A TYPICAL ENGAGEMENT:
- Get the data. Sounds easy. It is not. Customer data nearly always resides in multiple, difficult-to-use-and-access legacy systems. Analytics data from the web site is disconnected from the customer file. And we have yet to come across a web analytics package that is actually tracking what the company thinks it is tracking. We fix the web analytics and meld the online and offline data into a single view of the customer.
- Clean the data. Customer information always has data quality issues, including missing – or wrong – data. Or data whose meaning has been lost in the annals of time as employees and vendors have churned through the organization. We clean this up.
- Mirror the data. We import the cleaned data into an Amazon Web Services Redshift database. Redshift is simultaneously exceedingly powerful and exceedingly cheap. This provides us an analytics platform without impacting traditional company operations, systems, and operating procedures.
- Analyze and answer the important questions. This is the fun part. Our data scientists distill simplicity from complexity by deploying techniques from neural networks to dimension reduction to cluster analysis to regression to classification.
- Turn these answers into business results. We use these insights to drive more efficient customer acquisition, more effective customer retention, and higher sales and profits. This is not about generating reports or dashboards (although we do both in the course of our work) but rather turning data into information, and information into action.