Using Mobile Data to Change Consumer Behavior

Posted on by Chief Marketer Staff

By Olly Downs

Analytics applied to mobile data may be the next big thing when it comes to figuring out ways to change customer behavior in ways that really matter.

If you were offered a discount on your car insurance premiums for a better driving record, would you alter your speeding habits? And by alter I’m not alluding to an abrupt slow down every time you spot a police car sitting in the median.  I’m referring to a sustainable change in behavior—no more rolling through stop signs, speeding through construction zones and accelerating to get through yellow lights. 

Every business knows that over time consumer behavior is likely to change.  Consumers will change the products and services that they use.  They will change when they make purchases and how.  They will change what they recommend to their friends.  They will change how they respond to certain offers.  They will change the way that they communicate with you.  But how long does every business have to wait for customers to actually change their behavior in ways that will positively impact the bottom line?

Some companies are saying goodbye to the waiting game in exchange for a more proactive approach.

Look at State Farm’s Drive Safe & Save program.  Instead of waiting for customers to change their behavior to then act, i.e. a customer has a squeaky clean driving record for six months and subsequently receives a ‘good driver reward, they’re looking at what behaviors they need to change, and how they can proactively change them.  And they’re finding that through incentives that tie monthly discounts to customers’ daily driving habits, they can actually change driving behavior over the long term.    

The benefits are clear.  If a business can determine which behavioral changes will have the greatest impact on their bottom line, and determine how to change those behaviors, they can expedite capitalizing on the results.  

So what’s next in advancing this paradigm shift in how businesses are engaging with their customers to proactively change consumer behavior? Analytics applied to mobile data.

Yes, some are questioning whether the massive volumes and dynamic nature of mobile data makes harnessing and understanding it to truly influence consumer behaviors even possible.  But the nature of mobile data is actually what makes the opportunity so viable.  Mobile does not restrict you to engaging with customers at a certain time or waiting on a previous action to take place; and mobile data is so vast and rich that when leveraged intelligently, it can enable businesses to engage each customer with the right communication at the right time in the right context.

The question is, how do you determine what’s ‘right’?  This is where data science comes into play.  For years, the data sciences community has been incorporating a variety of theories from numerous fields—like math, statistics, behavioral learning, pattern recognition and data engineering—to extract meaning from all available data for the goal of monetization.  In a nutshell, we’ve been applying science to solve real-world business problems.  And mobile is the ultimate darling—tons of varied data, tons of decisions to influence, immediate ability to impact behaviors, and a continual stream of learnings for ongoing optimization. 

The excitement around mobile data is resulting in a unique collaboration among data scientists from a variety of fields—including medicine, pharmaceuticals, financial trading, underwater sonar and signals intelligence—to solve the challenges that mobile presents in maximizing customer value and loyalty.    

As an example, prepaid mobile operators continue to struggle with proactively addressing the problem of churning customers.  It’s the classic tale of acting too late.  By the time they identify an at risk customer, it’s too late to change the customer’s mind—and even worse the minds of those that the customer influences. Knowing that it can mean the difference between keeping or losing customers, this approach of using data science to proactively change behavior has piqued the interest of mobile operators at large, and specifically their in-base marketing teams.  

By leveraging solutions fueled by scientific analysis technologies, such as fingerprint hunting, longitudinal behavioral analysis and predictive analytics, these marketers are determining which behaviors and associated contexts are directly tied to churn, which customers are entering these contexts and when, and the optimal way to influence a change in behavior among each customer.  With this understanding and the ability to automatically act on it, the focus is no longer on putting out fires. It’s on nurturing each customer relationship to drive the right behaviors at the right time over time, which is having a dramatic impact on customer retention.    

But remember that nothing happens overnight, and just as with applying science in other fields of study, mobile data results are not static; they change over time.  Business impact analysis as well as behavioral impact analysis that guides ongoing optimization is a must for mobile, which is why businesses are turning to adaptive scientific marketing solutions which learn and respond to feedback.

Every business should be looking at how they can use mobile data science to be smarter and “more scientific” about how they engage with customers to actually change their behaviors. Like with anything, there is an expected curve whereby consumers will naturally discover, buy, adopt, use, fall in love with, recommend, share, and become loyal to a particular product or service.  Shoot, they may even become better drivers on their own with time.  But why continue to play the waiting game versus initiating change?   It’s time to look at every decision that a consumer makes as a unique opportunity to change their behavior and most importantly, capitalize on it.

Dr. Olly Downs is senior vice president of Data Sciences for Globys, a big data analytics company that specializes in contextual marketing for mobile operators.


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