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Angus_McCann1
Angus_McCann1
Level 3
August 15, 2019
Question

Engagement Scoring Decay

  • August 15, 2019
  • 1 reply
  • 5181 views

We are trying to come up with a general engagement scoring model in our Marketo Instance.

Right now we are the beginning phases (creating the model and let it run in the background for a month or two before rolling it out to users) and I'm trying to come up with an appropriate score decay model based on inactivity (the filter would be score has not changed in the past 30 days).

The concern is that if a person is super engaged - gains a bunch of points January through June, but then falls completely off in July, we want to be able to make sure that our decay isn't too light - that is to say that we are not still counting a person in Dec as "engaged" who hasn't engaged in 6 months. 

IDEALLY - We have a person's score decrease by say, 10%, for inactivity so the flow step would be "Change Score (*0.9)" but the only operators are addition, subtraction, and just changing it to a specific value.

I'm honestly stumped and I'm at the point where I'm concerned I just might be over thinking this - does anyone have any tips on how they decided an appropriate score decay value?

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1 reply

Jay_Jiang
Jay_Jiang
Level 10
August 16, 2019

There are many discussions and blog posts on this. But again, scoring is unique for your business. Think about the lead lifecycle and typical sales cycle length for your business. What actions indicate and how long do leads go inactive for before they typically become disengaged?

Create a date or datetime field field to track "last activity" - this is different to last modified, where last activity catches any action that indicates the the lead is still engaging with your business (you only need to allow leads to run through once a day) - e.g. visited webpage, opened/clicked email, filled out form, SFDC activity is logged/updated. Use this field in your inactivity scoring. 

How much to deduct and for what period of time, is really up to you and your understanding of your business.

Angus_McCann1
Angus_McCann1Author
Level 3
August 16, 2019

Sorry I misspoke before - since our marketing users are already tracking success in their programs based on the activities we want to capture, the scoring campaigns are going to be based on that.

Score Increasing programs will look like this:

  • SL:
    • Program Status was changed to Success
  • FLOW:
    • Change Score +X

*Schedule is daily, people can run through as many times as necessary

And the Decay Campaign is going to be:

  • SL: (All)
    • Was sent email 3 times in the last 30 days
    • Last Program Success was in past before 30 days
    • Not Score was changed in the past 29 days
  • FLOW:
    • Change Score -[Decay Value]

*Schedule would be daily, but a person can only run through the Decay Campaign 1 time every 30 days.

** I'm aware the SL filters already accommodates for this, but this is just a fail-safe 

I've googled and most of the blogs and article have not been very helpful as to how to determine the correct decay value.

So for example, I've predicted that we can expect over the course of the year, the average person's score get to be as high as around 3,000, without any decay. If someone was inactive for a year, we would want their score to dip to an "inactive" range, about 500. Based on that model, we could have a decay of around 130-140/month.

However, if a person stays engaged for 3 years and then falls off, their score would get as high as 9,000. With a decay value of 130/month, it would take almost 5 years for that person's score to drop to 500.

Has anyone else encountered this problem as well or am I over-thinking this? I just can't help but feel there is an obvious solution here that I am missing...

Jay_Jiang
Jay_Jiang
Level 10
August 17, 2019

When you have no maximum (or no minimum) lead scores, unless all your prospects take similar routes to become customers, you risk losing meaning in your lead scores. I, for one, have no idea what 3000 even means for your business, let alone someone with a score over 9000!

Consider redoing your lead scoring model so you have minimum 0 and maximum 100. We made 60 = MQL.

This way anyone can pretty well guess what a lead with a score of 80 means, or 20 etc.. which also makes decay easier.

Also, with webhooks, you can use any math operation you want for scoring.

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