Get to know your customer better
Above anything, a customer analysis is based on transaction data. This can be derived from cash systems. Aditionally, we include data from targeted marketing. In e-commerce we are able to evaluate each client’s account, thanks to personalized shopping carts. Information regarding the contents of each cart can be interrelated with other carts among all categories. This way, you gather valuable information about your customer’s buying behavior. This way, you always know when to advertize which product to which customer. Thus, with the results of our customer analysis you increase your upselling potential.
Additionally you respect individual privacy, while still allowing the retailer to evaluate expected buying behavior. This helps you to narrow down articles, likely of being purchased together and advertize them accordingly. Time, as an additional factor allows further proposition about the quantitiy of expected sales.
Despite of all these advantages, extensive customer analysis can portray huge challenges to many companies. This is due to the large quantity of transaction data, wich needs to be evaluated for this process. Using tradition, relational methods, results are calculated slowly. However, we help you carry to carry out these calculations quickly, due to our theoretical and technological approach to the subject.
With our solution, customer groups and their future buying behavior can be thus be identified and segmented. Self-learning systems continuously adapt to current behavior. This way, you identify customers with higher revenue potential and advertize accordingly. Developing prognostic models you can predict cancellations and take measures to avoid them (Churn Prevention). At the same time you increase customer satisfaction (Customer Lifetime Value) and improve your sales figures.
Our algorithms analyze existing customer data and transaction data to carry out a segmentation (ABC customer analysis). huge amounts of data are processed rapidly to identify behavioral patterns of your customers and derive predictions (predictive customer analysis).
Thus, you identify your customers and are always aware of their interests. By including data about contracts and customer interaction, predictions regarding customer loyalty and upselling potential can also be made for service industries. This way, you always know what your customer wants.
