Cultivating customer loyalty is a top priority for any business, and most brands today believe that if they can preemptively gain a better understanding of customer needs, they can take subsequent steps to anticipate and fill those needs to create a more positive customer experience.
To accomplish these goals, companies often collect as much customer data as possible to analyze consumer behavior, identify patterns, and use analytics to predict what their customers are likely to do next.
Insights Beyond Analytics
Unfortunately, understanding customer needs is not that simple, and analytics are only a part of the answer. Analytics can give companies a snapshot of customer behavior but they miss the important fact that humans are largely unpredictable.
So, as companies invest in ways to know their customers better, they often realize that developing insights into those relationships depends on understanding and adapting to customer behavior through in-person interactions. Technology and analytics can make that communication more productive, but nothing can replace the human component.
Predicting Individual Behavior
Bottom line, even when companies have the best of intentions when using predictive analytics to anticipate consumer behavior, they also need to be right on each individual case.
For example, companies may use predictive analytics to find customers who are exhibiting the kinds of behaviors that might indicate they’re thinking of taking their business elsewhere. Those companies can then take action to encourage dissatisfied customers to stay before they actually leave. However, not every customer may respond to that action in the same manner.
The Pitfalls of Predicting
Predictive analytics operate under the dual assumptions that customer data is accurate and that customers will behave in the future exactly the same way as they have in the past. Yet, even in its advanced state, analytics is an imprecise science — and it always will be. It assumes that input data is correct, there is no disconnect between speech and action, and that each person learns or receives information in exactly the same way.
Essentially, the formula is rigid and leaves no room for the fluidity of human emotion or behavior.
There’s another risk as well: Although the data may be accurate, without human input to frame the right question or follow up on the findings, it could be addressing the wrong problem. For instance, say that predictive analytics indicate that a customer may be thinking of leaving. An impersonal, automated note may further torpedo the relationship, while a phone call from someone who can adjust strategies and tactics in the moment to generate options or resolutions might go far toward improving customer sentiment.
Of course, there will always be outliers, or customers whose behavior will be at the long tails of larger data patterns. When customers feel like companies are lumping them into categories based on the behavior of others, it undermines the entire purpose of the customer experience, which is to treat the customer as an individual and tailor solutions to his or her needs. In cases like these, the sweeping use of technology at the expense of the human touch, can create tension between the customer and the brand, which may have the adverse effect of customer churn.
Personalizing Aggregate Predictions
To avoid these pitfalls, companies must tread carefully when using predictive analytics technology. The data should shape customer experience, but never control it. That responsibility lies with people, which is why it is imperative that companies have the right team in place.
As technology and analytics provide better insights and context for conversations and actions, the backbone of the customer experience must ultimately reside with highly trained and empathetic staff who can finesse aggregate data to make the right decisions for individual customers. It is with the customers who have been mislabeled, according to the data, where companies have the biggest opportunity to make a difference and truly build personal relationships.
Humans Create Loyalty
Predictive analytics are necessary to create the right customer experience but they aren’t sufficient. When used to inform and educate about customers, but not replace human interaction, predicting customer behavior can be a welcome sign that a company truly wants to understand and engage with the very people who keep its doors open.
Customers are demanding more for their loyalty and companies must oblige in a truly personal way in order to cultivate long-standing relationships with customers. Now, as the business world becomes more dependent on technology, ironically, it’s that same technology that should free up companies to connect with their customers on a much more human level.
The upside of predictive analytics is that companies can now be more informed about behavior so they can focus on building the right customer experience. Smart companies know that the more technology advances and grows, the more it will reinforce the need for the human element.
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