The correct use of lead grading helps you to disregard those prospects entering your database who do not fit your customer profile. For example, if you only service companies in the United Kingdom and somebody from America enters your system, they could be assigned the lowest grade and subsequently ignored by your lead distribution automation. This is despite their lead score, which may be high if the prospect is particularly engaged and active.

The ideal lead scoring model The general rule then, is that you want A+ grade prospects that have tallied up enough points that they are flagged in Pardot and pushed into Salesforce as being at the bottom end of the sales funnel – in that Consideration layer where they are looking at vendors and making purchase decisions. This is the ideal scoring and grading model.

Challenges of Pardot Lead Scoring Theoretically, this all makes complete sense (I hope!). In practice though, we’ve found that many Marketing Managers face challenges such as: How do I know how many points to assign per action?

What should the total lead score be before the leads are flagged as sales-ready?

I don’t have complete/fully developed buyer personas, how should I grade prospects?

What do I need to know when setting up an automated scoring and grading model in Pardot? I’ll tackle each of these one-by-one.

How do I know how many points to assign per action? I’ll be the first to admit there is an element of trial and error with this. Pardot does actually have a default baseline scoring model that you can use to take the hassle out of creating your own scoring system but as this is a generic system I’d suggest tailoring your own to fit your business and objectives once you have the data to do so. Upfront, the most important thing to do is to analyse those prospects who have turned into customers and understand their journey because this will give you an initial idea as to how you should weight the scoring. For example, if viewing a specific product page was a key part in their conversion then this will need a high score. You’ll also want to identify the engagement and conversion actions on your website that matters most to the business. Contact us pages tend to be weighted higher than a generic blog post, for example. Start by reverse engineering your converted prospects and making a list of all the valuable engagement and conversion actions for your site and any communication channels such as email, then map these out in order of importance. They might include: Certain page views

Time spent on the website

Number of visits to the website

Content downloads

Attending an event

Use of Live Chat

Form enquiries

Email opens

Email clicks

Email responses

Video view completion rate Once you have a clear map of what you want to score points for, start to draft a points value next to each one based on the journey that your customers took. Think about what actions contributed to the conversion. Did they download a product whitepaper and purchase straight after? Or were there multiple actions taken between the download and the purchase? Taking the time to map your lead scoring and grading model is a key step in aligning marketing and sales teams. It’s an iterative process that is constantly evolving into a more accurate representation of the quality and engagement of prospects so understanding that this is completely bespoke to you is key – there’s no template! Once set up though, you can automate your lead distribution and expect marketing and sales processes to become a lot more streamlined and efficient. A massive benefit to remember is that once the scoring and grading model is in place, your sales teams can work with marketing to define what an MQL actually looks like and they can use actual metrics to do this. For example, they may say a prospect with over 50 points in a particular scoring category with particular customer profile is an MQL and should be approached, therefore they can receive notifications in real-time when this happens!

What should the total lead score be before the leads are flagged as sales-ready? The answer is, it’s completely contextual. Although, keeping it simple helps so most clients opt for 100. As I’ve mentioned though, once you set a threshold for triggering your sales-ready leads, using the method above, it then becomes about assessing and adjusting your Pardot lead scoring approach by focusing on the quality of leads being followed up. It’s extremely important to work with your sales team on defining what a sales-ready lead (MQL in Pardot’s eyes) actually is. The reason for this is because they’re the ones who will actually be receiving your leads! So, arrange a meeting with them, get their feedback and incorporate it into your approach. Of course, sales will always tell you the best-case scenario so be realistic with their feedback and push back on them if they’re asking too much. Communicate with your sales team and work together to get your total lead score threshold right for your business.

I don’t have complete/fully developed buyer personas, how should I grade prospects? Perhaps you only have partial buyer personas or even just basic demographic information you work with for marketing. If that’s the case, my first suggestion is, of course, to try to develop full and documented buyer personas because for any inbound marketing strategy to be successful, completely knowing your buyers is paramount. Otherwise, set up a generic profile where you can pick out information and grade based on the data that applies to all of your personas. Things like revenue, employee size, job title tend to work quite well for a generic profile. You can get more granular with your profiles when you have more data!