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The leveraging of Customer Relationship Management (CRM) data with predictive analytics enables corporations to fully grasp the entire spectrum of their customer relationships. This includes a wide variety of business applications, such as trend detection, customizable marketing campaigns, understanding & measuring customer satisfaction, and predicting buying behavior. All of these elements, collectively termed as “CRM analytics,” reflect the possibilities of combining customer-oriented data with a predictive analytics methodology from a macro-perspective. Drilling down this topic to a deeper level reveals a nuanced application of CRM data usage: lifetime value optimization.
Lifetime value optimization is the prediction of net profit that is derived from the entire future relationship with a customer. This approach takes value determination to a deeper level by shifting away from retrospective methods and moving towards a progressive analysis of future earnings. It’s not enough to simply ask, “How much profit will Customer X produce for us in Q2?”; rather, targeted conditional questions inclusive of multi-source data should be asked. Value optimization techniques should include the prediction of the conditions themselves, after which they can be applied to the core question in order to further refine lifetime value optimization models.
A significant challenge that stands in the way of implementing value optimization techniques are barriers to efficient data analysis. These barriers can be technology-based (e.g., using inefficient data analysis tools), human-based (e.g., access to analysis tools limited to a select few), or—more often than not—a combination of both.
In order for organizations to make decisions about the lifetime value optimization of customers, data needs to be analyzed in real-time. Using current data means that modeled results will be relevant and decisions made will be accurate. Technological barriers to real-time analysis may be due to a number of different factors, from connectivity issues to processing time. Many solutions simply cannot handle Big Data... in these instances, calculations and number crunching could drag on for days.
Some solutions are not user-friendly and require specific knowledge and/or training. This creates an accessibility issue where analysis becomes the domain of a handful of trained technicians — a bottleneck is created while everyone queues-up and waits for technicians to complete analysis requests from their colleagues.
Data Science Studio (DSS) is a powerful solution that is designed to overcome traditional barriers to predictive data analysis by producing real-time results from multi-sourced data. DSS is highly accessible to multiple user profiles; its collaborative features, coupled with an intuitive navigation, is purposely user-friendly. All types of users are encouraged to create powerful models and visualizations, from marketing and business data analysts to expert data scientists.
DSS uses a 3-step approach when implementing value optimization techniques:
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