Predictive analytics is now almost synonymous with AI and machine learning, but the ability to predict what customers want goes back much further than the development of artificial intelligence. Indeed, the ability to predict customer needs is a key skill for service professionals. So what’s the difference between a machine predicting our needs and wants over a human?

Customer service representatives learn about their customers through observations and cues. Those cues could be the expression on the customer’s face as they enter the building, how he is dressed, his accent, who he is with, the questions he asks, how quickly he approaches the service desk, the items he looks at, the services he pays for – even before digital interactions enter the picture, customer service teams can observe dozens or hundreds of “data points”. Mental calculations about how to respond to each customer need to be made in real-time. If records were made during previous visits, those would need to be reviewed prior to the present encounter (something that typically only happens at a luxury level).

One key difference between traditional anticipatory service and AI-powered predictive analytics is the vastness of available information that can be drawn upon when making decisions. Traditional service relies on a single human to use any number of cues available and make a judgement on how to best serve the customer. In contrast, AI can take into account the experiences of many other customers using a large data set, and then determine what is most likely to be the best response.

Transforming the customer journey and business operations

When we combine predictive analytics with online customer engagement platforms, we have the ability to shape the course of the customer journey even when they are not physically present. So while the analytics might tell you that a customer is unlikely to return due to a particular sequence of experiences or actions, a service representative, when given that insight, could potentially lure the customer back through some form of online engagement or sharing a special offer. This is just one way predictive analytics can improve  customer experience. Other benefits to a business include:

  • Creating staff efficiencies by predicting patterns of customer behaviour and re-allocating resources 
  • Gaining product feedback in real time by tracking how customers behave after having received a product or service
  • Matching products/services to customers through greater knowledge of what other customers like them prefer
  • Sending real time marketing offers, particularly when a high chance of churn has been identified
  • Identifying friction points with the product or service to understand what aspects need optimizing
  • Reducing customer churn by being able to react in real time when friction occurs.

The amount of data available to an organization, once compiled and removed from departmental silos, holds tremendous potential. Customer data collected through surveys, feedback tools, email, SMS, webchat and social media gives organizations a truly 360 degree view of their customer which ultimately enables a better customer experience while streamlining operations.