LISTedTECH is a leader in HigherEd market analytics. Having mostly worked in close collaboration with selected companies, it has only recently decided to directly make available its analytics to HigherEd institutions, consulting companies, investment firms, vendors, etc. As such, it wanted to establish right from the start a solid customer analytics program to better understand what their potential customers were looking for and how to automate the workflow of their relationships, to increase conversion, retention and engagement.

The implementation process is very similar to the one described here, but with a few extras and a little magic sprinkled here and there.

A Newsletter-Driven Strategy

Since putting the infrastructure in place back in July 2017, there’s been a couple of thousands of visitors to the LISTedTECH website. The deployment of the Customer Analytics Program was coordinated with the release of a new weekly newsletter that informs the readers of major shifts in systems being used by HigherEd institutions.

That coordination between the deployment of both components (the Customer Analytics Program and the Newsletter) is important as new visitors were encouraged to register to the newsletter on the website. And this is how we were able to identify about 10% of all visitors to the website so far.

Our first results

This strategy allowed us to start building up rich profiles on who your customers are. For example, one reader is Justin himself (CEO of LISTedTECH).

Customer profiles through Woopra

Customer profiles through Woopra

As you can see above, we now have access to all his history of activities on the website. And, as we’ll soon be implementing for LISTedTECH, we can also enrich the user’s profile (left column) by integrating information from other sources (more on that in a later post).

For each action, we have a footprint of the source of visit, the technology used, as well as the location from which the visit was made.

Details about each customer's visit

Details about each customer’s visit

As all of this information is automatically being stored into a Redshift Data Warehouse, we can further explore it using our favorite BI tool (Tableau in my case). So bellow is just another way of visualizing Justin’s timeline.

Timeline of Customer Pageviews

Timeline of Customer Pageviews

Future directions

We’ve only started scratching the surface of the results we can start pulling from that infrastructure. But here are a few potential avenues we’ll start exploring in the future:

  • Automated identification of potential leads and the contents that interests them.
  • Profiling of those potential leads for a more accurate segmentation of users.
  • Content marketing strategy based on the topics of interest for segments of users.