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Do you want to be able to predict your customers’ future needs? With the help of smart data and predictive analysis you can determine which individuals are likely to buy a car, take out a private loan or become parents in a given time span. In short, you can identify consumers that are moving into a new phase of life with new needs, interests and buying behaviors.
A generation ago, predicting the behavior and consumption of different customer groups was more straightforward. People had children at about the same age and lived at the same address for a long time. But things have changed. Today there is much more variation and there are many more choices to make in life. A 25-year-old can have more in common with a 50-year-old than with a person of his/her own age.
In today’s markets, it is important to adapt your communication to increasingly choosy consumer groups. With a quality analysis of public data sources together with data collected through surveys, you can get to know your customers better.
"If the analysis is done in the right way, you can find answers to questions about how people act as individuals, what their values are and what phase of life they are in. This in turn provides important information about their consumption needs."
Sara von Schoultz, senior analyst at Dun & Bradstreet
The customer’s buying power
A person’s level of income doesn’t always say so much about his or her buying power. A person with an annual salary of €36,000 who recently bought a house is likely to have less money in his wallet after the bills have been paid than a person with the same salary who has lived in the same house for ten years. Using data you can deduct whether the entire household’s buying power is high or low.
The customer’s phase of life
What phase of life is your customer in? These days, a 55-year-old man can be a new dad, or a grandfather. The customer’s phase of life often sets the framework for what the customer is interested in as well as how you can best get their attention.
The customer’s next buy
By combining large amounts of data with historical consumption patterns, you can calculate what each customer will likely buy next. Today it is possible to say that “with 70 percent probability, Eric will move house within one year, and he will therefore need a mortgage as well as home insurance.”