Investor pressure and myopic management
- Discover the ESSEC Asia-Pacific campus
- Apply for the MSc in Marketing Management and Digital (MMD) on the Singapore campus.
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