Finding Hidden Customer Behavior Patterns Using Big Data Analytics

In 2013, an astounding statistic began circulating in the tech and business spheres: 90% of all global data was created over the last two years. It’s as if data had been dormant prior to 2015 and has only just begun to be produced.

In many ways, that’s true. Historically, companies have been able to track only a limited amount of information about their customers, such as names, phone numbers, and previous transactions. Thanks to advancements in smart phones, social media, and e-commerce, we’re now able to track almost every major moment of our lives and turn this information into data.

Businesses now have access to a whole slew of new information about their customer base across many decision points — including their reactions to different stimuli in social media and advertisements.

They have a massive opportunity to better understand how customers make decisions and predict their future behavior when presented with new stimuli. While the customer may not know exactly why he bought a new hat, the business that sold it to him will. New techniques allow businesses to analyze data points over a long lifespan of interactions in order to disaggregate how customers make decisions.

While this influx of data is exciting, it is also difficult to interpret.

Without the ability to interpret the data, it becomes meaningless. That’s where behavioral analytics come in. Much like applying complex root-cause analysis to weather patterns, with the right information, human behavior can be predicted too.

Behavioral analysis aims to understand how and why consumers behave the way they do in order to predict what they are likely to do when presented with new situations. By compiling and analyzing raw data points like clicks, tweets, and online purchases, behavioral analysts can uncover hidden inner drivers. These insights help businesses understand what marketing should be used for individual customers — at the right time and at the right place.

Many businesses are already taking advantage of these analytics. At my company, Brillio, we recently worked with a U.S. retailer and used behavioral analytics to identify their most loyal segments within their customer base. We tracked millions of individuals through years of transactions and made various discoveries.

For example, we learned that customers that engaged with this retailer for their wedding plans remained extremely devoted to the brand over time and became one of their most profitable customer types. What did the company do with this insight?  They began to invest in and market towards customers that shared similar underlying traits (especially during special occasions). This resulted in a significant uptick in revenue and long-term value.

In another retailer, we utilized a deep learning engine to predict customer-buying decisions. When customers choose a shirt or pair of pants, their brain makes millions of different decisions at once. On behalf of this company, we developed a neural network algorithm to model the brain’s decisions when exposed to different stimuli.