Customer Value: AI (Artificial Intelligence) breathes life into CRM data
Many companies apply CRM systems but use the existing data inadequately in identifying and exploiting turnover potentials. But algorithms driven by AI (Artificial Intelligence) enable the prediction of individual purchasing behaviours, so that data that is lying dormant in a company’s CRM system can be turned into a valuable information source.
A recent study indicates that CRM systems do not fulfil their purpose. The sales department doesn’t understand its customers! There is plenty of data, but it is not adequately interpreted. In particular, companies are not aware of their turnover potential. This tells them which customer will buy which product and with what probability, thus providing an effective marketing and sales management instrument.
AI understands unstructured data
With PA (Predictive Analytics), data from CRM systems can be analysed in such a way as to enable the prediction of customer behaviour. To achieve this, the software goes through a learning process involving a multitude of data sets, transactions and customer call reports. Until recently, transactions and normal variables were the main input for such forecasts, but the latest developments in the field of AI make it capable of also incorporating unstructured data. The applications “understand” call reports or entries in free-text fields and integrate this valuable data into the projections.
Past turnover is not the criterion for Customer Value
The determining factor for Customer Value is not the historic turnover achieved by a company with a consumer or an organisation, but the analysis of actual turnover in relation to potential turnover. Not just its self-learning capability, but primarily its sheer performance capacity predestines AI for generating significant sales and marketing insight. It is able to evaluate millions of customers’ data in a matter of minutes and recognises relationships that would escape the human intellect.
Up- and cross-selling with Next Best Offer
The Next Best Offer can be determined by relating a customer’s probability of purchase to individual product groups or products. This often opens up previously unrecognized up- and cross-selling potentials that have a positive effect on profitability, not only in direct sales but also in e-commerce and tele-sales. Good recommendations boost the conversion rate. Our tests have shown that Next Best Offer raises the level of acceptance by around 15% in comparison with a randomly composed shopping basket.
