How PE Can Leverage Lead Scoring Models

How PE Can Leverage Lead Scoring Models

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February 18, 2021
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Modernizing private equity firms’ approach to deal flow goes beyond new tech stacks and strategies. When it comes to the daily actions of partners, a more scientific approach towards potential new portfolio companies or add-on acquisitions can reap huge benefits. Closing a deal is really a matter of a sale, and anyone near sales teams knows how people can sometimes miss great opportunities, or get target fixation on deals that just won’t work. 


Lead scoring prevents this, and scales your teams expertise so that time is spent where the potential is highest. Don't let market discovery slow your deal origination initiatives, 

SourceScrub has the research so you can focus on closing more deals. Continue reading to learn more about how private equity and venture capital firms can leverage lead scoring models to revolutionize your deal sourcing and tracking efforts. 

What is lead scoring? 

One of the seven strategies of New School Deal Flow is lead scoring, or the process of scoring the viability of prospective investments. Lead scoring is the process of assigning value to each lead generated for businesses, usually based on a numerical point system for ranking to determine sales-readiness. Traditional lead scoring adds and subtracts value based on how certain properties of a lead meet criteria. Predictive lead scoring uses an algorithm to determine if leads in a database are qualified or not qualified based on a number of criteria:


  • Information that leads provide
  • Behavioral data
  • Social information
  • Demographics
  • Media written about the company


The goal of lead scoring is to uncover potential sell-side and buy-side opportunities, and   maximize your deal origination efforts by enabling your ability to better landscape the prospect playing field and quickly identify the right investment opportunities. Lead scoring models leverage key firmographic data points that meet your ideal investment thesis and apply them as filters against an extensive dataset of companies and sources.

Do VCs & PE firms need a lead scoring model? 

Private equity and venture capital firms are experiencing an increasing level of competition in their respective markets. The influx of capital as well as the increased transparency of private markets is helping drive an increase in competition for deals. Operators and owners of privately held companies are being pursued by investors and advisors at increasing rates. 


For investors, this creates higher valuations and squeezes investor returns. For bankers and advisors, the increased sophistication of owners marginalizes their value squeezing service fees. This means purveyors of private equity deals can no longer afford to wait for deals to come to them, but sponsored deals are expensive and undifferentiated, both of which lead to mediocre returns, proven by 10-year PE returns trailing the 10-year public market indices. 


Remaining opportunistic and non-thematic makes it difficult to create a differentiated approach where you can build expertise and help create value with operators. A blanket approach prevents organizations from being strategic and disciplined in what deals they  pursue and how they pursue them. A lack of strategic depth and insight limits the ability to win clients and build meaningful relationships with operators .


Lead scoring models allow VC and PE firms to not only identify and target prospective leads, they help close more and better deals. 


Lead scoring leverages technologies and data sets that allow firms to take a structured, data-driven approach to their deal making. By doing so, firms can be more strategic and differentiated in their approach to the market. They can move from being reactionary and generic, to being proactive and thematic. Thus, firms can create and pursue unique perspectives and experiences in the market, which helps investors not only land deals but also add value in their portfolios. Lead scoring models are especially useful for sourcing add-on acquisitions and management buy-ins. 

Lead Scoring for Add-On Acquisitions & Management Buy-ins

An add-on acquisition is when a private equity firm or other buyer acquires a company and integrates it into an existing company within the buyer’s portfolio. Add-ons are generally strategically positioned and sought out to add value to the portfolio or use them to stimulate growth inorganically within a portfolio company. Many PE firms use add-on acquisitions to stimulate the development of a company that they have interest in growing and increasing the value of, with the goal of taking it public or selling it for a greater return on their investment. Some of the benefits of add-on acquisitions include:


  1. Lowering costs by merging staff members with similar expertise 
  2. Expansion into new regions domestically or abroad
  3. More product offerings
  4. Cross-selling opportunities
  5. Consolidation of management
  6. Consolidation of finances
  7. Increasing buying power


Add-on acquisitions have been booming in 2020, most likely due to the lower risk they pose compared to previous investment choices that created the bubble of 2008. The typical strategy of add-ons is to acquire specific assets at a cost lower than it would take for the buyer itself to develop that specific asset. This practice of picking and choosing from specific assets and discarding others, or “cherry-picking”,  makes lead scoring even more important. To be able to identify which leads are the best prospective add-on acquisitions, you need a scoring model that can quickly evaluate and identify prospects that best fit your current position, goals, and objectives. 


