If you’ve seen the hit Netflix exclusive series called House of Cards, what you’re watching is largely a product of big data. If you haven’t seen it, at least there is a good chance you’ve probably heard of it.
Netflix is a great example of the collection and use of big data due to the massive amount of data they collect from their viewers. Netflix collects this data from almost 30 million users. GigaOM has reported this data includes the users locations, the videos watched, every time a video is paused, fast forwarded or rewound, video searches and ratings, as well as data from social media. So what does Netflix do with all this data? Well, they purchased the series House of Cards, because their big data told them it was a good idea.
The data they collected from their users revealed there was a significant amount of users who would be likely to watch a series directed by David Fincher, starring Kevin Spacey. On top of that, after releasing the series, Netflix already knew exactly how it needed to push the series out to its users: by featuring the series as a recommendation for the same users their data previously told them would be willing to watch it.
Big Data can be used by many different companies for many different things. The first major benefit of big data is recognized from viewing the data at face-value, which will provide an accurate picture of how users/members/customers are currently behaving. Secondly, the data can be further analyzed to reveal trends or predictions. The large amount of data provides many opportunities for drilling down on specific sets of customers, which is often referred to as segmentation. However, big data sets may not always be able to provide a complete picture. In an ideal world, we would be able to collect all usage and satisfaction data and incorporate this into one large database. However, all companies are structured differently and interact with their customers through different channels in different ways, making it not always feasible. That’s where traditional market research steps in.
Conducting a large quantitative market research study with customers will help you drill down on some of the same audiences identified and examined in the big data sets. It will also provide an organization with the ability to find additional information – information that wasn’t collected or couldn’t be confirmed with the data. This information could be factors as simple as satisfaction, or competitive usage if the nature of your operations don’t allow for that data to be collected on everyone, but could dig deeper into more of the driving factors behind decision-making and what goes on in the customer’s mind.
In addition to conducting quantitative research, qualitative interviews or focus groups with select audiences will also allow an organization to get an in-depth understanding of what customers are looking for as well as provide a different perspective on what the data shows. This can be especially useful when conducting exploratory research to back up information uncovered or inferred as a result of the analysis of big data.
Big Data will always be a huge information source for businesses, particularly when tied with traditional market research methodologies. As data analysts, we have seen some of the results of data driven decision-making, and couldn’t encourage it enough. Using a plethora of actual data to backup decisions will always be much more productive (and profitable) then investing in decisions that don’t have the data to support it.