How to make zero lowest score?

[Anonymous]

Is there a way to keep scores from dipping below zero?

[Edward_Unthank_ · Accepted solution]

A few ways you can do this.

1. You can add a choice in the flow step, you have two choices: if score is greater than or equal to 0, change score, else do nothing.
2. You can set a nightly or weekly scheduled batch campaign to run on people whose scores are below 0 and change those scores back to zero.
3. You can set a triggered campaign fixing the score back to zero. Triggered when score changes and new value is less than 0, change the score back to zero.

Depending on how many decay campaigns you have (smart campaigns that subtract points from a score) , I'd probably choose between options one and two. If you only have a few, the first option is easy to implement. If you have hundreds of smart campaigns decreasing scores, the nightly or weekly scheduled batch campaign is going to be a quick solution to your problem, and doing nightly or weekly batches is a better use of processing power than triggered campaigns running to fix things (#3).

Cheers,
Edward Unthank | Founder, Etumos

[Anonymous]

Hi Sarah

Why do you want to keep zero?

I am afraid that it will cause poor scoring.

[Anonymous]

I don’t want to keep it at zero, just not allow it to dip below zero. What are your thoughts? I am interested!

Sarah Mayer

Marketing Operations Manager

MarcomCentral

858.847.6638 direct

800.220.1727 office

marcom.com<http://marcom.com/>

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[Edward_Unthank_]

I have a few opinions about this!

Behavior Score. I think of this as a "likelihood to have a positive conversation with sales" measurement. The higher the score, the more likely a lead is to have a positive conversation with a salesperson. This only goes up, and has the normal behavior score treatments. I don't put a decay on this score because I think decays hurt this as a proxy. If someone had 200 points on January 1, falls dark for 6 months, and then reactivates, it's important to know that their score was high before and that data isn't lost. That lead is more likely to have a positive conversation with sales than a prospect who hits 150 for the first time.

Demographic Score. This is a proxy for potential spend. If this person were to come crawling to you begging to do business, would they have enough money to be able to write you a check? This is more of how much you want a prospect regardless of how much they might want you. There is a threshold cutoff as a minimum, where people below that threshold are not demographically qualified, and people above that line are demographically qualified. Point accrual beyond this shows that some prospects might be better options and larger opportunities, but all demographically qualified prospects could purchase your product.

Activity Freshness Score. This is where decay scores matter. This measures how much recent activity a prospect has. It goes up based on how involved prospects are, and it goes down when they're not involved. This is a score candidate for hitting zero but not dropping below it. You would want to put a hard cap on the bottom of this score, because when this score gets into the negatives, it's misleading—someone who hasn't been active for 6 months would have to show 6 months of activity just to get out of the negative. Really we want to use this to selectively recycle leads at certain thresholds and then mark them as revived once they show more activity. The thresholds here push to a "Recycled" boolean, and you sort them in the Lifecycle Processing appropriately—put them into slower-paced nurture here, don't bug them as much. When their score changes from 0 to more-than-0, that's a great opportunity to assign tasks to sales people if they were SAL or SQL, or to trigger welcome-back emails if they were MAL or MQL.

(Also, there are profiling scores such as Buyer Stage Scoring, Persona Profiling, and Product Interest Profiling. These are more about putting people into best-fit marketing, so the scores are more inputs into profiling than important standalones. More info here.)

Cheers,
Edward Unthank | Founder, Etumos

[Josh_Hill13]

Interesting thoughts on this Ed.

I am a fan of negative scoring so I wouldnt bother with the logic for this. But I know some models are 0-100 scaled and must rigidly maintain that.

So you could say, in each campaign:

If Score>0, then ok to do x

or if a negative score, say If Score > 5, then -5 points. otherwise, do nothing.

[Justin_Norris1]

Sarah Mayer

I think there's two overarching goals at play here.

1. Identifying the best leads: How can I tell which leads are most likely to buy/most qualified/best fit for sales? (whatever your intent in scoring is)
2. Driving the right action: How can I best communicate that information to sales so they take action on the leads I am prioritizing for them?

In terms of identifying the best leads:

I personally don't fuss too much about negative scores if you have a relative scoring model.

For example, I have built models that were more around predicting likelihood of purchase (using historical data) vs. identifying who is someone displaying interest. (Think of it as a poor-man's predictive lead scoring model. )

With that data-driven approach, negative scoring becomes as important as positive scoring and the entire score can dip significantly below zero. It doesn't really matter because it's more about where a lead stands vs. it's peers than an absolute raw score.

However, if your model is more about just delivering interested qualified leads to sales then I think Edward Unthank​ makes some good points about how to think about the different scores. I really like the idea of having "freshness" as the third leg of the stool in addition to demographic and behavioural.

In terms of driving the right action:

Unless you are using an absolute scale of 0-100, I have found that displaying the raw score to sales reps has not been very effective. Even if you prevent it from going below zero, how should a rep feel about a lead with a score of 37. Is that good? Bad? What if another lead's score is 256. What does that mean? It's not very intuitive.

The best results I've gotten in terms of buy-in from sales have been from converting the raw score into a "Lead Grade" (like a grade on your term paper) and displaying that grade in SFDC using big colour-coded letters, like so:

Honestly, the difference was remarkable when we changed to grades from raw scores. The sales people really started to "think" in terms of the grade, and would get excited about an "A" lead, whereas the raw scores were mostly met with confusion.

I know there are also stars and flames with MSI but I never had huge buy-in to those, whereas a big colour-coded letter (they shade from green to red) really seem to resonate on an intuitive level.

[Anonymous]

Justin, thank you for your input! I agree with the Grade Scoring. We actually just partnered with SalesPredict, and they offer the graded scoring as well. I’m hoping it will work wonders for our Reps.

Sarah Mayer

Marketing Operations Manager

MarcomCentral

858.847.6638 direct

858.847.6600 office

<http://marcom.com/>

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[Anonymous]

Edward – excellent points! I’m going to look into these other types of scoring and see what could work best for our reps. I appreciate your detailed thoughts on this!!

Sarah Mayer

Marketing Operations Manager

MarcomCentral

858.847.6638 direct

858.847.6600 office

<http://marcom.com/>

This email message is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. If you are the intended recipient, please be advised that the content of this message is subject to access, review and disclosure by the sender's Email System Administrator

[Anonymous]

Hi Edward!

I'm getting ready to implement your Activity Freshness Score, but I realize that this custom score is not available in the "Change Score" flow step. How do you handle this?

Thank you!

Sarah