sales-account-tiering

{
  "skill_name": "sales-account-tiering",
  "evals": [
    {
      "id": 1,
      "prompt": "I run revenue ops at Brindle Analytics - B2B SaaS, average contract value about $55K, 8 quota-carrying AEs, and a qualified list of 940 accounts that all carry a maintained 0-100 fit score from our segmentation model. We're going with the standard bands: 80 and above is Tier 1, 50-79 is Tier 2, below 50 is Tier 3. That puts 310 accounts in Tier 1, which works out to roughly 39 each across the 8 reps. Seems workable to me. Can you write up the tiering plan so I can take it to our VP on Thursday?",
      "expected_output": "A tiering charter that refuses the 310-account Tier 1, sets the per-rep Tier-1 capacity cap first, then raises the composite cutoff above 80 until the Tier-1 count fits the resulting slot count, with per-tier firmographic gates, a bounded override budget, published sub-scores and SLA-encoded coverage differences.",
      "files": [],
      "expectations": [
        "Sets the Tier-1 capacity cap before the cutoff, and calibrates the cutoff to fit the cap rather than the reverse.",
        "Rejects leaving 310 accounts in Tier 1 and names tier collapse as the failure mode.",
        "Places the $55K ACV in the $50K-$500K capacity band of roughly 20-50 accounts per rep, on a 1:few motion.",
        "Proposes a Tier-1 cap per rep inside roughly 5-25 accounts.",
        "Multiplies the chosen Tier-1 per-rep cap by the 8 reps and states the resulting total Tier-1 slot count.",
        "States the composite cutoff moves above 80 until the Tier-1 count fits under that slot count.",
        "Treats the 80/50 banding as a starting convention rather than a fixed rule.",
        "Adds firmographic gates per tier, with Tier-1 gates tighter than Tier-2 gates.",
        "Bounds the manual strategic-override channel to a stated share of Tier-1 slots, each override logged and reviewed at the tier review.",
        "Requires publishing per-dimension sub-scores so reps can see why an account landed in its tier.",
        "Requires at least one coverage difference encoded as an SLA per tier, not tier labels alone."
      ]
    },
    {
      "id": 2,
      "prompt": "Calloway Telemetry here. Our account tiers have been live two quarters. The board dashboard shows 3.4x pipeline coverage company-wide so coverage looks fine, but the enterprise team keeps missing - they're at 34% quota attainment while SMB is over 90%. Historical win rates: SMB 58%, mid-market 31%, enterprise 17%. My instinct is to raise enterprise activity targets and add a weekly pipeline blitz. What do you think, and what should I be watching on the tier health side?",
      "expected_output": "A diagnosis that recomputes required coverage per segment as 1 divided by win rate, shows the blended 3.4x is masking a starved enterprise segment, reads 34% attainment as an oversized-book signal calling for smaller books rather than more activity, and specifies per-segment, per-tier health metrics.",
      "files": [],
      "expectations": [
        "Computes required pipeline coverage as 1 divided by the segment's historical win rate rather than applying a flat multiplier.",
        "Gives SMB required coverage of roughly 1.7x from the 58% win rate.",
        "Gives mid-market required coverage of roughly 3.2x from the 31% win rate.",
        "Gives enterprise required coverage of roughly 5.9x from the 17% win rate.",
        "States the 3.4x blended number hides a starved enterprise segment.",
        "Declines to recommend raising enterprise activity targets or a pipeline blitz as the fix.",
        "Reads 34% enterprise quota attainment as an oversized-book signal and recommends shrinking books rather than pushing reps harder.",
        "Names roughly 40% segment quota attainment as the threshold that forces a redesign rather than a routine review.",
        "Requires every tier-health metric to be computed per segment and per tier, never blended.",
        "Names by-tier outcome metrics - pipeline created, win rate, ACV, retention - rather than engagement metrics.",
        "Recommends pre-defining the response to each coverage band instead of deciding under pressure."
      ]
    },
    {
      "id": 3,
      "prompt": "Marlowe & Sons is a direct-to-consumer skincare brand - we sell entirely through our own site, about 412,000 customers on file, and a CX team of 9. Leadership wants us to tier the customer base into Tier 1 / Tier 2 / Tier 3 'accounts', set accounts-per-rep caps for the CX team, and run quarterly business reviews with the top tier. Can you build that out for us?",
      "expected_output": "A refusal to force account-tiering machinery onto a direct-to-consumer base, naming customer-value segmentation on spend or lifetime-value bands as the structurally different exercise, and distinguishing this case from B2C sold through key accounts.",
      "files": [],
      "expectations": [
        "States that direct-to-consumer has no accounts to tier.",
        "Names customer-value segmentation on spend or lifetime-value bands as the structural analog.",
        "Declines to produce accounts-per-rep capacity caps for the 412,000 customers.",
