When I first got into B2B sales, our account scoring method was ridiculously simple. We mostly focused on basic demographic information and a few key behavioral signals. It did get the job done, but wasn't perfect - especially when dealing with complex, multi-stakeholder buying processes.

I remember a deal where we were targeting a large eComm company. We thought we had it in the bag based on our initial account scoring, but we completely missed the fact that they were in the middle of a major platform migration. Our basic account scoring model didn't account for this piece of info. Long story short, we ended up wasting months chasing a deal that was never going to close.

In this guide, I want to share my favorite advanced account scoring models and techniques that have delivered real results for me.

What is Account Scoring?

Account scoring is a strategic method used by marketing and sales teams to evaluate and prioritize potential customers or accounts based on their likelihood to convert and their potential value. It involves assigning scores to accounts based on various criteria such as firmographic data, engagement levels, and fit with the ideal customer profile (ICP). This helps focus sales efforts on high-value accounts, improving conversion rates and driving revenue growth.

The Foundations of Effective Account Scoring Model

Before we get into the advanced account scoring models, let's quickly review the foundations of effective account scoring.

Your sales and marketing teams need to speak the same language when it comes to what constitutes a qualified lead or account. Get everyone on the same page.

You need a well defined Ideal Customer Profile (ICP). To define our ICPs, we use CustomerBase AI to analyze past deals and identify winning trends.

You also need to consider both Fit and Intent. "Fit" refers to how well an account matches your ICP, while "intent" shows their readiness to buy.

The Growth Propensity Index (GPI)

When I first heard about Growth Propensity Index (GPI) - account scoring methodology - I was very skeptical. I've seen many account scoring frameworks come and go. But the more I dug into it, the more I realized that it's actually not bad.

The GPI builds upon the REACH Framework and offers a comprehensive way to evaluate accounts for growth potential.

Here's how Growth Propensity Index (GPI) works - GPI evaluates accounts based on five fundamental factors, each rated on a scale of 1-5:

1. Relationship: This measures the strength of your customer rapport. For example: a score of 1 might indicate a new customer without an internal product champion, while a 5 represents an account with a strong product advocate within the organization.
2. Engagement: This assesses product usage levels. For example: a 1 might signify a trial user, while a 5 reflects extensive product usage across multiple licenses or products.
3. Actions: This evaluates customer-initiated activities. For example: a 1 might indicate minimal interaction, while a 5 might indicate a customer who actively seeks info, features, or new applications.
4. Customer Value: This gauges the perceived worth of your product to the customer.
5. Horizon: This considers the account's maximum growth potential based on estimated Lifetime Value (LTV).

By averaging these five scores, you can determine the GPI for each account. This account score can then be mapped against current account value to identify expansion opportunities and help with strategic decisions.

One of our B2B SaaS clients was struggling to identify which of their existing customers had the most growth potential. Within three months of using the GPI account scoring system, they had increased their upsell revenue by 28%.

At CustomerBase AI, we've incorporated some elements of this approach into our AI-driven account scoring system. We've found that this evaluation methodology provides a more holistic view of an account's potential than traditional account scoring methods.

Multi-touch Account Scoring Model.

Multi-touch attribution is all about giving credit where credit is due. You know how rare it is for a single interaction to lead directly to a conversion. Usually it's a series of touchpoints over time that eventually leads to a sale. Multi-touch account scoring models distribute value across all these touchpoints throughout the customer journey, giving you a more accurate picture of which activities are truly driving conversions.

Multi-touch account scoring model step-by-step-guide.

• Step #1. Identify all possible touchpoints: Simply list all the ways a prospect can interact with your brand. For example: website visits, email opens, content downloads, webinar attendance, sales calls, things like that.
• Step #2. Choose an attribution model: These are the most common attribution models models: linear (equal credit to all touchpoints), time decay (more credit to recent touchpoints), u-shaped (most credit to first and last touchpoints), w-shaped (most credit to first, middle (MQL), and last touchpoints), custom (weighted based on your niche or business goals).
• Step #3. Implement tracking: Check if you have the tech to track all these touchpoints. Basically, you integrate your CRM, marketing tools, and analytics.
• Step #4. Assign values: Based on your attribution model, you assign values to each touchpoint.
• Step #5. Analyze and optimize: Analyze your data to see which touchpoints are most valuable in driving conversions. Then use this info to optimize your marketing and sales strategies.

