Predictive analytics is evolving

The field of data science has evolved considerably. Predictive analytic tools combine the power of a big data engine with advanced statistical algorithms to deliver actionable insights from large data sets that can be used to refine business decision making. Every CSP executive knows their business intimately.  Every executive has a tactical and strategic roadmap to improve growth and cashflow. That said, turning these dials of refinement to deliver the financial lift investors expect gets more challenging every quarter.  Predictive analytics is a powerful tool in the arsenal of business refinement and delivering the financial results that investors expect – both short term and long term. Executives who embrace and adopt this early, will gain a huge advantage – just like Brad Stevens did at Butler University and Billy Beane did with the Oakland Athletics.

Think for a moment that you’re a wireless marketing executive in London, and you’re charged with growing your subscriber base by 5% -- just in London.  By applying predictive analytics to the customer acquisition process, CSPs can refine their customer target and segmentation efforts in a much more granular, data-driven, and intelligent way. And unlike post-hoc analysis, which doesn’t match the pace of change in the market, these solutions are highly automated and rapidly deliver value by predicting which customers will remain loyal and which might be a credit or bad debt risk.

Four key questions that predictive analytics answers

Leveraging data science and complex fiscal analysis in the form of a predictive analytics solution enables CSPs to answer four key questions:

• Which customers should I pursue? Identifying the customers with the most long-term value is crucial. What good is it to spend money acquiring customers who are likely to churn in six months?

• How should I market to these customers, and what is the impact? Developing and focusing offers that entice new customers is critical. CSPs have hundreds of offers on the table at any one time, but often only a few actually perform well. This performance can be correlated with demographic, psychographic, and social data to create and refine compelling offers with a high hit rate.

• Which channels are most effective for each segment? There are many ways to reach a potential customer, from email and SMS to direct mail. It is crucial to understand how each medium performs in each market segment. Typically, this insight comes from campaign testing that takes many months. Rather than run individual A/B tests over six months, an automated solution that can test hundreds of options simultaneously and deliver actionable intelligence on the front-end is a much better strategy.

• What does my bad debt risk look like if I take on a specific segment? Not all customers are the same--some bring credit and fraud risks with them. In relationships, we call this baggage. Understanding the potential impact of adding new customers is critical.

Driving optimal growth

Optimal growth is not a numbers game built simply on the number of subs. It’s about attracting the right customers, creating an optimal-margin product mix, considering impacts on support operations and fraud, and measuring the efficacy of new revenue opportunities. Driving optimal growth requires an ability to measure future customer value. It requires an ability to correlate potential customer segments with campaign performance and truck rolls, or calls to customer care.