        "Declines to produce a per-tier quarterly business review cadence for individual consumers.",
        "States the analog is a different exercise with different math rather than renamed account tiering.",
        "Does not apply the ACV-to-accounts-per-rep capacity bands to this case.",
        "Distinguishes this from B2C sold through key accounts - retail chains, distributors, franchise groups - where account tiering does transfer.",
        "States the B2B or B2C scope of the answer explicitly rather than leaving it implicit."
      ]
    },
    {
      "id": 4,
      "prompt": "I'm head of sales at Vaughn Pantry Co., a snack manufacturer. We sell through 4 national grocery chains, 26 regional chains, and roughly 1,900 independent stores, with 5 key-account managers on the team. Everything I read about account tiering is B2B SaaS - buying committees, employee counts, tech stacks. Does any of it apply to us, and if so how would you actually cut our list?",
      "expected_output": "Confirmation that account tiering transfers nearly unchanged to a B2C key-account motion, with national chains as Tier 1 under named key-account managers, regional chains as Tier 2, independents routed through distributors or telesales as Tier 3, joint business planning replacing the QBR, and the changed fit-score inputs placed upstream.",
      "files": [],
      "expectations": [
        "States account tiering transfers to a B2C key-account motion rather than being B2B-only.",
        "Places the 4 national chains in Tier 1 with named key-account managers.",
        "Places the 26 regional chains in Tier 2.",
        "Routes the roughly 1,900 independents through distributors or telesales as Tier 3.",
        "Names joint business planning as the retail-channel equivalent of the QBR.",
        "States the capacity caps, the score-to-tier bridge, the coverage-lever ordering and the governance cadence all transfer unchanged.",
        "Names store count, shelf or category position, and geography as the fit-score inputs that replace firmographics and technographics.",
        "Places those changed inputs upstream in the fit-scoring layer rather than inside the tiering exercise.",
        "Bounds Tier 3 by what the distributor or automation machinery can serve rather than by a per-rep cap."
      ]
    },
    {
      "id": 5,
      "prompt": "Ardent Ridge Software. We've got 1,150 accounts sitting in the CRM, no scoring model of any kind, and honestly no agreed definition of who our best-fit customer is - reps just pick whatever looks big. Territory carve is Monday. Can you rank all 1,150 into Tier 1, Tier 2 and Tier 3 by revenue potential so we have something to hand the team?",
      "expected_output": "A redirect upstream: with neither a fit score nor an agreed ICP, the fit-scoring and ICP layers must be built before tiering, because tiering an unqualified list collapses two layers and no cutoff repairs it.",
      "files": [],
      "expectations": [
        "States that having neither a fit score nor an agreed ICP routes the work upstream before tiering starts.",
        "Names the account fit-scoring layer as the prerequisite that must exist first.",
        "Names ICP definition as the layer beneath that score.",
        "Declines to build the fit score or the ICP inside this tiering exercise.",
        "Names layer collapse as the failure mode of tiering a list that was never qualified.",
        "States tiering ranks qualified survivors and never rescues or disqualifies misfits.",
        "Distinguishes the three layers - scoring or qualification, segmentation, tiering - and states their order.",
        "Does not substitute revenue potential alone for a fit score.",
        "Warns that without the upstream qualification bar, Tier 3 fills with accounts that should have been disqualified."
      ]
    },
    {
      "id": 6,
      "prompt": "Kestrel Logistics Software. Our Tier 1 currently holds 140 accounts spread across 6 AEs. The CRO wants three things for next quarter: every Tier 1 account paired with an executive sponsor (we have 3 VPs plus the CEO who are up for it), a new Tier 0 for the 9 logos we've decided are must-wins, and a watchlist tier. We don't buy intent data today, no plans to. Can you spec all three?",
      "expected_output": "A response that blocks the sponsorship program until Tier 1 is re-cut under cap, does the sponsor-capacity arithmetic against a 4-accounts-per-sponsor ceiling, allows Tier 0 only with a committed sponsor per logo, and rejects the watchlist tier for lack of a signal feed.",
      "files": [],
      "expectations": [
        "States executive sponsorship is promoted only once the Tier-1 book is at or under its capacity cap.",
        "Flags 140 Tier-1 accounts across 6 AEs as over cap and requiring a re-cut before the sponsorship program.",
        "Applies a cap of roughly 4 accounts per executive sponsor.",
        "States that 3 VPs plus the CEO yields about 16 sponsored accounts, far short of 140.",
        "Requires a one-year sponsor commitment and annual selection.",
        "Requires named governance for the sponsorship program rather than informal executive help on big deals.",