Similarly to add-ons, a management buy-in (MBI) is the kind of transition that can make or break a company, and an investment. An MBI is when an outside manager or management team purchases a controlling ownership stake in an outside company, and replaces its existing management. This type of action can occur when a company appears to be undervalued or poorly managed. 


Like add-ons, MBIs require a level of insight to determine whether a lead aligns with your investment thesis and if it can be a worthwhile deal. A lead scoring model can be useful to help identify if there is more value in a company than previously thought, or if its value can be increased by changing how it’s managed. This ability to recognize and quickly seize a good investment opportunity that others previously didn’t, gives companies a chance at a brighter future, and can increase IRR. 

How to Leverage Your Current Tools & CRM when Building a Lead Scoring Model

To begin with, ask yourself what data you currently track, and what you should be tracking. Most are familiar with customer relationship management systems, or CRMs, but some are not aware of how to leverage it for lead scoring. A CRM is a valuable source of data that is the basis for lead scoring criteria You can use CRM to track every interaction a lead has with your company and others:


  • Contact forms
  • Search engines
  • Emails
  • Phone calls
  • Social media


Tracking these interactions provide the data necessary to score the quality of a lead based on their attributes and behaviors. 

How to Score Leads Like the Pros 

The first and most important step of lead scoring is to get clarity on exactly what the sales team needs out of lead scoring in order to more effectively prioritize leads and close deals. Traditional lead scoring means identifying scoring criteria and determining the right scoring weights assigned to them. Instead of trying to determine what properties should be included or how much to weigh each property, a predictive lead scoring model can save you a lot of time.


Predictive lead scoring models use algorithms to look at what information your prospects have in common, as well as what information your leads that did not close deals have in common. It then produces a formula that will automatically compile your leads for you to easily identify the most qualified ones. This automated system cuts down on time spent tracking down leads and helps find better deals.

How to Create a Lead Scoring Model

Creating a lead scoring model starts with three components: data, tools for obtaining and organizing data, and alignment of sales and marketing teams.

Data You Need to Create a Lead Scoring Model 

Data is the foundation of creating and operating a functional lead scoring model. There are three criteria sets of data used to score leads: Demographic, behavioral, and negative. 

Demographic Information

Demographics and company information are the first set of data and lead scoring criteria to identify qualified leads:


  • Company size
  • Industry
  • Age
  • Location
  • Ownership structure
  • Employee growth
  • Executive team
  • Investor/Board
  • Revenue size
  • Buyer’s lists
  • Transaction readiness


These kinds of attributes can help you understand and recognize patterns of behaviors, and remove outliers from your sales team's queue by subtracting points for prospects that fall into an industry or region that is not aligned with current investment goals and objectives. 

Activity & Behavior

A Lead’s interactions, or engagement with content is the second set of data and lead scoring criteria:


  • Web and social
  • Conferences
  • News and events
  • Revenue growth
  • Fill out a contact form
  • Download a lead magnet
  • Clicks on links
  • Emails opened
  • Register for webinar
  • Request a demo 


These types of activities can help indicate a  prospect’s transactional interest and readiness.

Negative Scoring

Negative scoring is the use of combined demographic and behavioral data to determine  which actions should warrant point deductions when the lead stops doing them. You should score behaviors and activity that show a lead may be a bad fit or lack interest in transactions by deducting points. Possible activities and demographics that that warrant negative scoring include but are not limited to:


  • The industry
  • The company size or annual revenue
  • Last email opened
  • Their email domain
  • Unsubscribing from email list
  • Visiting careers page
  • Reporting a company post as spam
  • Negative comments or complaints on a company channel


Some of this information is proprietary, while the rest is readily available in the public market space.  Everybody has access to market signals like buyers lists and news and events, while proprietary signals include information like private company info, which provides more insight into whether it’s a good lead. This is where M&A and deal origination platforms like SourceScrub come in. PE and VC firms can access proprietary information like revenue size and growth and transaction readiness to determine if they are a good prospective investment. This insight of data creates a more effective lead scoring model so you can close more and better deals in less time. 

