Right now, we're in the midst of a data explosion, thanks to advancements in technology and of course, the internet. This data deluge is in fact a great opportunity -- if only the industry could unlock the potential and hidden value of data.
Given that many of data are driven by customers, businesses are positively teeming with customer data available from an increasing number of sources. In the ultra-competitive battle for customers, it's a race to capture as many data and information about customers as possible.
Great, you have the data. Now what? Taken as discrete pieces, data points are essentially meaningless. Data are worthless without the ability to translate the data into actionable insights and intelligence, which is where customer analytics plays a vital part.
Customer analytics is a customer-centric approach to guiding, driving, managing, and optimizing key customer decisions, based on a systematic analysis of the data available on customers and their behavior -- which can translate into a veritable goldmine for targeted marketing campaigns.
Most companies are in fact already using some form of customer analytics. To gain a better competitive advantage, businesses are hungry for more sophisticated analytics, to drive the biggest impact from the data and achieve true advantages.
The good news is there are a number of advanced statistical tools and data analysis techniques that enable more in-depth customer analytics and actionable customer intelligence, including predictive analytics (PA), customer lifetime value (CLV) analysis, and mixed market modeling (MMM).
Stop crunching your forehead, and let's take a look at what these tools can do for you.
Predictive analytics (PA)
Predictive analytics is primarily used by companies with a strong customer focus, such as retail, financial, communication, and marketing. A form of statistical analysis, PA uncovers relationships between explanatory variables and the predicted variables from past occurrences and patterns in data. That data are then used to predict future trends of customer behaviors, which is reliable enough to shape decision-making, forecast trends, and ultimately to identify and reduce potential risks and improve performance.
Predictive analytics is about sifting through the noise and the patterns in data, deriving actionable information and insights to enable and shape more proactive decision making, and ultimately improve business performance; it helps connect data to effective action. The crux is you need enough quality data to accurately identify and extrapolate patterns and predict future behaviors.
The more a business can predict the future, the better it can make intelligent business decisions. If you can predict short- and long-term behavior, you can affect short- and long-term behavior as well.