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How to prioritize marketing leads via automated lead scoring & grading

As a B2B marketer, attracting many inbound leads via marketing activities is great, but when attracting (too) many leads, it is also harder to prioritize which ones to reach out to first and to have your (inside) sales / business development teams spend their time with the right leads. One of our recent analyses of the inbound lead funnel at Celonis has shown us that a short versus a high time to touch (sales follow up) increases the likelihood of converting a marketing lead into a sales qualified lead by more than 100%. So how can you as a B2B marketer ensure that your sales team knows which are the most valuable leads to contact first?


Lead scoring & grading is a concept to help solve this problem. This article will introduce you to the basic ideas of scoring & grading and will give you some insights into some of my personal learnings & tips for B2B marketers.


What is lead scoring & grading and why is it useful?


Lead scoring and grading is a marketing automation concept to prioritize inbound leads for your sales team and to classify existing contacts in your CRM database. It is especially helpful to define your Marketing qualification (MQL) process and the threshold for passing leads over from marketing to sales. 

How to prioritize marketing leads via automated lead scoring & grading

Lead scoring


Scoring relates to reactions and engagement of a lead with your (marketing) content and DemGen activities (how interested is the lead in your company or product). The score of a lead is normally displayed in a number and should be visible to the users of your CRM database - the higher the scoring number, the more interested the lead is in your company/ product (this information is not only interesting for sales & marketing but also for e.g. your customer success managers). 


Typically, the following scoring activities can be captured after form submissions & cookie consent via your marketing tools (if you do not want companies to know about these activities, you should definitely always turn off the marketing cookie within the cookie consent page of a website):

How to prioritize marketing leads via automated lead scoring & grading

Besides positive scoring, also negative scoring categories should be considered, e.g. when someone opted out of your marketing emails or if the activity indicates that a contact is rather interested in a career at your company than potentially buying your products.


Activities should be valued differently representing the worth and time spent of that activity, e.g. the attendance of a webinar is typically worth more than a click on one of your marketing emails. Furthermore, the definition of scoring categories should always be based on the conversion rates of the touchpoints into SQLs/ opportunities and deals. The higher the likelihood of an activity resulting in an SQL the higher the score should be. 


The following table gives an example of how the categorization of different activities could look like:

How to prioritize marketing leads via automated lead scoring & grading

Lead Grading


Lead grading relates to the demographic information of a lead and gives you the possibility to separate your target audience for your sales teams from inbound contacts that your sales team shouldn´t touch (how interesting a lead for your company). The grade of a prospect is often displayed in a letter (e.g. A-F) and can incorporate the following dimensions:


Lead grading categories B2B marketing


The lead grading automation logic should follow your Go-To Market strategy, e.g. if your product is only available in Europe and targets the needs of SMEs, leads from Fortune 500 companies outside of Europe should receive a grade decrease because you won't be able to sell to them. When setting up this logic it makes sense to have a closer look at your past sales & opportunity data to determine criteria that lead to higher conversions of closed deals. A scenario could be that you intended to sell your website analytics software to marketers but instead, your sales data reveals that web developers were the ones that actually converted better throughout your sales cycle. Subsequently, you should grade web developers higher than marketers.


Interpretation of lead scoring & grading scenarios


Bringing lead scoring and lead grading together is an art and requires a lot of testing, adjustments and calibration, and close collaboration with your (inside) sales team.


The following section gives you an overview about how to interpret the combination of the scoring and grading categories you defined (what is a high score or high grade obviously is up to how you define it as outlined in the previous sections), how to define follow up tactics and how to come up with an MQL definition.

How to prioritize marketing leads via automated lead scoring & grading

Low grade 


Leads with a low score should be excluded from your database or parked in a queue. Ideally, you set up your grading in a way that a low grade clearly defines leads that don't fit your target persona at all. Examples could be students or applicants that inform themselves about your company. No matter how high their score, you don't want them to be handed over to your sales team.


Medium grade - medium score; High grade - low score


Leads with a medium grade and low to medium score as well leads with a high grade and a low score require further nurturing, meaning you need to get them more engaged until they are ready to be talked to by your sales team. Ideally, you engage them further with customized content like customer success stories, industry specific content, webinars with competitors etc.


