Every morning when we get up the first thing that most of us do is checking our emails. Now if you are a Netflix subscriber you might find an email from Netflix about a new movie based on your preferences. Sounds a little bit awkward so you might start wondering how they know what you like without even asking you? The short answer is they are “watching” you. I know the next question you ask is how? And here we are going see how they do what they do.
Marketing research in 21st century
These days marketing research plays a key role in every single organisation. Marketing managers should work diligently to find management decision problem (MDP) and come up with relevant marketing research problem(s). It is marketing researchers’ responsibility to find out more about customers’ attitudes preferences. As Kotler (2009) notes “Marketing Research is the systematic design, collection, analysis, and reporting of data and finding relevant to a specific marketing situation facing the company”.
As Malhotra (2012) notes marketing research is defined as a systematic and objective process with clear-cut steps as seen in Figure 1.
In fact, what Netflix and similar data driven companies do is going through the same steps in answering marketing research problems but with a little tweak in data collection and data analysis steps.
At the data collection step instead of relying on traditional methods such as questionnaire surveys to find out about its customers’ preferences, Netflix collects data from the actual behaviour the customers. The company has got more than 70 million subscribers worldwide each spending 568 hours watching Netflix on average. When you log on to Netflix they track you and know what you search for and what you watch and they do it customer by customer. So they have a corps of data from millions of customers on what they like and what they don’t (Carr, 2013). This way they avoid response and non-response bias (Malhotra, 2012) which are common issues in collecting data in traditional approaches of data collection. Also using this approach in collecting data they get access to data from the whole population of their customers and not just a sample of them which makes them more confident about the outcome of the analysis.
What Netflix gets from its innovative method of data collection is what is known as Big Data. According to Gandomi and Haider (2015) “Big data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information”. Therefore, to process and analyse such datasets techniques tailored for dealing with datasets with such characteristics should be used and this is where data mining comes to play Data mining. By definition data mining is the “The exploration and analysis of large quantities of data in order to discover meaningful patterns and rules” (Berry & Linoff, 2004). This includes techniques such as association rules, clustering, and classification (Berry & Linoff, 2004).
So then by using such capabilities and such rich datasets Netflix would become able to understand its customer preferences and recommend similar content and more interestingly use its customers’ preference data to predict success of the shows it is creating. Sounds weird? But is it real!
In fact, when the company wants to create a new TV show they come to the data they have collected from millions of customers and their preferences and they see that for instance people really like work of David Fincher the director of “the social network” and films featuring Kevin Spacy had always done well. On this basis, records of subscribers’ streaming activities enabled Netflix to predict that remaking the original series House of Cards, with actor Kevin Spacey and director David Fincher, would be a success. So the made an American version of House of cards, with actor Kevin spacy and director David Fincher and it became a hit. The very interesting point is that Netflix had already known it before even releasing it (Erevelles, Fukawa, & Swayne, 2016).
Berry, M. J. A., & Linoff, G. S. (2004). Data Mining Techniques for Marketing, Sales, and Customer Relationship Management (2nd ed.). Indianapolis, Indiana: Wiley Publishing, Inc.
Kotler, P. (2009). Marketing Management: A South Asian Perspective: Pearson Education India.
Malhotra, N. K. (2012). Basic Marketing Mesearch : Integration of Social Media (4th ed.): Boston : Pearson.
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