Predictive Analytics Are Retailers’ Crystal Ball

Can predicting the future help retailers and other organizations avoid negative outcomes? It’s not just a thought exercise: By analyzing the past, organizations can shape the future they want to see, starting with creating the best possible user experience.

Those who don’t know history are doomed to repeat it, and all that.

Customer churn doesn’t have to be a total loss. Instead, retailers should be looking at which customers churned and why so that they can learn from their mistakes and leverage that information to prevent others from making the same choice.

To do so requires no crystal ball. All it takes is predictive analytics, a tool powered by artificial intelligence (AI), which looks at dozens or even hundreds of variables to determine what a user is likely to do next based on what he’s doing now. How is the customer behaving on the website or mobile platform, and what did others who behaved the same way do next?

That’s the question that Rephael Sweary, President, WalkMe hopes his company’s new AI Predictive Analytics solution can help organizations answer.

For instance, if a user is moving his cursor toward the X that will close the site, said Sweary, it may mean that he is frustrated with his experience and leaving. Sweary said now is the critical moment for the organization behind the system to offer help.

In some cases, step-by-step guidance through a confusing process may be valuable, with shout-out messages popping up to highlight the next step. Sweary noted that this is where WalkMe got its start: by helping organizations walk their customers through digital experiences.

“The key to retention is user adoption,” Sweary said. “It’s true for web as much as mobile. If you can’t convey how to use the app the first one or two times I use it, and if I can’t see the key values and uses, then I will churn.”

On a retail site, Sweary continued, the goal is for people to buy. Those who do so successfully followed a certain, optimal path. Others stray from the path and never convert to a purchase. Predictive analytics can see where the latter went astray and guide them back onto the straight and narrow path to purchase.

Essentially, it’s a way to see who’s going to churn before they churn.

But Sweary said retail customer churn is only the most dramatic use case for predictive analytics. There’s a significant use case in web and mobile, where this technology can help companies see which of their sales representatives are likely to succeed or fail. This creates further opportunities to deliver shout-out-style messages to drive the perfect contextual experience, Sweary said.

Predictive analytics can help banks avoid frustration and churn among older customers, who Sweary said may find themselves stumped by prompts to change their password during a mobile banking experience. This can lead them to stop banking on their phone altogether.

To avoid this, AI may identify signs of user frustration and prompt the user to sign up for fingerprint authentication so that they face fewer hurdles when banking via mobile in the future.

Predictive analytics have a place in internal systems, too, such as HR and customer relationship management systems. These can be difficult to navigate. Rather than allowing lost customers to struggle until they give up or call customer service in a temper, Sweary said machine learning can reveal which users are going to fail to complete a process so the organization can get there to offer guidance in time.

From shopping to travel to gaming, negative app experiences lead to negative reviews, said Sweary, and that’s bad for any brand. AI can help reveal the reasons behind the bad reviews.

If customers are annoyed by banner ads in a gaming app, they may stop using it and leave a negative review. Yes, ads are how gaming apps make money, but Sweary said some people are just never going to click on them no matter how many times they’re shown.

Instead, the ads end up doing more harm than good by driving users away from the platform. AI knows to stop showing banner ads to those people — and also what sorts of things to show them instead that may be of more interest to a dedicated user.

It’s important to show those users things they want to see as well, Sweary said. The common denominator between users who stick with an app is the adoption of more features.

In other words, unsurprisingly, “Better engagement leads to better ROI.”