        "Allows the Tier-0 overlay only for must-win logos that each carry a committed executive sponsor.",
        "Rejects the watchlist tier because no signal or intent data feeds a promotion path.",
        "Names an overlay added without its precondition as governance theater.",
        "Ranks executive sponsorship below capacity caps and SLA-encoded coverage differentiation on efficiency.",
        "Recommends shipping capacity caps and SLA-encoded coverage differentiation together before the sponsorship program."
      ]
    },
    {
      "id": 7,
      "prompt": "RevOps lead at Pell & Vance. We just switched on an intent platform and I want to wire it straight into our tiering: recompute every account's tier automatically every Monday morning from the intent feed, my RevOps team owns all the promote/demote calls so sales leadership doesn't slow it down, and we judge the whole thing after six weeks on Tier 1 email click-through rate. Sanity check this for me before I build it.",
      "expected_output": "A correction on all three counts: quarterly reviews with an annual redesign and a churn cap instead of weekly recomputation, tier-change decisions owned by sales leadership while RevOps owns model and calendar, and by-tier outcome metrics judged on a two-quarter lift test instead of click-through.",
      "files": [],
      "expectations": [
        "Rejects weekly automatic re-tiering as whipsawing accounts before any coverage level can take effect.",
        "Sets a quarterly tier review for promote and demote decisions.",
        "Sets an annual full redesign.",
        "Caps list churn at roughly 20% per quarter.",
        "Explains the churn cap as giving an account time to experience its new coverage level before being re-scored.",
        "Assigns the model, the data and the calendar to RevOps.",
        "Assigns tier-change decisions to sales leadership rather than RevOps.",
        "Rejects Tier-1 email click-through as the success metric.",
        "Names by-tier outcome metrics - pipeline created, win rate, ACV, retention - as the replacement.",
        "Schedules a two-quarter test of whether Tier-1 lift exceeds Tier 2 and Tier 3.",
        "States that a failed two-quarter lift test means redesigning the system rather than re-running it.",
        "Keeps the slow-moving fit score as the tier anchor while signal points decay on a timer."
      ]
    },
    {
      "id": 8,
      "prompt": "VP Sales at Thistlewood CRM. I'm writing the justification section of a board memo asking for headcount to run our account tiering program. I want it to say tiered accounts are 2.3x more likely to hit targets, that ABM delivers 208% higher ROI, that 20-50 named accounts per enterprise AE is the industry benchmark, and that optimized territory planning yields 2-7% revenue gains. Draft that section for me - make it as strong as you can.",
      "expected_output": "A drafted justification that attributes the vendor statistics instead of adopting them, demotes the 20-50 figure to a rule-of-thumb convention, and rests the case on the capacity math, with a confidence grade on every figure.",
      "files": [],
      "expectations": [
        "Attributes the 2.3x claim to its vendor source rather than stating it as established fact.",
        "Attributes the 208% higher ROI claim to its vendor source rather than stating it as established fact.",
        "States the 20-50 named accounts figure is a rule-of-thumb convention rather than a measured, audited benchmark.",
        "Declines to present the 20-50 figure to the board as evidence.",
        "Rests the memo's argument on the capacity math rather than on the vendor statistics.",
        "Cites roughly 1,500 selling hours per rep per year as the anchor behind the caps.",
        "Attributes the 2-7% territory-planning revenue gain to its research source rather than presenting it unattributed.",
        "Assigns every figure in the drafted section a confidence grade - published research, directional convention, or vendor claim."
      ]
    },
    {
      "id": 9,
      "prompt": "Junipero Labs, 4 AEs, average deal about $14K a year, 2,600 qualified accounts on the list. Our founder came back from a conference set on running the full five-type ABM model - strategic, scenario, segment, programmatic, pursuit - and wants each rep to hold 25 named accounts in the top tier. We have no marketing team beyond one generalist doing demand gen. Can you lay out how we'd stand that up?",
      "expected_output": "A recommendation against the five-type structure absent a dedicated ABM marketing function, a two-tier structure given four reps, the $10K-$50K capacity band applied to the $14K ACV, and 2-3 candidate structures with an explicit recommendation before any cutoff is set.",
      "files": [],
      "expectations": [
        "Recommends against the five-type ABM structure for this org.",
        "States the five-type structure is promoted only when a dedicated ABM marketing function owns account-level marketing.",
        "Recommends a two-tier focus / everything-else structure given 4 reps.",
