Take, for example, the experience of eating a meal in a nice restaurant. While almost all customers will rate the importance of visual appeal highly, it’s not what brings them back to the restaurant. If you get a meal that looks amazing, but doesn’t taste great, you are not going to rate the meal highly. Even though taste is influenced by visual appeal, ultimately, the taste of the food is one of the top defining factors in cuisine.
In CEM, interconnected data points can make it difficult to pinpoint exactly what’s driving customer behavior. A customer journey isn’t a series of separate, discrete data points. The experience a customer had online impacts the experience they have in the store. The speed of service impacts their opinion of the value of the product. There’s very few standalone moments when measuring customer experience.
This interplay is where it gets harder to separate which factors are truly relevant to the customer experience and what would actually move the needle to have an impact on your business.
And here’s where predictive analytics shines. At Cisco, we want to move beyond correlation analysis and start accurately predicting the drivers of long-term success.
Without the ability to accurately predict, CEM programs become a practice of randomly guessing at what will work. It’s an expensive hobby—and it won’t have the impact you want.
A predictive analytics platform identifies the drivers of customer loyalty, and pinpoints the touchpoints where taking action would have a measurable impact on achieving business goals.
In this section, we look at three advances in the field of predictive analytics that can give your data more power and increase the ROI of your CEM programs.