Best Predictive Analytics For Customer Experience


One can surely say that through data analytics one can only look backward at what had already happened. But with the advent of predictive analytics, brands can use their vast amounts of data to forecast what will arrive shortly. Data is no longer a level playing field because organizations that are leveraging AI and machine learning software have an edge up over competitors who are still only using data to look at what had happened and not what is going to happen. Research shows that 77 percent of high-performing customer service teams rate their capability to leverage AI as excellent or above average. Organizations that get predictive analytics right can significantly enhance their customer experiences.

1. Prophesying Customer Needs

The most essential, but perhaps the most significant, type of analytics is anticipating customer needs. The above reasons are why the Sephora app so successful because by using data off when one purchased certain products, the brand knows when the person will need to repurchase them and when they will be looking for something new.

2. Real-Time Product Feedback

Predictive analytics influence so immediately that they can help tailor a customer’s experience as it happens. This characteristic is built into the algorithms of assistance like Netflix and Spotify. A customer’s actions, such as watching a specific show or skipping certain songs, impacts the next recommendations they will receive. Things change quickly based on consumer feedback and choices, so brands can capture what consumers want at that precise moment.

3. Distinguishing Flight Risk Factors

Data can pinpoint which shoppers are most at risk for moving. Companies that use predictive analytics to recognize flight risk factors can significantly augment their customer maintenance. FedEx uses data to foretell which of its customers will defect to a competitor with 60-90 percent accuracy. Similarly, Sprint can identify a segment of customers that are ten times more likely to cancel compared to other customers. By using data to determine the circumstances that lead to churn and the groups most inclined to leave, businesses can reach out with targeted messages to get the consumers to stick around.

4. Optimizing A Better Pricing Model

Many corporations used to change their pricing standards based on age or gender, but they can promptly do it with predictive analytics. This is particularly common with insurance companies. Progressive employs a telematics program called Snapshot and in-car sensors to measure how well and how often customers drive. That data personalizes the rates for each personality based on their probability of getting in an accident. A person who drives less often and stays close to home will have a lower price than someone who is always in the car and fancies to speed.

5. Staffing Up Or Down

Predictive analytics can improve brands anticipate high or low call volumes. Data from the website’s browsing models can tell a firm if it needs to staff up or down. If a lot of personalities have been shopping, there is a prominent chance of more calls, but if traffic has slowed, there likely will not be as many calls. Staffing properly saves companies money by not paying people when there is not any work and produces a better customer experience by ensuring there are enough people to help consumers during busy times.

6. Pre-emptive Service Model

Predictive analytics can be used to predict essential events in a consumer’s life cycle and enhance their revenue during those times. Insurance firms frequently use this model by predicting if kids will get their drivers’ licenses or when clients will move to a bigger house. Being able to foretell life events means the organization can proactively approach consumers about new products right when they require it most.

7. Real-Time Placing Bets

Personalized marketing can be powerful. But, it has to be based on data. Predictive analytics can operate through data nearly immediately to help companies make real-time purchasing bets. Caesar’s Palace and other hotels and casinos frequently use this application. Analytics can prognosticate what it will take for clients to stay at the casino.