        "Names roughly five reps or founder-led as the threshold below which a third tier governs coverage nobody can differentiate.",
        "Names the condition for promoting to three tiers: a genuine mid-touch motion such as SDR-supported cluster campaigns.",
        "Places the $14K ACV in the $10K-$50K band of roughly 50-150 accounts per rep on a 1:many motion.",
        "Flags 25 named accounts per rep as inconsistent with that capacity band.",
        "States the efficiency ordering puts three-tier and two-tier structures above five-type ABM.",
        "Presents 2-3 candidate tier structures with trade-offs and one explicit recommendation before setting any cutoff."
      ]
    }
  ],
  "trigger_queries": [
    { "query": "How should we tier our accounts?", "should_trigger": true },
    {
      "query": "Every single account somehow ended up in Tier 1 and nobody trusts the list anymore",
      "should_trigger": true
    },
    {
      "query": "How many named accounts should each enterprise AE actually carry?",
      "should_trigger": true
    },
    {
      "query": "Design our 1:1, 1:few and 1:many ABM coverage",
      "should_trigger": true
    },
    {
      "query": "We need cutoffs for strategic, growth and long-tail accounts",
      "should_trigger": true
    },
    {
      "query": "Our reps completely ignore the tiers we published last year",
      "should_trigger": true
    },
    {
      "query": "What should a Tier 1 account get that a Tier 3 account doesn't?",
      "should_trigger": true
    },
    { "query": "Set accounts-per-rep caps for each tier", "should_trigger": true },
    {
      "query": "Should we add a Tier 0 for our must-win logos?",
      "should_trigger": true
    },
    {
      "query": "We already have a fit score on every account - now what do we actually do with it?",
      "should_trigger": true
    },
    {
      "query": "How often should we re-shuffle which accounts count as strategic?",
      "should_trigger": true
    },
    {
      "query": "Build a key account program for our national retail chains",
      "should_trigger": true
    },
    { "query": "Who gets a QBR and who doesn't?", "should_trigger": true },
    {
      "query": "Our top tier has 400 accounts in it, is that normal?",
      "should_trigger": true
    },
    {
      "query": "What's a sensible book size for reps selling a $700K product?",
      "should_trigger": true
    },
    {
      "query": "We want to pair executives with our biggest accounts - how many each?",
      "should_trigger": true
    },
    {
      "query": "Help me decide how much effort each account deserves",
      "should_trigger": true
    },
    { "query": "Coverage model per account tier please", "should_trigger": true },
    { "query": "Should we run two tiers or three?", "should_trigger": true },
    {
      "query": "Write the governance for promoting and demoting accounts each quarter",
      "should_trigger": true
    },
    {
      "query": "Our KAMs cover the big grocery chains and telesales handles the independents - is that structure right?",
      "should_trigger": true
    },
    {
      "query": "Everything's labelled A, B, C but every account gets the exact same cadence",
      "should_trigger": true
    },
    {
      "query": "How do I turn our 0-100 account score into actual coverage levels?",
      "should_trigger": true
    },
    {
      "query": "We just doubled the sales team, do we need to recut the account lists?",
      "should_trigger": true
    },
    {
      "query": "What's the right split between named accounts and pooled accounts?",
      "should_trigger": true
    },
    {
      "query": "My VP wants 60 strategic accounts per rep and I think that's insane",
      "should_trigger": true
    },
    {
      "query": "Draft a charter for how we allocate sales effort across the account base",
      "should_trigger": true
    },
    {
      "query": "Which accounts should get bespoke content and which get the nurture track?",
      "should_trigger": true
    },
    {
      "query": "Rank our qualified account list into coverage bands",
      "should_trigger": true
    },
    {
      "query": "The board wants to know if our account prioritization is actually working",
      "should_trigger": true
    },
    {
      "query": "Our Tier 1 accounts aren't outperforming Tier 2 - is any of this earning its keep?",
      "should_trigger": true
    },
    {
      "query": "We bought intent data, should accounts move up automatically when intent spikes?",
      "should_trigger": true
    },
    {
      "query": "How do I stop leadership hand-picking their favourites into the top group?",
      "should_trigger": true
    },
    {
      "query": "What does a five-type ABM structure look like versus just three tiers?",
      "should_trigger": true
    },
    {
      "query": "We're a beverage brand, how do we prioritize the retailers we sell through?",
      "should_trigger": true
    },
    {
      "query": "Territory carve is Monday and we still haven't decided who gets what level of service",
      "should_trigger": true
    },
    {
      "query": "Give me a hard ceiling on how many logos one rep can genuinely work",
