I'm in the process of trying to review our current Demographic and Behavioral Lead Scoring methods.
Trying to figure out what types of behaviors have accounted for the most accumulation of Lead Score and figure out where adjustments could and should be made to better the quality of MQL passed over to Sales.
Does anyone have any tips or advice on how to go about this review process?
This is a good question. I've done two things: one is exporting all records and performing an analysis on groupings, scoring by stage etc, so for example if you see a big cluster of leads at a certain score that is below mql threshold, then you may want to change your threshold etc.
Also, I have looked at a few leads/contacts in closed won and disqualified and look through the entire scoring history and find out what happened, you can pull insights from there.
I'm sure there are a number of ways to do this!
That would be ideal. I'd also compare SQL/Opp vs. recycled before that stage. If you look exclusively at Closed Won, I sense you will lose opportunities.
You can do a giant extract of behavior data with API.
You could also just get a predictive tool.
I agree content should be matched to the Stage.
One of the things we have found is that scoring behavior based on content format (white paper vs infographics vs blog article vs webinar) is less relevant than scoring based on the content position in the lead buyer's journey. But this means you need a model for the buyer's journey, of course