After delivering a 49% increase in leads for a fintech client, this data visualization project was meant to demonstrate this result to them—as well as to show how their current system was handling the influx of leads.
The latter point was important because while the conversion rate had fallen, further investigation showed that this was caused by a 46% internal backlog in the client’s sales team when it came to processing these additional leads. With a big chunk of these final results unknown, it was hard to see how well of a job we were doing in terms of the quality of leads we were bringing in.
The data was taken from the client’s CRM over a period of eight months. The statuses for leads in the database were a lot more granular. For the sake of simplicity, I narrowed down 31 statuses into 6: (leads that had yet to be contacted, to be interviewed, leads that were moving down the funnel, leads that have been closed/converted, those who had been declined, and those who weren’t interested).
I used Python for data cleaning and wrangling, and Tableau for the visualization. Turn around was about a week.
Since bringing in additional quantity was no issue, I could only suggest ramping up the processing of leads internally with the client. This way, we could get a more conclusive conversion rate and evaluation of the quality of the increased traffic.
Once this has been set-up, it would be possible to get regular feedback and look into factors such as the most notable reasons for being declined, features or combinations of features that lead to a high probability of conversion, etc.