Familiarity: The missing signal in your account scoring model

Which accounts are most likely to buy from you?

The prospects who already know and love your offering. 

Traditional account scoring models have mainly prioritized how closely an account fits an ideal customer profile (ICP). But a company’s size, industry, and tech stack only paint a partial picture.

More recently, sales teams have incorporated buying intent and readiness data from tools like 6Sense into their account scoring models. This data gets you closer to finding accounts that are in the market, but don’t consider how likely they are to choose you.

That’s why every account scoring model should include a familiarity metric—i.e., how many people on the buying team have already evaluated or used your product.

In this article, we’ll cover:

• Why account scoring (and incorporating familiarity in particular) is a better approach than spray-and-pray
• The variables and tools that will help you find your best-fit accounts within your total addressable market
• How to use your account scoring model to drive an actionable go-to-market strategy

What is account scoring?

Account scoring identifies the highest-potential accounts within your total addressable market by weighting several variables from 1st and 3rd party data. Sales and marketing teams use account scoring to focus their efforts on the accounts they have the highest chance of winning and retaining.

A strong account scoring model combines several data points into one number:

• Intent data (e.g., buying signals)
• Fit data (e.g., firmographics and technographics)
• Growth data (e.g., fast-growing industries)
• Familiarity data (e.g., former users on the buying team)

Account scoring differs from lead scoring in that you’re rating the fit of a prospective company, not one person. 

Before we get deeper into how to weigh each variable, let’s get into why account scoring is worth your time.

The importance of account scoring

Early adopters of sales automation tools had a huge leg up. Sales reps could take a spray-and-pray approach to outreach by hitting up as many people’s inboxes as possible. 

But now that sales automation is a saturated market and everyone’s inboxes are flooded with cold pitches, the high-volume approach has lost its shine.

This gives sales teams three big reasons to nail their account scoring model.

Quality, not quantity

To build a quality pipeline, sales teams should focus on quality, not quantity. This means:

• Focusing on your core ICP
• Tailoring your messaging to each account
• Leveraging familiarity with your product

Targeted focus

However, sales reps only have so much time in a day. It’s easy to say “Spend more time on the right accounts,” but gut feeling will often point reps in the wrong direction. 

Now, especially in a down market, your sales team’s limited attention and energy should be focused squarely on the accounts most likely to buy.

Decreased CAC

Decreasing customer acquisition cost (CAC) is about deploying sales and marketing resources efficiently. Focusing attention on higher-quality accounts is the best path to sales efficiency.

It’s the old adage about spear-hunting whales vs. netting fish—by spending more concentrated time on fewer accounts you’re more likely to win and retain, you’ll get better returns than by casting a wide net.

What variables should you use in your account scoring model?

The need for account scoring is obvious. The real challenge is coming up with an account scoring model that is truly predictive of a great account.

Most account scoring models take three data categories into account—fit, intent, and readiness—but we’re suggesting an extra fourth category: familiarity.

1. Account fit data

Fit is about which companies get the most value in your product. This is your ICP at the account level.

Firmographic data: These are data points about a company, such as:

• Company size
• Annual revenue
• Industry or vertical
• Geography

For example, you might only target US-based companies with over 100 employees or over $1m ARR in the healthcare industry.

Technographic data: This is what you know about the company’s technology stack that impacts whether you can sell to them. 

This includes whether they use:

• Competitor solutions: Do they use a competitor that might be a rip-out opportunity?
• Compatible solutions: Do they use technology that plays nicely with yours?

For example, you might know that companies who use Salesforce as a CRM are more likely to close with you than companies who use Hubspot. Or your product may only be compatible with Shopify websites.

Tools like Zoominfo, Apollo, and Sales Navigator can enrich your CRM data and help you surface these accounts.

2. Buyer intent data

Buyer intent data predicts an account’s potential buying interest based on their actions and online behavior. 

It asks questions like: 

• Are they researching your product category online?
• Are they looking at your website?
• Have they downloaded marketing materials from your website?
• Are they trialing your product?
• How many people are engaged in the sales process?

Tools like 6Sense enhance your CRM data with buying intent signals by tracking an account’s online traffic activity (e.g. visits to review websites) using IP addresses, cookies, and devices

3. Growth and buyer readiness data

Similar to buyer intent data, buyer readiness data takes company and industry growth data into account to identify accounts that may be ready to spend:

This can include data on:

• Revenue growth rates or milestones
• Headcount growth 
• Funding announcements
• Industry growth

Outside market forces (e.g., economic downturns, new competitors, regulations, tax laws)

4. Familiarity data

Familiarity is the most-overlooked data channel when it comes to account scoring.

Why is that?

It’s extremely costly to familiarize an account with your product—from marketing to sales outreach to multithreading, through to onboarding. 

But it’s just as costly for the prospect’s team to learn about a new product—from researching the market to building requirements to assessing vendors, through to onboarding. Most people would rather stick with what they know.

Therefore, the prospective accounts that are most familiar with your product are the most likely to buy. For example, Heap converts 73% of meetings to opportunity when they are already familiar with their product, which is 2x their standard outbound conversion goal.

There are a few ways to assess the familiarity level of an account:

• Have people at the company already used and enjoyed your product?
• Have they attended an event?
• Have they looked at your website?
• Do you have connections through previous customers?
  

For example, Champify helps you identify the 25% of your customer contacts who change jobs every year—and whether they’re at a new account that fits your ICP.

Champify’s new Surge feature scores accounts based on their pre-existing familiarity with your business. It populates an account field with the number of former customer relationships that currently work at each account in your CRM.

So not only can you identify customers who are interested in the category, but you can also identify companies with a cluster of people who are advocates. 

That’s very powerful and should be an important part of scoring.

Use account scoring to drive your strategy

Once you have built your account scoring model, use the same inputs you used to create it to educate your go-to-market strategy.

For example, if familiarity is a key factor in your account scoring, you could use the following targeting strategies.

• Account-based marketing (ABM): Using Champify’s Surge feature in your account scoring can help you identify accounts that are more likely to evaluate your service. For example, you may already have an ideal list of accounts in your territory. But you may also have a long-tail list of greenfield accounts. You can prioritize these accounts based on how many former power users they have in their company.
• Account prioritization and assignment: Accounts that reach a threshold account score can be assigned to new or existing sales reps. For example, you can prioritize accounts that have a cluster of former users in your account territory planning.
• Targeting competitive rip-outs: If you flag a surge in former customers at a prospective company, that signals they may be primed to move away from a competitive product or service.‍
• Breaking into new verticals and segments: If your former users and champions have moved into a new vertical, it might give you a new wedge into that industry.

Measure account familiarity with Champify

To sum it all up, most account scoring models take into account fit, buyer intent, and buyer readiness.

But most sellers are ignoring familiarity. And it’s mainly because they don’t know how to measure it.

Champify helps you build a quality pipeline by identifying former power users and champions at target accounts. Champify integrates seamlessly with your CRM and leverages your existing lead routing tools and logic.

To learn how Champify can strengthen your go-to-market motion, schedule a demo with our team.

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