Customer Intelligence - Business Analytics Software

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Knowing the risk posed by your customers is not enough. You have to know when and how to act.

Nowadays, the economic situation is forcing organizations of all sizes to take a fresh look at their customer retention, cross-sales and customer acquisition strategies. All companies try to reduce costs associated with these strategies precisely when pursuing success is critical. Keeping the most profitable customers, increasing their satisfaction and spending levels, as well as new customer acquisition, all require companies to be different; truly different. They must do things another way, based on knowing their customers more deeply. Not doing it or doing it just like your competitors mans alienating your customers:

Ascertaining the risk of defection is not enough to set up an efficient prevention plan.
Lowering prices according to customer spending over the past few months is exactly what other competitors do.
Directing marketing campaigns to non-responsive customers amounts to an annoyance.

With every client, it is necessary to know when and how to act – or when it is best to do nothing.

Increase your results by 30%

The Apara dVelox Customer Intelligence solution represents a fundamental change on existing customer intelligence applications. This is a powerful predictive analysis solution and its 3D visualization and automated modeling engines make it easy to use. Only Apara technology can explain visually why a customer presents a risk and what the best course of action is.

The main capabilities of Apara dVelox Customer Intelligence are:

Customer data analysis, with automated data processing and easy to use interface
Automated learning and predictive modeling to provide analysis of campaign results, cross-selling initiatives and churning risks.
Advising on what customers must receive an offer and what specific offer to make; also which specific retention offers should be made to which customers.
Easy to use interface, enabling the visualization of complex data, from different angles.

Case study: Prediction of churn in telemarketing (spanish version)