Team Integration & Compliance

When creating a lead scoring model, it’s important to make sure all teams on the same page. Aligned marketing and sales objectives are vital to the success of creation of an effective lead scoring model. Marketing and sales need to communicate and establish criteria, goals, and objectives:


  • What indicates a lead is transaction ready
  • What indicates a lead has potential to be a good deal
  • When a marketing-qualified lead (MQL) becomes a sales-qualified lead (SQL) and passes to sale


Team integration and alignment becomes easier and more efficient when you utilize the right tools for easy communication and collaboration. 

Tools to Build a Lead Scoring Model

There are a number of tools PE and VC firms can use to create and operate a lead scoring model, whether they’re for people and task management, or tools to manage and collect data.

Project Management Tools

PM tools are exactly what they sound like, tools designed to assist individuals or teams to effectively organize and manage projects and tasks. Examples of features that many project management platforms have include:


  • Collaboration - Assign and complete tasks, add comments, and organize dashboards for easy proofing and approvals.
  • Documentation - Organize files for editing, versioning, and storing.
  • Evaluation - Track and assess productivity and growth with resource management and reporting
  • Planning/scheduling - Plan and delegate work all in one place with tasks, subtasks, folders, templates, workflows, and calendars


PM tools are helpful for any project, but they are especially important for large transitions like the aforementioned MBIs and add-ons.

CRMs, Analytics, & Tracking 

Tracking and analytics is the process of gathering, analyzing, and applying data, information, and reports related to the content prospects interact with online. Examples of analytics and tracking platforms are Google, SugarCrm, and Salesforce. 


Social analytics specifically focuses on the data obtained from content shared on social profiles and the social profiles themselves. Examples of social analytics are Hubspot and Mailchimp. Businesses use social analytics for a number of reasons:


  • Understand the metrics that matter most, such as engagement, reposts, shares, clicks, impressions, and sessions
  • Identify effective ways to increase brand awareness and reach
  • Resonate with your audience
  • Increase traffic to your other content and website
  • Increase conversions


Many businesses already use CRMs for sales, marketing, and management, which can easily be used to track and analyze data to help you create your lead scoring model. Typical features offered by a CRM platform include:


  • Lead management: Automatically or manually enter new leads into the CRM and track and analyze data about them.
  • Marketing automation: CRMs can automatically send marketing emails or publish social media posts according to a schedule. 
  • Sales automation: CRMs track customer interactions and automate selected business functions of the sales cycle that are necessary to follow leads.
  • Workflow automation: CRMs optimize processes by streamlining workloads to free up employees for more complicated tasks. 
  • Analytics: CRM solutions can offer built-in analytics tools for insights to analyze attributes in the data and target leads. 
  • Artificial intelligence: CRM solutions can offer AI capabilities built into their systems to help automatically recognize patterns leading to successful sales, which can help you build more accurate strategies for future deal sourcing efforts.
  • Individualized experiences: CRM can be used to create personalized and consistent experiences across various marketing channels to help increase conversions and brand awareness.

Lead Magnets

A lead magnet is what it sounds like; a tool to attract prospective leads by offering them an incentive in exchange for their contact information. The incentive is usually one that can help you gauge their interest for future transactions, while also evaluating their qualifications as a lead. Lead magnets usually come in the form of digital, downloadable content, such as a free case study, checklist, eBook, report, resource list, guide, whitepaper, workbook, video, etc. This is a helpful tool because the data derived from interactions like these are both the behavioral and demographic information needed for lead scoring. 

Data Aggregation Tools

Data aggregation tools are used to combine and organize data from multiple sources into one place, to derive new insights, without losing track of the data source. SourceScrub’s data enrichment tools do this to enhance workflow, using AI-augmented, human-audited company data. You can siphon data from the companies that matter by filtering search results to quickly identify top opportunities. Never again worry about soured or duplicative data polluting your system records. Integrations functionalities include SalesForce, DealCloud’s DataCortex, and SugarCrm.

