PredictiveAnalyticsCompanies must be future-oriented and pursue forward-looking marketing to enthuse today’s increasingly demanding customers. This means they need to always be one step ahead of consumer trends and wishes, which is made possible with the help of an impressive database, smart analytics, and predictive analytics.

In the following, we want to talk about the basic requirements for predictive analytics and what kind of analysis methods should be used sensibly.

Consumer behavior has changed, shopping opportunities have become even more diverse, and customer demands have gotten significantly higher. Today’s consumers can choose from a seemingly endless range of products and services – nonstop – leading to tough competition among providers. To survive, marketers are increasingly relying on customer-centered communication and innovative analysis methods such as predictive analytics that enable them to understand consumer behavior, forecast future changes at an early stage, and use marketing campaigns in a more targeted way.

Anyway, the first step is to generate relevant data that must be linked across channels and evaluated with the help of machine learning and AI-based analysis models and scores. Only through this it is possible to get a real understanding of the customer and make valid predictions about the wishes and purchasing behavior of consumers. Certainly, information must be collected in high-performance databases in compliance with data protection regulations, linked with one another in a meaningful way, and thus made usable. Finally, this requires structured, efficient data management and experts with the appropriate specialist knowledge to transform data into real customer knowledge.

Companies must not only know the needs and interests of their customers, but continuously take them into account; for instance, in the form of tailor-made offers, highly personalized messages or individual incentives. With predictive analytics, retailers can also foresee the behavior of consumers in the future with a certain probability, control communication activities in terms of time and content, and thus further optimize the customer experience. By constantly taking customer wishes into account, sales can be increased and customer loyalty sustainably enhanced, making this approach without question worthwhile.

For example, predictive analytics can be used to identify when and where marketing spending should be more intense or reduced: a scoring engine could be used to determine which customer groups are most likely to react to a campaign, which channel is most likely to be redeemed, or which type and level of incentive will achieve the best results. In addition, you can analyze which target groups are buying more because of actions or campaigns and not just relocating purchases, allowing companies to select the customer groups that are most likely to react and generate additional sales. Campaign results show that when sales increase significantly, ROI sustainably increases, and budgets are used optimally. Not to mention that with the help of predictive analytics, customers can be identified at an early stage, long before becoming inactive.

To counteract churn, individual warning systems can be implemented that are triggered as soon as customer behavior changes negatively. Depending on the individual interests, automated campaigns are then triggered that should continue to inspire the customer. Here, the timing is extremely important, because the success rate of retaining a customer, who has not yet fallen into inactivity, are significantly higher than with reactivation campaigns.

My advice is to stay vigilant, informed, and use technology.

By Daniela La Marca