Replacing Static Account Tiering with Dynamic Rankings

Why static account tiering leaves millions in pipeline on the table – and how you can fix it.

How many times have you heard the outbound advice: “Just pick 3-5 high-fit accounts to focus on this week”?

As a sales practitioner, it’s reasonable advice. The accounts a rep chooses to focus on at any given time has much more impact on their results than the quality of their email copy or cold call talk tracks.

However, if sellers are manually prioritizing accounts in the AI era, they aren’t being set up for success.

Organizations need to move past the basic static account tiering model (i.e., A, B, C) that’s historically been based on basic industry categorizations, financial data and headcount.

It misses the most important ingredient of account prioritization: Propensity to buy.

With a) LLMs being able to make sense of unstructured text and b) the commoditization of intent signals, go to market teams have an opportunity to modernize account scoring and better equip sellers to generate pipeline, faster.

Let’s dig in.

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What Sellers Are Doing Today

Good sellers inherently know that at its core, outbound pipeline generation is a “Next Best Action” problem.

The accounts a seller chooses to focus on at any given time is the highest-leverage decision they can make.

So when sellers are provided with an (at-best) semi-accurate static account tier, they a) don’t trust it and b) want to do their own homework to decide which accounts to focus on.

They compensate by monitoring “dynamic” stuff manually: page views, job postings, trial activity, marketing engagement, social activity, tech stacks, promotions or leadership hires, etc., and factoring this into their targeting decision.

However, there are two downsides to doing this manually:

1. It’s extremely time consuming (it doesn’t scale).

2. It’s very difficult to do it effectively (tons of data points to find/crunch).

And if your reps aren’t focusing on the prospects most likely to buy, the worst-case scenario isn’t just wasted time –

It’s a $250K ACV opportunity that slipped through the cracks because a rep was busy emailing a ‘Tier A’ account that will never buy, while a ‘Tier C’ account just raised $500M and was actively searching for your solution on G2.

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How Operators Can Improve Prioritization

Static rankings are useful but they don’t but they don’t answer the only question that matters at 10:07am on a Tuesday: “Who should I contact next, and why?”

This is where go to market teams can improve account scoring by adding a dynamic layer that updates continuously based on real-time data:

• Account-level website visits

• Product usage or trial activity

• Internal promotions and leadership hires

• Relevant information in job postings

• Relevant social activity (posts, comments, community engagement)

• G2 reviews

This enables them to effectively answer three questions:

• Static: Could this be a good account?

• Dynamic: How good is the timing?

• Total: How good of an account is this right now?

And provide reps with holistic rankings (and eventually, recommendations) based on both static and dynamic components.

This can be applied to contacts as well.

The account is the target, but the contact is the entry point. Contact-level behaviour drives the overall account score, so it’s logical to eventually identify, enrich and score high-propensity contacts for sellers.

Examples of ‘static’ contact ranking inputs:

• Seniority and title

• Role relevance and/or department

• Experience with your product or services (scraped from LinkedIn)

• Track record (i.e., promotion quantity, accomplishments listed)

The ‘dynamic’ weighing can come from things like:

• Recent promotions

• Recent LinkedIn, Github, or Slack activity related to your category

• Page views and marketing engagement

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The Benefits of This Approach

This shift moves your team from a ‘volume first’ mindset to a ‘relevance first’ mindset. By automating the ‘who’, ‘when’ and ‘why,’ your reps can focus entirely on the ‘how’ and break into accounts by doing things that don’t scale.

The impact is immediate:

• Increased Pipeline: You’ll see an immediate uptick in opportunities sourced as things stop slipping through the cracks.

• Increased Win Rates: Reps’ time savings can be re-directed towards deal strategy and execution.

• Higher Morale: Sellers hate research; they love selling. This can also be used as a selling point for recruiting top reps.

A Simple Framework to Build This:

• Static Fit Score (25-50%)

  • Your baseline score based on the criteria that doesn’t change often (similar to the static ‘tiering’ that’s done today).

• Dynamic Signals (25-50%)

  • Flexible score that continuously updates based on the latest activity and signals.

• Decay Score (for recent unsuccessful outreach; 25%)

  • If you’ve tried to reach an account multiple times recently and nothing happened, your system should (temporarily) de-prioritize that account so reps stop burning touches and move to higher-probability targets. Then, after a cool-down period, the account can recover.

• Other Things to Consider

  • Ignore accounts where there’s an active deal, recently closed lost opportunity, etc.

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The Bottom Line

Many sales teams miss pipeline targets because they rely on outdated, static account tiering. By transitioning to a dynamic ranking engine, RevOps can automate research, reclaim weekly selling time, and ensure reps are always focused on the high-propensity targets most ready to engage today.

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If you enjoyed this article, make sure to subscribe. You can also let me know with a “comment” if you’d like to see the build of an Account ranking model in Clay.

-Cam Wright

P.S. - I always love meeting people building in the space. Reach out if you’d like to bounce ideas about outbound, GTM engineering, or AI implementations at scale.