Analytics For Customer Experience- I was recently in the market for new makeup and spent some time playing around with Sephora’s Visual Artist app that allows me to experiment with different looks and products through augmented reality. The app recommended products based on my purchase history, skin type and beauty preferences. It made for a great experience and was a lot of fun! The root of the app is predictive analytics. By knowing my demographics and what I had purchased in the past, Sephora could predict what I would want in the future.
It used to be that data analytics could only look backwards at what had already happened. But with new predictive analytics, brands can use their huge amounts of data to predict what will happen next.
Data is no longer a level playing field. Companies that leverage AI and machine learning software have a leg up over competitors who are still only using data to look backwards. Research shows that 77% of high-performing customer service teams rate their ability to leverage artificial intelligence as excellent or above average. Companies that get predictive analytics right can greatly improve their customer experiences.
There are seven types of analytics we can pay attention to when it comes to customer experience. Each type helps gain better understanding of customers and improve the overall brand experience.
1. Predicting Customer Needs
The most basic, but perhaps the most important, type of analytics is predicting customer needs. This is largely what makes the Sephora app so successful. By using data of when I purchased certain products, the brand knows when I’ll need to purchase them again and when I’ll be looking for something new. Similarly, L’Occitane uses AI and predictive analytics to ensure every section of its site meets individual customers’ needs.
2. Real-Time Product Feedback
Predictive analytics move so quickly that they can help tailor a customer’s experience as it happens. This feature is built into the algorithms of services like Netflix and Spotify. A customer’s actions, such as watching a certain show or skipping certain songs, impacts the next recommendations they’ll receive. Things change quickly based on customer feedback and preferences so brands can capture what customers want at that exact moment.
3. Identifying Flight Risk Factors
Data can pinpoint which customers are most at risk for leaving. Companies that use predictive analytics to identify flight risk factors can greatly improve their customer retention. FedEx uses data to predict which of its customers will defect to a competitor with 60-90% accuracy. Similarly, Sprint is able to identify a segment of customers that is 10 times more likely to cancel compared to other customers. By using data to identify the factors that lead to churn and the groups most likely to leave, companies can reach out with targeted messages to get the customers to stick around.
4. Optimizing A Better Pricing Model
Many companies used to change their pricing models based on age or gender, but they can now do it with predictive analytics. This is especially common with insurance companies. Progressive uses a telematics program called Snapshot and in-car sensors to gauge how well and how often customers drive. That data personalizes the rates for each individual person based on their likelihood of getting in an accident. Someone who drives less often and stays close to home will have a lower rate than someone who is always in the car and likes to speed.