Predictive Analytics: A Tool to Improve Customer Experience

    At the end of the day, what is the strongest determiner of whether a company will succeed in the long term? It is not pricing structures or sales outlets. It is not the company logo, the strength of the marketing department, or whether the company utilises social media as an SEO channel. The strongest, single most important determiner of business success is customer experience. And creating a positive customer experience is made easier through the use of predictive analytics.

    When it comes to creating a positive customer experience, company executives obviously want to succeed at nearly every level. There’s 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 company does before deciding how to proceed. Executives have to be able to predict responses and reactions in order to provide the best possible experience right from the start.

    Predictive analytics is the perfect tool because it allows those with decision-making authority to see past history and make predictions of future customer responses based on that history. Predictive analytics measures customer behaviour and feedback based on certain parameters that can easily be translated into future decisions. By taking internal behavioural data and combining it with customer feedback, it suddenly becomes possible to predict how those same customers will react to future decisions and strategies.

    Positive Experiences Equal Positive Revenue
    Companies use something known as the net promoter score (NPS) to determine current levels of satisfaction and loyalty among customers. The score is helpful for determining the current state of the company’s performance. Predictive analytics is different in that it goes beyond the here and now to address the future. In so doing, analytics can be a main driver that produces the kind of action necessary to maintain a positive customer experience year after year.

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

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

    Predictive analytics is the tool of choice for this endeavour because it measures past behaviour based on known parameters. Those same parameters can be applied to future decisions to predict how customers will react. Where negative predictors exist, changes can be made to the decision-making process with the intention of turning a negative into a positive. In so doing, the company provides valid reasons for customers to continue being loyal.

    Start with Goals and Objectives
    Just like beginning an NPS campaign requires establishing goals and objectives, predictive analysis begins the same way. Team members must decide on goals and objectives in order to understand what kind of data they need to collect. Furthermore, it’s important to include the input of every stakeholder.

    In terms of improving the customer experience, analytics is just one part of the equation. The other part is getting every team member involved in a collaborative effort that maximises everyone’s efforts and all available resources. Such collaboration also reveals inherent strengths or weaknesses in the underlying system. If current resources are insufficient to reach company objectives, team members will recognise it and recommend solutions. מערכת אומניצנל

    Analytics and Customer Segmentation
    With a predictive analytics plan off the ground, companies need to turn their attentions to segmentation. Segmentation uses data from past experiences to divide customers into key demographic groups that can be further targeted in relation to their responses and behaviours. The data can be used to create general segmentation groups or finely tuned groups identified according to certain niche behaviours.

    Segmentation leads to additional benefits of predictive analytics, including:


    • The ability to identify why customers are lost, and develop strategies to prevent future losses
    • Opportunities to create and implement issue resolution strategies aimed at specific touch points
    • Opportunities to increase cross-selling among multiple customer segments
    • The ability to maximise existing ‘voice of the customer’ strategies.


    In essence, segmentation provides the starting point for using predictive analytics to anticipate future behaviour. From that starting point flow all of the other opportunities listed above.

    Your Company Needs Predictive Analytics
    Companies of all sizes have been using NPS for more than a decade. Now they are beginning to understand that predictive analytics is just as essential to long-term business success. Predictive analytics goes beyond simply measuring past behaviour to also predict future behaviour based on defined parameters. The predictive nature of this strategy enables companies to utilise data resources to create a more qualitative customer experience that naturally leads to long-term brand loyalty and revenue generation.


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