Technology in the realm of “customer profiling” has gone mainstream in recent years, making an impact in people’s lives in ways they may or may not realize. As it makes its way forward and becomes more robust—in an always-on, cloud-connected world—privacy and security issues have risen to the forefront.
In the world of data warehousing and mining, customer profiling is an area of interest that has reached a sort of critical mass in recent years. The advent of profiling has enabled organizations of all kinds to make sense of their customers and learn more about them as part of aggregate groups and patterns than ever before. This review briefly outlines some of the definitions, applications, current development, and future trends in customer profiling.
In order to understand customer profiling, it is important to understand the underlying process behind profiling and general and the steps involved. The first step is preliminary grounding, where the process starts by specifying the issues that are applicable to profiling and identifies what the goals are of analyzing the data. This is followed by data collection, where the database or dataset is formed through a selection of the applicable data.
Next is data preparation, where unnecessary attributes are eliminated. Central to the process is data mining, where the prepared data is analyzed using the algorithms that were developed for the data at hand and the goals of the process. The next step is interpretation, where patterns of data that were mined are studied by specialists in the field to make sure they are relevant and that they are valid. Following that is application, where the profiles that were constructed are applied to categories of people.
The final step is institutional decision, where the organization that has studied the data decides what should be done – what actions should be taken, for example – with groups of people or individual people who match up with a profile (Solove, 2004). These defining steps underscore the basic technical definition of profiling. Customer profiling, therefore, refers to these steps applied in the business/customer environment.
There are numerous applications for customer profiling, especially in the private and commercial sector, but also in the nonprofit and public sector. For-profit companies will like to attempt to predict customer behavior so that they can more effectively sell them products.
The loyalty card industry is a great example of customer profiling as well as one of the most popular applications. These come in the form of reward cards and coupon cards for various stores and products. One of the most relevant examples is Kroger, which offers the Kroger Plus Card to its customers and has an entire analytics arm, Dunnhumby USA, dedicated to storing and analyzing the data on those cards to better understand its customers and target more products to them on an individual and aggregate basis that the profile data suggests they might be interested in (Neff, 2013). Collecting and analyzing data such as this about customers has become crucial for businesses to stay competitive.
The entire realm of customer relationship management is also an application of customer profiling. Popularly known as CRM, the technique encompasses first identifying the business’ customers, then attracting the customers that will bring in the most profits, and finally retaining those customers to make sure growth continues. Customer profiling in this context applies to the process of creating the profiles of those customers so that the further relationship management with them can be carried out (The Economist, 2009).
Personalized advertising is another form of customer profiling. In this context, the profiles of customers are applied in a context of marketing and advertising to generate ads that the company believes will be more in specific customers’ interests as compared to generic mass advertising. This phenomenon is especially prevalent across the Internet, as almost all major brands now carry out some kind of personalized advertising. Like loyalty cards and CRM, the aggregation of personal data for advertising has raised privacy concerns, and there have also been some concerns over the relevance of such ads (Watson, 2014).
Recent developments have shown that customer profiling has become ever more essential to modern business. The implementation of in-depth customer profiling at Kroger, discussed earlier, was instrumental in helping turn the company from one that was seen as lagging behind to one that led the industry with rapid growth (Neff, 2013). Similarly, in the Internet space, online services and social media websites such as Google, Twitter, and Facebook have seen that personalized advertising has been instrumental to their continued growth and profits – even Apple, which often insists user privacy is the name of the game, began using personalized advertising in its iAd network (Watson, 2014).
As we look to the future, it appears that privacy, security, relevance, and reception among individuals will be important factors in customer profiling. In recent years, so-called regular users have begun to wake up to the fact that much of their personal data exists in numerous places across the Internet and in private data centers. As data breaches affect both stores and online services, customers want to be sure their personal data is secure. Moreover, customers also want to make sure their privacy is protected – businesses and organizations must make sure the personal data they collect through customer profiling is used responsibly and not in a way that “creeps out” users (Watson, 2014).
Beyond the privacy and security concerns, businesses and organizations will want to make sure the ways that their profiling is surfaced to customers is relevant and received well by those users. It becomes ever more important for the data collection and profiling methods to be accurate and appropriate for users so that they feel as if they are receiving some sort of benefit from the process.
As important as it is to understand the technical underpinnings and processes that make customer profiling possible, it is equally important to understand the implications of the data aggregation and use that goes into it. Customer profiling is a prime example of an issue at the intersection of technology and culture that will only become more prevalent as the years continue to go by. Organizations and individuals alike will be taking notice.
- Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. New York: Wiley.
- Neff, J. (2013, September 2). Kroger’s Not Lagging Anymore: Smart Data Tactics Propel Grocery Chain. Retrieved from Ad Age.
- Solove, D. (2004). The Digital Person. Technology and Privacy in the Information Age. New York: New York University Press.
- The Economist. (2009, September 18). Customer relationship management. Retrieved from The Economist.
- Watson, S. (2014, June 16). Data Doppelgängers and the Uncanny Valley of Personalization. Retrieved from The Atlantic.