Predictive Analytics: A tool to improve the customer experience

At the end of the day, what is the strongest decision on whether a company will succeed in the long term? These are not price structures or points of sale. It is not the company logo, the strength of the marketing department or whether the company uses social media as an SEO channel. The strongest, most important determinant of business success is customer experience. And creating a positive customer experience is facilitated by predictable analytics.

When it comes to creating a positive customer experience, business executives obviously want success at almost every level. There is no point in being in business if customers are not the focus of what a company does. After all, without customers, a business does not exist. But it’s not good enough to wait to see how customers respond to something a business does before deciding how to proceed. Leaders need to be able to predict responses and reactions to provide the best possible experience right from the start.

Predictable analytics are the perfect tool because it allows those with decision-making authority to see the history of the past and make predictions about future customer responses based on this story. Predictable analytics measure customer behavior and feedback based on certain parameters that can easily be translated into future decisions. By taking in-house behavioral data and combining them with customer feedback, it suddenly becomes possible to predict how those same customers will respond to future decisions and strategies.

Positive experience Equal to positive income

Companies use something known as net promoter score (NPS) to determine current levels of customer satisfaction and loyalty. The score is useful for determining the current status of the company’s performance. Predictive analytics are different, going beyond the here and now to tackle the future. Thus, analytics can be a main driver, producing the kind of action needed to maintain a positive customer experience year after year.

If you doubt the importance of the customer experience, analytics should make sense. An analysis of all available data will clearly demonstrate that a positive customer experience translates into positive revenue streams over time. In the simplest of terms, happy customers are customers who return to spend more money. It’s that simple. Positive experiences are equally positive revenue streams.

The real challenge in predictable analytics is to collect the right data and then find ways to use it in a way that translates into the best possible customer experience that team members can provide. If you cannot use what you collect, the data is essentially useless.

Predictive analysis is the tool of choice for this endeavor because it measures the behavior of the past based on known parameters. The same parameters can be applied to future decisions to predict how customers will respond. Where negative predictors exist, changes can be made in the decision-making process with the intention of transforming a negative into a positive. In this way, the company provides valid reasons why customers remain loyal.

Start with goals and objectives

Just as starting an NPS campaign requires establishing goals and objectives, predictable analytics begins in the same way. Team members need to decide goals and objectives to understand what type of data they need to collect. In addition, it is important to include input from all stakeholders.

In terms of improving customer experience, analysis is only part of the equation. The second part is getting each team member involved in a collaborative effort that maximizes everyone’s efforts and all available resources. Such collaboration also reveals inherent strengths or weaknesses of the underlying system. If current resources are insufficient to achieve the company’s goals, team members will recognize it and recommend solutions.

Analytics and customer segmentation

With a predictable analytic plan from the ground up, companies need to turn their attention to segmentation. Segmentation uses data from past experiences to divide customers into key demographic groups that can be further targeted in terms of their responses and behavior. The data can be used to create general segmentation groups or finely tuned groups identified according to certain niche behaviors.

Segmentation leads to additional benefits of predictive analysis, including:

  • The ability to identify why customers are lost and develop strategies to prevent future losses

  • Opportunities to create and implement problem-solving strategies targeting specific touch points

  • Opportunities to increase cross-selling across multiple customer segments

  • Ability to maximize existing ‘voice from customer’ strategies.

In essence, segmentation provides the starting point for using predictive analytics to anticipate future behavior. From this point of view, all the other options listed above flow.

Your company needs predictable analytics

Companies of all sizes have been using NPS for more than a decade. Now they are beginning to understand that predictable analytics is just as important to long-term business success. Predictive analysis goes beyond simply measuring the behavior of the past to also predict future behavior based on defined parameters. The predictable nature of this strategy allows companies to use data resources to create a more qualitative customer experience, which naturally leads to long-term brand loyalty and revenue generation.