Companies hold a huge amount of current big data that represent considerable added value in the process of strengthening customer relations. However, they are often not fully aware of how valuable old data can be. Nevertheless, if you use historical information efficiently enough, you can direct your marketing policy in an almost clairvoyant way. This is borne out by the study of ‘predictive analysis’, where historical data are employed to predict customer behavior.
Penetrating the depths of your customer’s emotional world, and above all, avoiding losing him: those are, broadly speaking, the objectives of predictive analytics, a study method that examines historical data and uses them to predict trends. The stronger the quality of the data you collect and the longer the time frame in which you collected the information, the better you can observe and respond to certain trends, good or bad.Take, for example, customer satisfaction. The wide range of existing big data makes it a great deal easier to identify and segment new target groups. Detailed information on user profiles and their patterns of behavior enables companies to target their customers far more effectively with products and services that answer their specific needs.
Inspiring confidence in consumers
Identifying the target group need not be based on purely commercial considerations. Naturally you want consumers to remain loyal to your business, but the opposite approach - inspiring confidence in your customers - is an equally effective way to ensure customer retention. For instance, you can turn negative messages that have previously been posted about your company on social media to your advantage. By bearing this in mind when you develop and launch a new product, you can proactively nip negative customer experiences in the bud and show your customers that you do understand them.
In that sense, predictive analysis is an important tool for giving specific advice to businesses on new products and services: what does the customer need and how best do you put that on the market? The 360-degree vision offered by historical data analysis also gives you an insight into which products and services can be ideally combined.
Lower customer churn
Potential technical issues, malfunctions and other bottlenecks are easier to detect and predict if you carefully retain and monitor historical data. A perfect example of this is a predictive maintenance policy, for instance a telecom operator who calls a customer to warn him that his digicorder is malfunctioning, even before the customer has noticed it himself.
Even if technical issues should arise, predictive analysis can still create a positive context in the customer’s mind. Take, for instance, the case where the customer care agent has already detected the issue by the time the customer calls: having the solution offered immediately as soon as the issue manifests itself will undoubtedly give the customer greater satisfaction than when he has to try and give a detailed description of his concerns. If you can avoid customer frustration by catering to his immediate and future needs, consumers will simply be less inclined to go to the competition.
Predictive analytics describes how businesses operate today. It helps them to remove certain bottlenecks and detect new opportunities, resulting in a better customer experience and a greater competitive advantage. Nevertheless, the thoroughgoing analysis of historical data is more than that: it forms the basis for allowing companies to really get to know their customers. This is the first prerequisite for creating a deep commitment across different channels.