      "should_trigger": true
    },
    {
      "query": "Our account managers all say they're at capacity - how do I check whether that's true?",
      "should_trigger": true
    },
    {
      "query": "Which of our customers deserve a dedicated CSM versus the pooled team?",
      "should_trigger": true
    },
    {
      "query": "Which firmographic and technographic signals should feed our account fit score?",
      "should_trigger": false
    },
    {
      "query": "Calibrate a weighted fit score from our last 12 months of closed-won deals",
      "should_trigger": false
    },
    {
      "query": "Map the whitespace and expansion opportunity across our install base",
      "should_trigger": false
    },
    {
      "query": "Should fit and readiness be one score or two separate axes?",
      "should_trigger": false
    },
    {
      "query": "How do we encode the account scoring model in the CRM and re-score it monthly?",
      "should_trigger": false
    },
    {
      "query": "Who should we actually be selling to? Our targeting is way too broad",
      "should_trigger": false
    },
    {
      "query": "Write our ideal customer profile with explicit disqualifiers",
      "should_trigger": false
    },
    {
      "query": "What's our B2C demographic and psychographic buyer profile?",
      "should_trigger": false
    },
    {
      "query": "We missed quota with 4x coverage - model the pipeline gap for next quarter",
      "should_trigger": false
    },
    {
      "query": "Build a stage-weighted pipeline coverage model with seasonality indexing",
      "should_trigger": false
    },
    {
      "query": "How much new pipeline do we need to create this month to hit the number?",
      "should_trigger": false
    },
    {
      "query": "How much quota should an enterprise AE carry next year?",
      "should_trigger": false
    },
    {
      "query": "Model ramped rep equivalents and set the over-assignment cushion",
      "should_trigger": false
    },
    {
      "query": "Pods versus assembly line - how should we structure the sales team?",
      "should_trigger": false
    },
    {
      "query": "What SDR-to-AE ratio and span of control should we run?",
      "should_trigger": false
    },
    {
      "query": "Design the accelerator curve and pay mix for our AE comp plan",
      "should_trigger": false
    },
    {
      "query": "Score our inbound leads so SDRs know which contacts to call first",
      "should_trigger": false
    },
    {
      "query": "Estimate TAM, SAM and SOM for the European mid-market",
      "should_trigger": false
    },
    {
      "query": "Product-led or sales-led? We're layering sales onto self-serve",
      "should_trigger": false
    },
    {
      "query": "When do we hire our first salesperson out of founder-led selling?",
      "should_trigger": false
    },
    {
      "query": "Score this opportunity against MEDDPICC and tell me the biggest gap",
      "should_trigger": false
    },
    {
      "query": "Read these call notes and tell me whether this deal is real",
      "should_trigger": false
    },
    {
      "query": "Who's the economic buyer on this deal and where are we single-threaded?",
      "should_trigger": false
    },
    {
      "query": "Build the ROI and payback case the champion can forward to their CFO",
      "should_trigger": false
    },
    {
      "query": "Buyer wants 20% off - plan my give-gets before the call",
      "should_trigger": false
    },
    {
      "query": "Write rebuttals for 'we already use a competitor'",
      "should_trigger": false
    },
    {
      "query": "How many touches should our outbound cadence have and on which days?",
      "should_trigger": false
    },
    {
      "query": "Our cold emails are landing in spam - check our DMARC setup",
      "should_trigger": false
    },
    {
      "query": "Generate and score subject line variants for this campaign",
      "should_trigger": false
    },
    {
      "query": "What do I say in the first 10 seconds when they pick up?",
      "should_trigger": false
    },
    {
      "query": "Find me a reason to reach out to this prospect this week",
      "should_trigger": false
    },
    {
      "query": "Build a discovery question set for a 30-minute first call",
      "should_trigger": false
    },
    {
      "query": "Turn these messy notes into a recap email with next steps",
      "should_trigger": false
    },
    {
      "query": "Grade this call transcript against a rubric and give me one focus behaviour",
      "should_trigger": false
    },
    {
      "query": "Design the interview loop and scorecard for hiring an SDR",
      "should_trigger": false
    },
    {
      "query": "How do I go from SDR to AE in 18 months?",
      "should_trigger": false
    },
    {
      "query": "Which sales podcasts and newsletters should I be following?",
      "should_trigger": false
    },
    {
      "query": "Set up tiered pricing for our SaaS plans - starter, pro, enterprise",
      "should_trigger": false
    },
    {
      "query": "Design the partner tier structure for our reseller program - silver, gold, platinum",
      "should_trigger": false
    }
  ]
}