Multi-touch account scoring model example

Let's say a prospect first visits your website after clicking on a LinkedIn ad (10 points), then downloads a whitepaper (20 points), attends a webinar (30 points), and finally has a sales call (40 points) before converting. In a linear model, each touchpoint would get 25 points. In a time decay model, the points might be distributed as 5, 15, 30, 50. The key here is to pick an attribution model that best reflects the reality of your sales process.

Predictive Account Scoring Model

Predictive scoring is where you use AI and historical data to forecast which accounts are most likely to convert. At CustomerBase AI, we've seen predictive account scoring model increase conversion rates by up to 30% for some of our clients.

Predictive account scoring model step-by-step-guide

• Step #1. Gather historical data: Collect as much data as possible on past deals, both won AND lost. Try to include firmographic data, behavioral data, and any other relevant info.
• Step #2. Pick predictive account scoring model: Common account scoring models include logistic regression, decision trees, and neural networks. The choice depends on your data and specific needs. I'll go a bit more in-depth on these account scoring models in a bit.
• Step #3. Train your model: Use your historical data to train the model. This process teaches the model to recognize patterns that lead to conversions.
• Step #4. Test and validate: Use a portion of your data (not used in training) to test the model's accuracy.
• Step #5. Implement the model: Once validated, implement the model in your scoring system.
• Step #6. Continuously refine: As new data comes in, use it to refine and improve your model.

Predictive account scoring model types

1. Logistic Regression: This is basicially a yes/no question machine. It's great for predicting outcomes that have two possible results (like will they buy or not?). Say you're trying to predict if it will rain tomorrow based on today's temperature and humidity. Logistic regression would look at how these factors have influenced rain in the past to make a prediction. It's relatively simple and easy to interpret, making it a good starting point for many businesses.
2. Decision Trees: Essentially, these are flowcharts that ask a series of questions to reach a conclusion. That's basicially what a decision tree does. It breaks down your data into smaller and smaller subsets, asking questions at each stage. For example: it might first ask "Is the company size over 500 employees?", then "Have they downloaded our whitepaper?", and so on, until it reaches a prediction. Decision trees are great because they're easy to understand and explain, even to non-technical sales team members.
3. Neural Networks: These are the most complex of the three. I won’t get too technical, but neural networks are designed to work kinda like our brains. Imagine a vast network of interconnected nodes, each making simple calculations but together capable of recognizing complex patterns. Neural networks are powerful and can handle very complex relationships in your data, but they're also a bit of a "black box". It's not always clear how they arrive at their predictions. They're great when you have a lot of data and are dealing with complex, non-linear relationships.

Predictive account scoring model example

Let's say your predictive model identifies that companies in the healthcare sector, with 500-1000 employees, that have engaged with your pricing page and attended a webinar in the last 30 days are 80% more likely to convert than your average lead. Your predictive account scoring system would automatically assign higher scores to accounts fitting this profile. Easy!

Account Engagement Scoring

Instead of just scoring individual leads, account engagement scoring evaluates the overall engagement level of an entire account. Here, an account with multiple engaged contacts is more valuable than one with a single highly engaged lead.

Account engagement scoring model step-by-step-guide

• Step #1. Identify key decision makers: For each account, identify the different roles typically involved in the buying process (e.g., end-user, technical decision-maker, financial decision-maker).
• Step #2. Define engagement metrics: Determine what actions you consider engagement for each role. For example: content downloads, product usage (for existing customers), meeting attendance, things like that.
• Step #3. Assign weights: Give more weight to actions from key decision-makers or to more high-intent actions.
• Step #4. Calculate individual engagement scores: Score each contact based on their engagement activities.
• Step #5. Aggregate to account level: Combine the individual scores into an overall account engagement score. You can go with a simple sum or do a weighted average.
• Step #6. Set thresholds: Determine what level of engagement trigger different actions from your sales team.

Account engagement scoring model example

Account A has three contacts: a end-user with an engagement score of 80, a technical decision-maker with a score of 60, and a financial decision-maker with a score of 40. Account B has one contact, an end-user with a score of 100. Despite the higher individual score, Account A might be considered more valuable due to the broader engagement across multiple stakeholders.

