Predictive Analytics Part 1: Optimize Your Decisions
How exactly do you go about optimizing your business decisions? Can you determine, for example, whether it’s more advantageous to cross-sell or upsell a customer during a service interaction?
The success of your decision depends on a variety of factors – the persona, or profile of the customer, the issue at hand and the mood of that customer at the time are a few of the critical factors. Since we live in a world of information overload, you very likely have mounds of information relating to these various factors stored somewhere in your service organization.
Look at the persona of your customer. A single trait of this persona can help you answer the question above, to effectively identify if a cross-sell or upsell offer will be accepted.
You must treat information like this as evidence and leverage it systematically in making your decisions. Essentially, your success means predicting the outcome based on historical trends. This is where predictive analytics is a key.
Predictive analytics is based on techniques for symbolic regression, which creates mathematical models used to synthesize even petabytes of data into intelligible answers. They help you better understand true variables driving success. These formulas work by comparing past examples of success and failure, and then using them to predict future outcomes. Models like these can be deployed and run by business analysts, not just statisticians with PhDs.
Such models have been successfully applied to a range of real-world scenarios:
- Adjusting fuel mixtures for optimal engine efficiencies
- Understanding the set of environmental and genetic attributes that correlate with breast cancer survival
- Adjusting oxygenation levels of premature babies to increase their rate of survival
They enable scientists and doctors to efficiently and effectively understand and capture the value from available information and use it as evidence to optimize outcomes. If predictive analytics is applied to fields like chemistry and medicine, then it can certainly be applied to customer service. In my next post, we’ll explore exactly how you can apply it effectively in your service environments.