How Do You Build an Account Tiering Sheet From a CRM Export?

How do you build an account tiering sheet from a CRM export?

Export your company records, keep only the fields that describe fit and engagement, score each account on three to five criteria, add the scores into one total, and cut the list into Tier 1, Tier 2, and Tier 3. Check the result with sales before anyone acts on it. A spreadsheet is enough to start.

This tutorial is for a founder or a small revenue team with a few hundred to a few thousand accounts in a CRM like HubSpot or Salesforce, and no budget yet for a dedicated scoring tool. You want to know which accounts deserve real attention this quarter, and you want an answer by Friday.

I like starting in a sheet because it forces the team to agree on the logic in plain view. Once the rules are settled, you can move them into the CRM or an enrichment tool. Doing it the other way round usually means automating rules nobody agreed on.

What is account tiering, and why use a sheet first?

Account tiering sorts your target companies into groups based on how well they fit and how likely they are to buy, so each group gets a different level of effort. Tier 1 gets personal outreach, Tier 2 gets lighter touches, and Tier 3 gets automated nurture. A sheet makes the rules visible before you automate them.

Without tiers, every account gets the same treatment, which in practice means the loudest accounts get attention and the best ones get missed. Tiering is a decision about where to spend limited time. That is a go-to-market decision, not a data task.

The sheet also gives you an audit trail. When a rep asks why an account is in Tier 3, you can point to the exact scores. That transparency is what makes sales trust the list, and a list sales does not trust will be ignored.

What should you export from your CRM?

Export one row per company with the record ID, company name, domain, industry, employee count, country, lifecycle stage, owner, last activity date, and number of associated contacts and open deals. Leave out notes and free text. You want columns you can score, plus the ID so you can bring tiers back into the CRM later.

Use your CRM's standard export for company records and choose the properties you need instead of exporting everything. Check your CRM's own documentation for how exports and property selection work on your plan. A narrow export is easier to clean and much less likely to leak data you did not need.

Before you export, decide which accounts are in scope. Most teams exclude current customers, competitors, and companies marked as disqualified. If you score those, they clutter the top tier and confuse the conversation with sales.

How do you set up the sheet before scoring?

Paste the export into a tab called Raw and never edit it. Create a second tab called Scoring that pulls in the columns you need, cleans the values, and adds one new column per scoring criterion. Add a third tab called Rules that lists each criterion, its point values, and a one-line reason.

Cleaning is the slow part. Industry values are often inconsistent, so map them into a short list of groups you actually sell to. Employee counts may be ranges or blanks. Decide how to treat blanks before you score, because a blank that silently scores zero will push good accounts down.

The Rules tab is the most important tab in the file. It is where the team agrees on what good looks like. Keep it short enough to read in two minutes. If a rule needs a paragraph to explain, it is probably two rules, or it is not ready yet.

How do you score fit for each account?

Pick three to five fit criteria tied to your ideal customer profile, such as industry, company size, region, and a technology or business model signal. Give each one a small point range, like zero to three. Use a simple IF formula per criterion so anyone can read the logic, then sum the columns into a fit score.

For example, industry might score three for your best segment, two for a good adjacent segment, and zero for everything else. Size might score three for the band where you win most often and one for bands where you win sometimes. Write those values in the Rules tab first, then build the formulas to match.

Base the point values on closed-won history if you have it. Look at your last twenty or thirty wins and note what they share. If you are still deciding which segment to focus on, my guide on how to pick an ICP when two segments both buy will help you settle that before you score.

How do you add intent or engagement to the score?

Add a separate engagement score from data you already have: recent activity, number of engaged contacts, open deals, and website or email engagement if your CRM records it. Keep it apart from the fit score. A good-fit account with no engagement and an engaged account with poor fit need very different plays.

A simple version uses two or three criteria. Last activity within the past ninety days might score two. Three or more associated contacts might score two. An open deal might score three. Use COUNTIF or a similar function on the activity columns if you need to count records.

If you want richer signals, such as tech stack or hiring data, you can add them through an enrichment tool later. I walked through one approach in how to score accounts by tech stack in Clay. Start with what you have. A clean simple score beats a rich messy one.

How do you turn scores into tiers?

Plot fit against engagement and set cut-offs. High fit becomes Tier 1 when engagement is also present, and Tier 2 when it is not. Medium fit becomes Tier 2 or Tier 3 depending on engagement. Low fit goes to Tier 3 regardless. Then cap Tier 1 at the number of accounts your team can genuinely work.

The cap is the step most teams skip. If two reps can each give real attention to about twenty-five accounts a quarter, Tier 1 should hold about fifty accounts, not three hundred. Sort by total score and draw the line where capacity runs out. Tiering is about capacity as much as quality.

Add a Tier column with a formula that applies your cut-offs, and a Reason column that names the top two criteria behind the tier. The reason column saves hours of debate later, because a rep can see why an account landed where it did.

How do you check the tiers before sales uses them?

Ask two people from sales to review twenty accounts: ten from Tier 1 and ten from Tier 3. Ask whether any account is clearly in the wrong place and why. Each disagreement either reveals a missing rule or a data problem. Fix the rule, rescore, and review again until the surprises stop.

Look especially at accounts sales already knows well. If a current hot opportunity lands in Tier 3, something is wrong with your criteria or your data. If a known bad-fit company lands in Tier 1, the same is true. These known cases are your test set.

Record every change you make to the Rules tab with a date and a reason. Tiering rules will change as you learn, and a change log keeps everyone from arguing about what the rules used to be. It also makes the next quarterly refresh much faster. If you want the score behind each tier to come from a written rule, my guide to writing an ICP scoring rubric in a spreadsheet pairs well with this sheet.

What should you do next?

Once sales agrees with the tiers, import the record ID and Tier column back into your CRM as a company property. Build views and tasks on that property so reps see their Tier 1 list every day. Refresh the sheet each quarter, and decide which accounts to enrich first based on tier.

That last step matters for cost. Every record you enrich costs something, so tiers tell you where to spend. I covered that decision in how to decide which accounts get enriched first.

I build websites and the automations behind them for lead generation, and a clear tiering model is usually the first thing I ask for before building any outbound system. If you want help turning your CRM export into tiers your sales team trusts, reach out and let's chat.