Private Data Sources

Private data sources like SourceScrub offer access to private company information, conference intelligence, and data enrichment. With SoursceScrub’s deal origination and M&A platform, never again scrub a list, instead, search through the comprehensive list coverage to find accurate data sources relevant to your investment thesis.


  • Identify non-transacted companies as potential merger and acquisition targets
  • Procure and refresh firmographic and dynamic business metrics continuously to enhance your deal flow process
  • Rapidly analyze target business firmographics, including growth of employees, investment data and more
  • Proactively track updates to key public and private companies for stronger market intelligence and more investment opportunities
  • Maximize your sourcing efforts with human-audited company data points
  • Drive more quality management meetings
  • Synchronize merger and acquisition targets automatically into your CRM or export into Excel
  • Access contact information for key decision-makers
  • Analyze company information for conference participants using data management and data scrubbing tools
  • Identify your best leads at the conference in-advance
  • Enhance your data quality by leveraging tens of thousands of lists from domestic and international events
  • SourceScrub’s solution takes the headache out of attending a conference. Gone are the days of wasting time and money on traveling to tradeshows and hoping to initiate ad-hoc meetings and interactions.


Lead Scoring Model Example

Each company has its own uniquely designed scoring strategy that fits to their particular objectives and targets. SourceScrub is the leading deal origination M&A platform, complete with strategies based on data, technology, teams, and lead scoring to find and close more and better deals. 

Lead Scoring Best Practices (& Common Pitfalls) 

A lot goes into the creation of a lead scoring model. Like anything else, there are certain pitfalls and the best practices to help you avoid them.  

Best Practice: Data Scrubbing

Since data is the basis of lead scoring, the entire model can suffer from unscrubbed data. Dirty or unclean data is loosely defined as poorly entered, improperly maintained, non-normalized or out-of-date information. If the data is not accurate, then the score won’t be accurate either. A part of lead scoring is doing the due diligence to verify that data is accurate, consistent, and reliable enough to bet a prospective deal on. This can be achieved when you standardize your process and track what you use.


SourceScrub makes this easy. Private company information, conference intelligence, and data enrichment tools provide access to data that has been confirmed to be reliable and ready to use to score prospective leads.  If you find a list we don’t have, you can leverage our data scrubbing services with a team of 500+ researchers and get it scrubbed on-demand. 


Best Practice: Implicit & Explicit Scoring

There are two kinds of scoring information you get from leads: implicit and explicit. An effective lead scoring model will use both, since both sets of data are complementary and therefore doubly informative. 


Implicit scoring means using behavioral actions to infer a level of interest in a transaction, as well as looking at data points about the lead to infer the level of fit for your investment purposes.Implicit scoring often contributes more to a lead’s overall score than explicit scoring. A lead can only be scored once for their job title, but will be scored every time they download a piece of content or open an email. If someone downloads an eBook from your company, you should award points for the very act of downloading the eBook, since you can infer from that interaction that the prospect has a certain level of interest in your company.


Implicit scoring refers to the points you award to a lead based on their behavior, such as:


  • Website visits
  • Social media interactions
  • Email opens/clicks
  • Newsletter subscriptions
  • Contact requests
  • Contact form submissions
  • Content downloads
  • Webinar
  • Free trials/product demos


With explicit scoring, you assign points to a lead based on specific objective qualities, such as firmographic or demographic information, as well as the information they shared directly, to infer a level of interest


Examples of explicit characteristics include:


  • Job title
  • Role
  • Level of seniority
  • Experience in the industry
  • Industry
  • Company size
  • Company revenue
  • Geographic location


Sometimes a lead will volunteer the information that you need for explicit scoring, like filling out a questionnaire to download gated content from your website. Or the info may be uncovered by research, which could involve checking out a prospect’s LinkedIn page or company website.

Best Practice: Separate Scores for Behavioral and Demographic Values

Demographic scores are best thought of as how interested you are in a lead. Behavioral scores are how interested that lead is in you. If you only use one lead score value, there will be no easy way to distinguish between the CEO with little to no interest in your solution vs. the low-level end user with a high interest. Scoring on both demographic and behavioral attributes allows you to provide more meaningful and relevant data to sales.

