Data Mining Software RapidMiner enables automated product recommendation systems and predictions of buyers’ decisions based on intelligent market basket analysis.

In the fierce competition among retailers, only those survive in the long run, that manage to quickly recognize and best serve the needs and desires of their customers and that at the same time optimally utilize their sales and margin potentials.  Data mining has proven to be a powerful tool in this endeavour by helping to identify customer needs and to serve them to maximize customer satisfaction and retention on the one side and profits on the other side.

Customer cards and loyalty bonus programs have become common among retailers.  On one hand, customers are bound by granted bonuses, and on the other hand, retailers gain enormous amounts of data about customer histories.  Re-occurring patterns in customer behaviour enables predictions of the customers’ behaviours and of their interests.  However, these enormous amounts of data make a manual use of the data infeasible.  The solution is RapidMiner, the leading open-source data mining software.  RapidMiner finds previously unknown useful patterns in the customer transaction data.  In contrast to classical hypothesis- or query-based data analysis tools, the user does not need to know in advance, what exactly he is looking for.  Instead, RapidMiner leads the user to the relevant information.  The analysis of the customer transactions provides insights into the customers’ behaviours and allowing to predict their buying decisions and to increase sales and profits by recognizing and utilizing up- and cross-selling potentials.  In an up-sell, a customer buys a higher-valued product or service than originally planned.  An insurance company may for example sell to a customer not only the requested basic car insurance, but possibly upgrade it to include additional features with an extra price tag on them like included insurance coverage for rental cars in foreign countries, if the customer likes to travel.  The customers thereby receives a higher-valued product better tailored to his needs, while at the same time the insurance company achieves a higher profit margin with that customer.  So, at the end, both are more satisfied thanks to the merits of data mining.  In a cross-sell, a customer buys further products in addition to the product he originally planned to buy.  In order to achieve this, online retailers recommend related products to the one currently selected, e.g. products other customers bought together with the product at hand.  An insurance company may for example offer a life insurance in addition to the requested car insurance, if the customer asks for a quote for an insurance for his family car.  If done well and matching the customers life situation, context, interests, and desires, this leads to significantly increased customer satisfaction and loyalty.  Using intelligent market basket analysis and mining customer histories, RapidMiner enables both retailers and sales departments to identify and appropriately leverage such up- and cross-selling potentials.  Training courses and services provided by Rapid-I, the company offering this powerful open source data mining software free of license fees to end-users, help companies to get started quickly with RapidMiner and with boosting their businesses.