Contextual Lead Scoring Algorithms

Our lead scoring frameworks use numerous data points across the marketing tech stack to make lead scoring algorithms increasingly contextual.

In our lead scoring framework we use numerous data points that are being tracked across the marketing tech stack to make our lead scoring algorithms increasingly contextual and drive best in the class qualification. Lead Scoring basically acts as a safety valve, which makes sure that tele-marketing and sales teams are being sent the most qualified leads at the right time. Given the extent of data points that can be acquired and tracked in modern marketing automation and CRM platforms, lead scoring algorithms have become increasingly sophisticated.

To make the most of such data points provided by the modern marketing tech stack, we have designed a 3-dimensional lead scoring framework, which takes into account the demographics and behavior of the engaged leads to decide when they are ready for a conversation with sales teams and when it is appropriate to move such leads into nurture streams. Combination of these 2 dimensions gives the 3rd dimension, which is the actual lead score that is used to prioritize engaged leads for the follow-up by the sales or tele-marketing teams. Our approach to lead scoring is powered by a completely customizable algorithm that is built to make sure that every lead, which engages with marketing campaigns is qualified on the 2 fronts:

Demographics (alignment with buying persona for products/services)
Engagement (actions with the demand generation programs that signal buying intent rather than just browsing/learning)

Our Approach to Contextual Lead Scoring

At marqeu, our approach to lead scoring is powered by a detailed analytics and reporting process to enable marketers to continuously track and fine tune the lead scoring model on an on-going basis. Designing the lead scoring framework is a step by step process where in alignment with sales teams is extremely important. A typical lead scoring engagement involves:

Understanding the buyer persona and actions, engagements that signal buying intent and driving consensus with the sales organization.
Data gathering in marketing automation platform, CRM, which defines the data points to qualify demographics and buying signals.
Review of the existing data capture processes to align with the needs of the new model.
Review of the data quality and completeness of the existing records in terms of the key data points needed for effective scoring. Documenting recommendations for database normalization and enrichment.
Designing workbooks and simulations to demonstrate how the proposed lead scoring would work in the real world to measure the impact of every single data point towards qualifying a lead for follow-up with the sales teams – we have seen these simulations do the magic when the stakeholders are able to see lead scoring work in a tangible way.
Building, testing and deploying the actual lead scoring model in marketing automation system.
Documentation and training for the teams so that they have a complete understanding of the lead scoring algorithm and are able to differentiate between the leads with different scores.
Regular review of the models by advanced data analysis of the scored leads (and their follow-ups) to continuously optimize the system and align with the feedback from the sales executives on the qualified leads

Demographic scoring is the dimension in our lead scoring algorithm that accounts for the “alignment” of the engaged lead with the “ideal” persona of the buyer. The commonly used data points in the algorithm are below and they can be weighted per the importance of each of the data point in the overall persona of the ideal buyer for a particular organization. This flexibility in our model is what makes it truly customizable for the uniqueness of the business for which is being tailored and from there on, it can be adjusted based on the insights captured by the algorithm once it is in production environment. Some of the data points that are commonly used for demographic scoring include:

Job Role/Level – helps in weighing decision makers and practitioner separately.
Company demographics – SMB, Mid-Market, Enterprise based on revenue, number of employees.
Industry – for some products and services are more tailored towards specific industries and at times organizations have strategies focused on few key industries like healthcare, finance etc. In such situations, organizations like to prioritize the leads coming from such industries.
ABM/Target Accounts – engagement from target accounts is weighted more as compared to other accounts.
Customers vs Prospects – New business opportunities are weighted separately vs cross/up-sell

These are just some of the commonly used demographic data points and there is no limit to the number of data points that can be included in the model. As part of the lead scoring engagement, we work with you to understand and define those data points that are most meaningful for your business when it comes to your buyer persona.

Engagement scoring is the dimension in our lead scoring algorithm that accounts for the “alignment” of the engaged lead with the “buying behavior” of the buyer. This dimension helps rank the ideal buyers on the basis of her web behavior and engagement with the marketing campaigns. Since each campaign has a different goal (awareness vs demand generation vs pipeline acceleration), so should be ranking of the lead who engage with different kinds of campaigns. The commonly used data points can be weighted per the importance of each of the data point in the overall marketing mix. This flexibility in our model is what makes it truly customizable. Some of the data points that are commonly used in engagement scoring include:

Engagement with high value content – we work with your teams to categorize all marketing content so that we know what are those key assets, engagement with which indicates that buyer is ready for a sales conversation and should be routed to the right sales rep as soon as possible with notifications for rep. Assets like pricing pages, industry specific product features, new customer wins, case studies, registrations for in-person marketing events, annual conferences are generally considered as high value engagements and can be scored higher in our algorithms.
Repeated web visits – to the high value content pages vs careers, investors, news pages are scored higher.
Engagement via form submits for tactics like webinars, in-person events are scored higher as compared to engagement with just email opens

As part of the lead scoring engagement, we work with the marketing teams to understand and define those data points that are most meaningful for their business. We then use these insight to create an excel simulation of your lead scoring model that can be tested to see the lead scoring model in action. We get the feedback from the simulation and make any needed changes and then we implement the finalized algorithm in marketing automation platform.

We believe in keeping things simple for the sales teams so that they are able to act on the key insights coming from the lead scoring algorithm and are not spending a lot of time deciphering the information behind the lead scores. We, at marqeu, consider this as the primary objective of a lead scoring algorithm. This is where our 3rd dimension of the lead scoring model comes into action. We plot the demographic (1st) and the engagement (2nd) scoring dimensions on a 2 dimensional axis and come up with what we call as “Lead Scores“, that usually consist of 1 letter form A to E and a each letter signifies different level of readiness of the engaged leads. Usually, leads with the lead score of A are the hottest leads and need immediate follow-up from the sales teams and the readiness varies for each subsequent letter.

These are just some of the commonly used approaches to come up with a simple lead score that can be easily understood and acted upon by the sales organization. As part of the lead scoring engagement, we work with you to define the lead scoring system that is most meaningful for your business. We then use these insight to create a simulation of your lead scoring model so that you can test it in action, make the needed changes and then we will implement it in your marketing automation platform.

Let’s discuss how we can leverage campaign performance data to optimize your lead scoring algorithms.