Pitfall: Score Inflation 

When you award points to a lead for a specific behavior, that score shouldn’t last forever. The value of someone attending a webinar last week will be less significant a year after the fact. However, many lead scoring schemas don’t reflect that decay of score value over time, the result is score inflation, where individual leads may tally up an excess of lead scores, by which time the score has become meaningless. One solution to consider is to use a system of expiration dates and deduct points over time.

Best Practice: Implementing a Degradation Scheme 

When it comes to lead scoring, no news is bad news. Ideally, you want to see leads moving swiftly through your sales funnel, rather than getting stuck at a certain stage and never progressing. Companies routinely use inactivity campaigns to subtract points. However, if a lead so much as clicks on one email or visits one web page during that period, their score will never go down. Typical questions that go along with negative scoring for inactive leads can include:


  • What does that action indicate about the prospect’s readiness for the next marketing or sales touch?
  • How many weeks of inactivity before you reduce score, and how frequently?
  • Fixed score subtraction or percentile reduction?
  • How much do you reduce a score due to inactivity, compared to the relative weight of other values in your scoring algorithm?
  • Smaller, more frequent reductions or larger, less frequent reductions?
  • How do you define no activity? No clicks, and no opens, no forms filled out, no pages visited? Examples of inactivity depreciation rules:
  • If the lead status is not yet verified and the score is above 50, reset the score to 50 if there is no activity for 45 days.
  • Reset score to zero if there is no activity for 45 days.


There is not always a clear answer to these questions. What’s more, the answers can vary over time, depending on the current market conditions and investment goals and objectives. 


A better approach is to institute a score degradation scheme. Score degradation helps you track stagnant leads. If they're not visiting your website or stop engaging with your email marketing, their lead score should fall over time. Like negative scoring, score degradation helps you sort out old leads and focus on more valuable leads.


Consider reversing the point system you use for implicit scoring: If a lead gets ten points for subscribing to your newsletter, they lose ten points for unsubscribing. Additionally, consult your marketing team to understand which actions promising leads do consistently. 

Best Practice: Establish a Threshold

Having a lead score threshold in place ensures that only qualified leads will be pursued. A lead scoring threshold is the point value where a prospect is considered sales-ready. When a lead’s score reaches or exceeds this score, they become a marketing qualified lead, or MQL, and are passed to the sales team.


If the threshold is too low, leads will qualify prematurely, and you will waste time on prospects that aren’t ready to be pursued. If the threshold is too high, you risk sitting on valuable leads for too long and giving them time to be snatched up by a competitor. You can determine your threshold by using what historical data reveals about the characteristics that identify a lead as qualified. This makes it easy to prioritize the leads that are most qualified.

Best Practice: Test & Refine Your Lead Scoring Model

To keep your lead scoring model as accurate as possible, continually update your scoring methods based on the most recent customer data, and test your scoring model against real-world scenarios. This can be achieved by either analyzing past leads and assigning them a score based on your model and then check if that score aligns with expected behavior. The other option is to try implementing the model for a few weeks or months and monitor the results to refine accordingly.


Indications that a model needs refining include the following scenarios:


  • If lots of leads are qualified but very few are being converted into deals, it’s likely that the lead scoring threshold is too low. 
  • If you notice deals from one type of persona or prospect behavior are increasing or decreasing, the way you weigh those variables may need to change.

The Results are Clear: Lead Scoring is An Investment

Leveraging a lead scoring model is how PE and VC get the advantage to seize the best investment opportunities and close better deals. Traditional deal sourcing is not enough, PE and VC firms need to turn their opportunistic eyes to the availability of technology and data to create differentiated approaches and use methodical strategies to enhance their deal flow. Lead scoring is one of those methodical strategies that can significantly increase IRR, by identifying the best leads so you can close the best deals in less time. Discover more opportunities and drive more management meetings. Upgrade your research process and growth potential with our innovative M&A platform.

Author
Cheyenne Kolosky

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