We scrub the data you close the deals
Discover more opportunities and drive more prospect engagement. Level up your deal team with the most accurate data set available.Lets talk
As a dealmaker, you want to see all of the potential investment opportunities available to you. But you also know that there simply isn’t enough time in the day to actively pursue each and every one.
For many, creating investment theses is a great first step toward maximizing efficiencies. But even after identifying the types of companies your firm should pursue, dealmakers still need to understand the precise combination of qualities that make up good, better, and best investment opportunities, or leads.
In tech, successful business development (BD) teams use a tactic called lead scoring to determine what makes up their “ideal customer profile” (ICP), or which leads are worth prioritizing. Lead scores are also helpful for mapping the progression of qualified opportunities through your M&A pipeline.
If your firm isn't currently using a lead scoring model to make your deal flow process more efficient, don't worry. We'll go over everything you need to know, including criteria and methods, as well as some examples and tools, to help you create your own models.
Lead scoring is a practice that marketing and BD teams use to assign numbers to leads in your funnel. These scores are based on values given to certain attributes the lead may have (e.g., company industry, employee title, organization size) as well as actions the lead may take (e.g., website visits, content downloads, sales engagements). Generally, a higher score means the lead may either better fit your firm's investment thesis and/or is more engaged with your firm's marketing and BD activities.
There are two main parts of a lead score: the criteria you use to assign values to your leads and then the values themselves. As you can see in the screenshot below, in its simplest form, lead scoring is based on an if/then model. If your lead has a certain attribute or takes a certain action, then you either add or subtract a value from its score.
Lead scoring is a highly customized part of your deal flow process. No two firms' lead scoring models will be the same and will differ based on what you find best represents an ideal lead. On top of that, your model will also change depending on what technology and data your firm has available.
Regardless of what models you end up creating and using, lead scoring helps your firm be more strategic in its approach to the market and move from being reactionary and generic to being proactive and thematic. When your firm focuses on leads with the best fit, highest potential returns, and greatest propensity to close, you can ensure your BD team is spending its time most efficiently.
Lead scoring models are the set of "rules" that determine how your firm scores its leads. As we showed above, these rules are essentially if/then statements that include your criteria and what happens to a lead's score when that criteria is met.
Some common criteria for lead scoring models are:
Lead scores themselves take different forms depending on how your firm sets up its values. Some models take a 0-100 approach, and others will use a much larger scale to allow for more nuance. At the end of the day, your firm's lead scoring model can be as simple or as complicated as you need it to be.
A full lead scoring model also includes what to do with leads when they hit certain thresholds, whether that's routing leads to your BD team, placing opportunities into specific deal flow stages, or even triggering automations to place leads into different tracks (e.g., an email nurture sequence).
That said, your model should never have leads categorized into just two buckets — e.g., "hot" and "cold." Not only do those labels not adequately indicate leads' value to the firm, but you should also have many more thresholds to effectively route leads to the appropriate path based on their fit and engagement with your firm. Additionally, until that lead is actually closed, your lead scoring model should never stop evaluating and updating the lead's score.
While not a complete lead scoring model, the above example shows both how lead scores are impacted by the interaction that a lead has with this email drip campaign as well as how this company uses tags to filter leads. By integrating your customer relationship management (CRM) system into your lead scoring process, you can easily set up this type of branching model.
Up until now, we've mostly discussed the traditional method of creating a lead scoring model: static models based on criteria and values that your firm knows signify a good lead. With artificial intelligence (AI) and machine learning, however, predictive lead scoring models are starting to become more prevalent.
Predictive lead scoring models look at what information your won opportunities have in common, as well as what information your leads that did not close deals have in common. A predictive scoring algorithm will then automatically score your leads based on that criteria. This automated system cuts down on time spent tracking down leads and helps find better deals.
However, as with all AI, it's important not to let it dictate everything involved with how you score leads. AI can help you sort through massive amounts of data far faster than a human can, but its results need to be regularly checked by a human. Ideally, a predictive scoring algorithm will be just one of the many formulas your firm uses to judge leads.
To help your firm understand how a lead scoring model functions, here are three examples of leads and the values they might be assigned based on their attributes and activities:
Lead Scoring Model Example #1
Action: Assigned to sales (based on score >100 + "High Interest" tag)
Lead Scoring Model Example #2
Action: Sent to nurture (based on score between 0 and 20)
Lead Scoring Model Example #3
Action: Flagged as needing right contact (based on "Bad Contact" tag)
Through a combination of scores and tags, your lead scoring and routing process can get incredibly nuanced and allow for many efficiencies through automation. Building a flowchart that designates what paths leads can take can be incredibly helpful to ensure leads don't fall through the cracks and that your team only spends time on the highest-quality and most interested opportunities.
When creating lead scoring models, it's important to use all the tools available to you. For firms, this includes a deal sourcing platform, a CRM, and a marketing automation platform. As we stated above, a good deal sourcing platform will have unique information for your particular ICPs, the ability to assign scores, and a way to push those values to your CRM for further action.
The CRM, where all your contact and company info should reside, will most likely be where the majority of the rules in your lead scoring model are actually enacted. A good CRM will not only score leads based on the information it has and has been given by other tools in your tech stack, but it will also properly route leads through your deal pipeline and drive the automations you set in your model. Just be careful your different systems aren't assigning values based on the same criteria, or you risk having inaccurate scores.
Some firms will also have a marketing automation tool (MAT) that helps track lead engagement. MATs are also where your marketing team sends out email nurtures, marketing campaigns, and newsletters. If you've included any of those as actions within your lead scoring model, you'll want to ensure your MAT is integrated with at least your CRM, if not the rest of your tech stack.
Over time, your lead scoring models should continue to evolve. Just as your investment thesis changes as your firm refines its strategy — or reacts to outside factors — so too should your lead scoring models reflect your firm's current focus and the most recent market dynamics. Business intelligence (BI) tools can help your data scientists better understand what makes up a good (and bad) lead and drill down into what criteria and thresholds make the most sense.
As you make updates, test them against real-world scenarios. By analyzing past leads and assigning them a score based on your updated model, you can check that your leads will be scored and acted on appropriately.
But first things first: before you can create a lead scoring model, your firm's tech stack needs to be in order. Download our guide to learn what's inside the modern dealmaker's tech stack and set your firm up for lead scoring success.