Profiling: The majority of currently available approaches count on existing customer data, complemented by the extrapolation of historical purchase behaviors – this is a reasonable first step, but far from being sufficient to tap the enormous potentials of predictive sales.
Our products support the screening of external data sources, based on advanced deep learning algorithms. Nowadays, clients voluntarily disclose a multitude of additional insights about the needs and interests shaping their purchase decisions – first and foremost in social media – why should you not use these insights to market your products more successfully? From long-standing preferences all the way to short-term, context-based requirements, there is an abundance of information you may deploy to perform a more accurate profiling of your customers.
Personalization: Recommendation engines have reached their limits – their algorithms have never been really suited for non-commodity products. Clients dislike recommendations featuring a poor fit with their needs, as well as obvious „shot gun“ marketing campaigns. If marketers disappoint their clients with annoying predictive sales experiences, they may damage their brand reputation, and will have to struggle even harder to regain credibility.
Advanced „Predictive“ models – like those deployed by Valculus – produce a considerably better fit between products and customer needs – based on „social CRM“, learning algorithms, and more substantial customer profiles. They do not only boost revenues, but also save time between lead generation and conversion – and ultimately, they increase customer satisfaction and loyalty.