Competitive Displacement Account Scoring Model

If you're selling in a high highly competitive market, you can try to identify accounts that might be ready to switch from a competitor. Competitive displacement account scoring model incorporates signals that indicate this readiness into your account scoring model.

Competitive displacement account scoring model step-by-step-guide

• Step #1. Identify competitors: Make a list of your main competitors.
• Step #2. Define displacement signals: These are the signals that work for me: negative sentiment about competitors in social media posts, engagement with your competitor comparison content, recent leadership changes at the account, contract expiration dates (if known). You can come up with your own based on the data you have access to.
• Step #3. Set up monitoring: Use social listening tools like Sprout Social, website analytics (Google Analytics), and platforms like CustomerBase AI to monitor for these signals.
• Step #4. Assign scores: Determine how much each signal should increase an account's score.
• Step #5. Integrate with your CRM: Make sure your sales team can easily access these account scores so they can actually take appropriate actions.

Competitive displacement account scoring model example

Say you notice that a prospect has been engaging heavily with your competitor comparison page, and through social listening, you detect some negative sentiment about their current vendor. Your scoring system adds 50 points to this account's score and flags it for immediate sales follow-up.

Product-Fit Account Scoring Model

If you offer multiple products or solutions, you may want to consider a product-fit account scoring model. Nothing fancy, it basically involves developing separate account scoring models for product or solution. What indicates a good fit for one product might not be relevant for another. Product-fit account scoring model can help you focus your sales and marketing on the right people.

Product-fit account scoring model step-by-step-guide

• Step #1. Define product-specific criteria: For each product, identify the characteristics that make an account a good fit. These can be as simple as industry, company size, technology stack, etc.
• Step #2. Create separate scoring models: Create a unique account scoring model for each product, incorporating both general fit criteria AND product-specific factors.
• Step #3. Identify product-specific behaviors: Try to understand what actions indicate interest in specific products. For example: viewing product pages, downloading product-specific content, etc.
• Step #4. Implement multi-product scoring: Set up your system to score accounts for each product separately.
• Step #5. Use in segmentation: Use these scores to segment your accounts for targeted marketing and sales efforts.

Product-fit account scoring model example

Let's say you offer both a CRM and a marketing automation tool. An account showing high engagement with your email marketing content and having a large customer database might score high for your marketing automation tool, while an account with a large sales team and high engagement with sales efficiency content might score high for your CRM.

The Most Common Account Scoring Mistakes

I've made quite a few of these account scoring mistakes myself, and I've seen countless others fall into these traps.

Mistake #1. Overcomplicating the Account Scoring Model.

I totally understand it, you want to account for every possible factor that could influence a sale. But here's the thing: more complex doesn't always mean better. I remember working with a client who had an account scoring model with nearly 50 different criteria. It was a total nightmare to manage and interpret, and it didn't even perform better than simpler account scoring models.

The fix: Start simple. Focus on the 5-10 most important factors that actually indicate buying potential. You can always add more factors later if you need.

Mistake #2. Ignoring Negative Account Scoring.

I've noticed many sales teams focus only on positive actions that increase a lead's score. But what about actions that indicate a lead is losing interest or isn't a good fit? Ignoring these can lead to inflated scores and wasted effort on poor-quality leads.

The fix: Incorporate negative account scoring for actions like unsubscribing from emails, long periods of inactivity, or engaging with irrelevant content.

Mistake #3. Failing to Align Sales and Marketing.

I can't stress this enough: your account scoring system will fail if sales and marketing aren't on the same page.

The fix: Involve both sales and marketing in the development of your account scoring system. Regularly gather feedback from both teams and adjust your model accordingly.

Mistake #4. Not Updating the Account Scoring Model.

B2B sales, technology, your target markets are constantly evolving, and so should your account scoring model. I've worked with companies who set up their account scoring system years ago and then never touched it again.

The fix: Regularly review and update your scoring criteria. At CustomerBase AI, we recommend a quarterly review at minimum.

Mistake #5. Relying Too Heavily on Demographic Data.

While firmographic data (company size, industry, etc.) is important, it's not everything. I've seen companies miss out on great opportunities because they were too focused only on demographic criteria.

