Customer segmentation

Consumers, customers, clients or users.  Call them what you will, but businesses exist and grow only when they can serve them well and attract more of them.  While there are several business strategies that one employs, one key methodology is to group or segment similar customers based on past purchasing behavior and geodemographic information.  This helps businesses tailor marketing and sales campaigns to each segment.

This has been an area of research for a long time and was included in formal marketing practice as early as 1950s.  A ton of research papers and articles have been published.  A simple google search for keywords “customer segmentation”, results in more than 4 million hits.  Today several technology companies exist that claim to have developed complex algorithms to better segment customers.

Given this background, what is springML’s role and what do we bring to the table?  We are approaching this problem holistically and provide an end to end solution.  The solution comprises of three components.

  1. Data preparation and enrichment. Any complex enterprise landscape comprises of multiple systems, each performing a specific function.  There could in fact be more than one system performing the same function, perhaps due to a merger or an acquisition.   For example, each business unit within the company could have a different CRM system.  We are able to leverage industry leading technologies that specialize in integration and ETL (Extract, Transform and Load) to bring this data together.
  2. Predictive modeling.  We don’t believe in reinventing the wheel here.  The decades of research has resulted in several techniques that have proven to work in the field.  We used this research as our foundation and have been developed a library of models that we can apply for each customer use case.  These models bring together the foundational research that’s publicly available as well as our expertise and methodologies to apply sound statistical principles to clean the data, fit the right model, test and train the model and finally evaluate it.
  3. We rely on the awesome visualization features of Salesforce Wave to display information that a user can consume easily.

This approach focuses on the solving the segmentation problem without added overhead of developing tools from the ground up.