The MQL score threshold for leads with a medium grade should be higher than for leads with a high grade. E.g. if the ideal potential customer is signing up for your free trial, you should not miss the chance to inform your sales team about this lead. As your sales team has much better ways to engage in a personal way you shouldn't keep them in your marketing database without a sales touchpoint.


On the other hand also someone with a lower, maybe less interesting job title who signs up for your free trial could be interesting to reach out to. From your past experiences & data you know that the person could be testing your product on behalf of his or her manager. Therefore, you might first want to further qualify his or her interest with a higher score until you know it's worth to follow up with him or her.


Medium grade - high score; High grade - medium to high score


These leads are ready to be handed over to sales and the result of your marketing qualification process: Marketing Qualified Leads (MQLs).


My experiences & learnings with scoring & grading

How to prioritize marketing leads via automated lead scoring & grading

Keep it simple


All of the above sounds like scoring & grading is a pretty simple concept, but if you are implementing the underlying automations you will understand pretty quickly, that it's actually not that easy. Everyone who has worked with automations before knows that multiple factors in automation rules can make the interactions of these automations quite complex, especially if you need to use fuzzy rules or similar. For example, “contains” rules for job titles can do unintended things: a downgrade for “Intern” will suddenly result in a downgrade for “International Head of Marketing”. Believe me, there will be quite some surprising results of your automations along the way.


So, even if it might be tempting to engineer the perfect concept on paper that takes every aspect of the available lead information into consideration, I highly recommend from my own experiences to keep your scoring & grading rules simple and basic. Especially for the grading mechanism, you should not use more than 2 criteria to classify your leads. There will be questions around why a certain lead received which grading and for you (as the automation owner) as well as for everyone else it is better if you can still explain and understand yourself how the grade with multiple upgrades and downgrades came together.


Define MQL thresholds & auto MQL activities


When setting up your scoring & grading automations you should define a clear threshold for when a lead becomes an MQL. It makes sense to define auto MQL actions in place, e.g. everyone who attended one of your webinars or submitted a contact form should automatically receive the MQL status (if they do not have a low grade of course). Share and enable your Inside sales team around the MQL threshold and the auto MQL activities and make the rules available for everyone to read through.


Monitor closely & review regularly


After you have set up your scoring & grading logics, you need to have a close look at the actual results of the automations (the scores & grade of your leads). You should have a fixed review and launch cycle for updating your scoring & grading logic. In the best case, you gather the feedback from your (inside) sales / SDRs / BDRs and look at the MQLs and their conversion rates on a monthly/ quarterly basis to identify possible improvements. Changes in the automation should be treated like releases in development, meaning that testing upfront is crucial. The steady revision and fine-tuning is the key to a successful MQL process.


Be careful with email scoring


Companies often use security bots that check if incoming (marketing) emails can be trusted and do not include harmful links. These bots go through every email and click every single link of that email to make sure they are sage. In a scenario where you score based on email clicks, this can lead to a very high lead score without any actual real engagement. SDRs/ BDRs will experience a lower interest than expected.


In my opinion, it makes more sense to score the arrival on the website or content the email CTA is directing to (e.g. a blog post or a whitepaper) instead of the email click itself. This way you ensure that your lead has actually completed the action that actually provides value to the user.


Use Scoring for database segmentation & email marketing


Scoring can be super useful for your email marketing strategy and the segmentation of your database. It will help you understand how engaged a lead already is and which type of content he or she should ideally receive in the next step of your nurture programs (TOFU, MOFU, BOFU). Segment your email database into low, medium & high engagements and build your nurturing and email tactics as well as target KPIs such as open rates and CTRs around these segments. You will see much better results with this approach than just blasting out the same content to everyone no matter how engaged they are.


95 - 5 Rule


Ultimately, your goal with the implementation of scoring & grading is to make sales spend their time on more relevant leads and subsequently to shorten the sales cycles to increase the conversion rates from MQL to SQL, MQL to opportunities, and MQL to closed-won opportunities. So what you want to see is an increase in conversion rates from before to after scoring & grading. Let me tell you: The scoring & grading automations most likely will not ever be perfect, there will be single cases where the automations don't make any sense, Sales will be complaining and doubting the scoring & grading system BUT after all, you need to see the bigger picture and this is only visible when looking at it at scale. Work towards a 95% confidence level of your automations, accept the outliers and be prepared to defend the 5% against the stakeholders.

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