The fix: Balance demographic data with behavioral buying signals. A smaller company showing high engagement might be a better prospect than a large company that's barely interacting with your brand.

Mistake #6. Ignoring the Buyer's Journey.

Not all actions are created equal. Downloading a top-of-funnel whitepaper shouldn't be scored the same as requesting a demo. Yet, I've seen companies treat all interactions with equal weight.

The fix: Align your account scoring with the buyer's journey. Assign higher account scores to actions that indicate a lead is moving closer to a purchase decision.

Mistake #7. Overlooking Data Quality.

Your account scoring model is only as good as the data you feed into it. Garbage in = garbage out. I once worked with a company whose CRM was filled with duplicate entries and outdated info. Their account scoring system was essentially working with garbage data, producing garbage results.

The fix: Implement rigorous CRM data hygiene practices. Regularly clean your database and make sure your sales team is inputting data consistently and accurately.

Mistake #8. Focusing on Quantity Over Quality.

It's easy to get caught up in the numbers game, trying to generate as many high-scoring leads as possible. But your goal isn't just to have a lot of high-scoring leads – it's to identify the leads most likely to convert.

The fix: Focus on the quality of your leads, not just the quantity. It's better to have fewer, higher-quality leads than a large number of mediocre ones.

Mistake #9. Not Considering the Full Account Picture.

In B2B sales, decisions often involve multiple stakeholders. Way too many sales teams focus too much on individual lead scores without considering the overall engagement of the account.

The fix: Implement account-based scoring that takes into account the engagement levels of multiple contacts within an organization.

Remember, account scoring is as much an art as it is a science. It requires a deep understanding of your business, your customers, and the broader market dynamics. Don't be discouraged if you don't get it perfect right away.

How to Build Account Scoring Models

I'm going to share with you the approach that's worked best for me and for our clients at CustomerBase AI.

You want to start simple. Don't try to account for every possible variable right out of the gate. You will get overwhelmed 100%. Begin by assigning higher values to high-intent activities like demo requests or quote inquiries. This approach prevents overqualification of window shoppers and creates a more reliable account scoring framework.

Don't try to create a complex account scoring system that takes into consideration many many different variables. It may look great on paper, but in practice, it'll be a nightmare to manage and interpret. Most likely, you'll end up scrapping it and starting over with a simpler account scoring model.

As your account scoring model matures, you can start to incorporate more nuanced behavioral signals. For example:

• content engagement
• website visit frequency
• participation in webinars

But don't just add these willy-nilly. Each new signal should be carefully evaluated for its "predictive power."

One often overlooked aspect of lead scoring is negative scoring. Don't just focus on positive signals. Implement negative scoring for actions that indicate disinterest or poor fit.

Another crucial element is account score decay. Engagement signals naturally deteriorate in value over time, especially in B2B where sales cycles can be long. Implement time-based account score degradation to ensure that qualification reflects recent interest rather than historical engagement that may no longer be relevant.

At CustomerBase AI, we use advanced AI algorithms to continually refine our scoring models based on actual outcomes. This helps us spot subtle patterns and signals that traditional rule-based scoring systems miss.

How to Balance Quantitative and Qualitative Factors in Account Scoring Model

I'm a big believer in data-driven decision making. I'm a data nerd! But I also know from experience that not everything can be reduced to a number. That's why it's super important to balance quantitative metrics with qualitative insights in your account scoring.

But, how do you incorporate qualitative factors into your account scoring model? Here are a few approaches that have worked well for me:

1. Feedback from sales reps: Create a system for your sales team to easily input their insights about prospects. It doesn't have to be overly complicated. This could be as simple as a 1-5 rating of how promising they think the lead is, or more detailed notes about the conversations they've had.
2. Decision-making structure: Understanding the decision-making process within an account can help too. Is there a clear champion for your product? Are there multiple stakeholders involved? This kind of info can significantly impact the likelihood of a deal closing.
3. Competitive situation: In my experience, knowing whether an account is currently using a competitor's product, and how satisfied they are with it, can be super helpful in predicting the likelihood of a switch.
4. Strategic importance: Some accounts might be valuable beyond just the immediate revenue they represent. Maybe they're a flagship client in a new industry you're targeting, or they have the potential to open up a new market for you.

