We recently re-vamped our lead scoring model, which included resetting all the scores. Before resetting we noticed contacts with extremely high scores (people binging thought leadership content, but not a serious buyer). We have some negative scoring in place to deduct points for Inactivity over a set time frame, but I am curious to learn what ways people are managing outlier scores in their instance.
Thanks in advance for the feedback!
We usually set smart campaign that ensure that scores do not go above an below some thresholds. For instance, if the call ready threshold is 50, we will set a smart campaign that makes sure that the score does not go above 100 neither below 0 (after negative scoring).
We also limit the number of times a given lead can go through scoring campaigns in an hour, a day or a month.
Just curious to know, what action do you take when a score reaches 100? Reset them, pause for a while, dig deeper in why the score reached so high or something else? Does this have positive feedback from sales/other stake holders?
Any one above the MQL threshold is given a close look before being assigned to the inside sales for phone qualification.
People who have weird or non sense behavior of any kind are usually blacklisted, especially if they are in addition not reachable. This will in turn add them to our "Do Not Score" smart list, and a smart campaign will automatically reduce the score to 0 and keep it there. After a few months, when we no longer need them for reporting, they will be deleted from the database.
Beyond what Greg suggests, consider a more robust route for the future... we use a webhook to calculate percentiles across the database, so we can automatically mark extreme outliers without having to know the number itself (and the "high-water mark" adjusts automatically).
There's another (maybe too simple) way you can do it as well:
In your scoring programs you can simply add a statement which would reset the score to a maximum you set.
If Score is greater than X = X - to reset the score to its max.
You could always break your score into various buckets and set max limits to each while summing them in another program to create a total score as well. More insights and more flexible scoring restrictions.
Yes, this is what I meant with :
We usually set smart campaign that ensure that scores do not go above an below some thresholds