Irrelevant offer, wrong target, a lack of listening or inappropriate response: these are all factors that can lead to conflict during contact with a customer. If it’s essential for contact center agent to know how to deal with this type of problem when it arises, being able to anticipate it is even more effective.
A When analyzed using predictive analytics, this data can be used to better target customers, understand how they will behave in the future, and thus anticipate and limit the risk of conflicts before they arise.
Based on AI tools such as machine learning, predictive analysis proves to be a good way of studying customer history, whether it’s past behavior, purchase or browsing history, but also current trends. This data enables us to better define customer segments, and target a more relevant audience.
Thanks to this targeting, companies are able to identify the prospects and customers who will respond most favorably to a specific offer and react positively to the various marketing actions undertaken. In this way, predictive analysis can not only anticipate, but also limit the risk of conflict linked to an inappropriate product or service.
Since it is based on the study of historical patterns, predictive analysis can also be used to examine customer intentions in order to anticipate potential conflict risks and react accordingly. In fact, as well as helping to identify the right prospects thanks to the analysis of historical data, and approach an appropriate target, predictive analysis, thanks to other tools, is also used to anticipate and better manage conflict at the moment of making contact.
Semantic analysis, conversational analysis or sentiment analysis are solutions based on machine learning and natural language processing: they exploit the history of oral or written conversations to study the feelings and sentiments of customers and prospects.
In this way, it’s possible to define a customer’s potential to become aggressive, and therefore for conflict to arise. Agents then have all the keys they need to anticipate and adapt their discourse, using the most appropriate words and intentions to reduce the risk of conflict.
Finally, with predictive analysis, it is entirely possible, thanks to the study of data, to better target customer needs and expectations. By optimizing their overall experience, it becomes an indispensable lever for customer satisfaction.
By improving the customer experience through predictive analysis, the risks of problems are not only anticipated, but also avoided. The company will be able to identify the positive points, but also the factors of conflict on which it will have to work to maintain or improve the satisfaction rate (response too slow, offer not personalized, target not relevant enough…).