TIP: do not ignore the human element in your account scoring. About a year ago or so, we had a large client where our quantitative account scoring had flagged an account as low-potential. But George had a hunch based on his conversations with the prospect. We decided to trust his instinct and pursue the opportunity. That "low-potential" account ended up becoming one of our biggest clients that year.

Urgency Indicators and Buying Signals for Account Scoring Models

Advanced account scoring is great because it helps spot signs that a prospect is ready to buy. In my experience, this can be the difference between closing a deal and missing your sales quota.

Here are some of the most effective urgency indicators and buying signals I personally use:

• Sudden increase in content consumption, especially bottom-of-funnel content: If a prospect suddenly starts downloading product comparisons, pricing guides, or case studies, that's often a sign they're getting serious about making a purchase.
• Multiple stakeholders from the same account engaging with your content: In B2B sales, decisions are often made by committee. If you start seeing engagement from multiple people within an account, that's a good sign that internal discussions about your solution are happening.
• Requests for pricing information or custom quotes: This one might seem obvious, but you'd be surprised how often this buying signal gets overlooked in account scoring models. A prospect asking about pricing is usually a strong indicator of purchase intent.
• Increased frequency of interactions with your sales team: If a prospect who was previously slow to respond all of a sudden becomes more engaged with your sales team, that's often a sign that their timeline has accelerated.
• Engagement with competitor comparison content: If a prospect is actively comparing you to your competitors, they're likely in the decision stage of their buying journey.

When these urgency indicators and buying signals are detected, your system should automatically increase the account's score and alert your sales team to prioritize this account.

The key here is speed and relevance. When you detect these buying signals, you gotta be ready to respond quick with highly tailored outreach. Your marketing team should have content ready to go that addresses common objections and pain points, and your sales team should be trained to quickly customize their approach based on the specific buying signals detected.

Account-Based Marketing (ABM) and Account Scoring

If you're not too too familiar with Account-Based Marketing, ABM is a strategic approach where marketing and sales teams work together to target best-fit accounts and turn them into customers.

In my experience, account scoring becomes incredibly powerful in the context of ABM campaigns. When you're focusing your sales and marketing efforts on a select group of high-value accounts, you need to be sure you're choosing the right ones.

For Account-Based Marketing campaigns, I highly recommend monitoring both traditional engagement metrics and account-specific indicators that reflect progression through the buying journey. Here are some key performance indicators (KPIs) that I've found particularly useful for ABM campaigns:

• Opportunities influenced by ABM efforts: This helps you understand the direct impact of your ABM activities on your sales pipeline.
• Direct MQLs/SQLs generated through ABM campaigns: This measures how effective your ABM efforts are at generating qualified leads.
• Impressions and clicks from target accounts: This gives you an idea of how well your content is resonating with your target accounts.
• Organic social engagement from industry-specific content: This can be a good indicator of thought leadership and brand awareness within your target accounts.

We help our clients establish benchmarks at 3, 6, and 12-month intervals to effectively evaluate and optimize their account scoring template within their broader ABM framework. This helps them ensure that their ABM campaigns are always aligned with their business goals.

I remember working with a B2B SaaS company that was struggling to gain traction in the healthcare sector. We implemented an ABM program with a pretty complex account scoring model tailored specifically for healthcare prospects. Within 6 months, they had landed their first major hospital chain client, and within a year, healthcare had become their fastest-growing segment.

The key attribute to their success was the ability to identify and focus on the accounts with the highest potential, which was made possible by the account scoring model we implemented. It allowed them to allocate their resources more effectively and create highly personalized outreach that resonated with their target accounts.

Conclusion

The goal of account scoring isn't just to assign numbers to accounts. It's about empowering your sales and marketing teams to focus their efforts where they'll have the greatest impact. It's about creating a more efficient, effective, and ultimately more successful revenue machine.

At CustomerBase AI, we're committed to helping B2B companies create repeatable growth by mapping their ideal customer profile with precision and segmenting their market to uncover the best-fit opportunities. Our AI-driven platform aligns marketing, sales, and leadership around a unified data layer, enabling teams to strategize and act on the same insights for consistent results.

As we like to say at CustomerBase AI: Validate your ICP. Segment your market. Grow your customer base. With the right approach to account scoring, you can do just that.

I hope this guide helped! Got questions? Shoot me a message on LinkedIn.