Hi - I'm interested in hearing some pros and cons to using each of these lead scoring models. I have limited experience implementing lead scoring, but have been introduced to the Sirius Decisions model. It seems complex to set up and possibly to maintain if you have changes, but (and I'm speculating) will more accurately produce MQLs. Does anyone have any experience or insights they could share?
Thank you!
I would suggest working backwards from what you picture a QL looks like on your own. Scoring models are a rough template, and the more time you can spend personalizing the scoring system to your needs the better. Set a QL threshold and then step back - how many ebook downloads would qualify if they did nothing else? How many high quality pageviews would qualify if nothing else? What about webinar attendees and non attendees? What about trial downloads if you have them? Every action a lead takes should have an associated score.
Building out examples of how you would want leads to score to hit the threshold when you want them to is a good way to begin behavioral scoring. I would try to get a firm grip on that before trying to incorporate any demographic scoring either - we use almost no demographic scoring and instead have a very sophisticated behavioral scoring system.
I am also unfamiliar with Sirius Decision's specific scoring model, but what I'm describing lines up pretty closely with the Marketo model. If you set it up carefully there should be almost no maintenance unless your sales team is coming back saying they're either not getting enough QLs (might indicate low scoring) or they're getting QLs who aren't ready (too high scoring).
I haven't heard that either firm had a "scoring model". If anything, I'd say they were likely the same since Marketo built the product to operate in a similar fashion to what Sirius Decisions recommends.
Please see some of my thoughts here:
You searched for scoring - Marketing Rockstar Guides
I agree with the others that YOU have to decide what works in your business and define these scores properly for your MQL level. I would consider the various Lead Lifecycles that both firms recommend and then do what your team agrees on.
Maybe "model" is the wrong word. There are importable Lead Scoring programs to start from - Sirius Decisions being one of them. We have our scoring parameters and have decided on what makes sense to MQL. I was just looking if anyone had any opinions on either of these importable programs.
I wouldn't use either of the import programs themselves - import both of them if you'd like to see examples, but I would still build the campaigns from scratch rather than using one or the other. There's no 'right' answer for lead scoring - every company ends up scoring differently based on their situation.
Agreed.
Frankly, lead scoring is becoming passe. You should focus on predictive or the behaviors that are precursors to real MQL>SQL leads. Obviously that requires a lot of analysis, which you may not have right now. If you think about it - regardless of any model -- how many clicks, form fills, etc can qualify someone? Does it make sense that 3 whitepaper downloads=MQL? It might...and it might just be damned annoying to the lead who isn't really ready. I've seen firms where 2 clicks=MQL and 1 visit to the Pricing Page=MQL.
The point is don't fret over the Model. Figure out how to best serve your audience.
We consulted with Sirius Decisions and adopted their "model" for lack of a better word. We did a lot of leg work to determine what factors we wanted to score on and how they should be weighted to fit our business model. The main difference between Marketo's scoring best practices and Sirius Decisions is that Sirius Decisions recommends a maximum of 100 points for Demographic and 100 points for behavior. You then lay it out on a grid (x axis is demographic and y axis is behavior). You decide if each square should be a qualified lead or not.
The other difference is that you degrade your scores monthly - so that you won't leads pushing over from having a lot of activity 6 months ago and then revisiting your website pushes them over. If they are not engaged that month, they reset to 0 for behavior. It did involve a lot of work with our Marketo consultant to get the system to work with Sirius Decisions best practices but we got there in the end. However - we have run into the problem of leads syncing over based on degrading scores.
Chrissy,
I am in the beginning stages of implementing a modified version of their model and would love to connect with you. I'd like to understand your experience so with the model so far. Thanks
Enrique
Absolutely - let me know what questions you have and I'll be more than happy to help.
I get the following message when I try to Import this model.
Any ideas on where in Marketo I should enter this field?
We created a custom score field called Explicit in our instance by going into Admin > Field Management > New Custom Field.
I had this issue and support had to fix something on the backend for it to work.
I think part of the problem is that Marketo changed Lead to Person and didn't update document. Error still occurs when creating a Custom Object. I will file a support ticket.
Here are my pros/cons for this question. However, I first want to say that I'm not familiar with either of these scoring models.
Pros: Models are preset, predefined, and many users use these because the thought/logic has already been put in place for you to follow. You should be able to get a lot of support and answers for questions that you may have in regards to the model.
Cons: IMHO - Scoring models are useless unless you have an idea of what you want your model to look like. Models are based off of scoring, meaning that the actual process of scoring informs the model. Therefore, your model can be anything that your score informs it to be. You just need to understand scoring. Scoring can be as simple or complex as you'd like, but you yourself need to understand the scoring logic, thresholds, engagements, etc ---> which make the model. Bottom line here is, since you're not familiar with lead scoring; you can learn the model or you can learn how to score and therefore understand how models are created.