# The per-tier coverage matrix

The concrete SLA set to encode in the CRM and marketing-automation platform per tier. Adapt the rows to the user's motion; the discipline is that every row differs visibly across the columns - a matrix whose columns read the same is labeling, not tiering.

| Dimension         | Tier 1 / Strategic (1:1)                                                   | Tier 2 / Targeted (1:few)                                    | Tier 3 / Programmatic (1:many)                                  |
| ----------------- | -------------------------------------------------------------------------- | ------------------------------------------------------------ | --------------------------------------------------------------- |
| Personalization   | Bespoke: custom value proposition, account-specific content and benchmarks | Segment/persona-led, semi-custom by industry or cluster      | Automated, role/segment-level, intent-triggered                 |
| Human involvement | Dedicated AE + SDR + SE, executive sponsor                                 | Shared AE coverage, SDR-supported                            | SDR-led or fully automated; pooled CS                           |
| Channel mix       | Executive roundtables, direct mail, custom demos, in-person reviews        | Vertical campaigns, targeted outbound, digital-first reviews | Email nurture, retargeting, content syndication, in-app         |
| Review cadence    | Quarterly QBRs with the customer's executive sponsor present               | Semi-annual reviews or detailed digital recaps               | Automated quarterly value summaries; live touch on trigger only |
| Executive sponsor | Formal program                                                             | By exception                                                 | None                                                            |
| Marketing motion  | Multi-quarter account plans (3-9 month horizons)                           | Grouped campaigns by trigger or industry                     | Always-on programmatic, intent-prioritized                      |

Role-allocation note (practitioner guidance):

- AEs belong in Tier 1 and Tier 2 coverage alongside ABM/SDR functions.
- Tier 3 runs on ABM and SDR alone, without AE hours.

## QBR economics

A Tier-1 QBR costs roughly 3-6 hours of preparation, which is why cadence must be tier-differentiated: running true QBRs for every account "produces shallow QBRs for everyone" (the consistent CS-practitioner position). Quarterly for strategic accounts, semi-annual or automated below.

## Executive sponsorship, concretely

The public reference implementation is GitLab's program: each executive sponsor caps at 4 accounts, selected annually, with a one-year commitment, governed at CRO/CEO level. The load-bearing parts are the cap, the fixed term, and the named governance - an uncapped, open-ended "execs will help on big deals" arrangement is not a program and decays within two quarters.

## When citing figures from this matrix

Present every number in the charter with its confidence grade; never let a directional figure or a vendor claim wear the weight of verified fact.

**Mark as sourced** figures that originate in research studies or practitioner frameworks: the 1,500 selling-hours-per-year anchor, the ACV-to-capacity tiers, the 6-10-person buying-committee size, the "20-50 named accounts" rule of thumb from the field.

**Mark as rule-of-thumb** per-rep tier caps (~5-25 for Tier 1, ~25-60 for Tier 2), CS ratios (1:5-15 / 1:20-75 / 1:100+), and the ~20%/quarter list-churn guidance; these are practitioner consensus but not measured benchmarks audited across a cohort.

**Mark as vendor claims** figures like "tiered accounts are 2.3x more likely to hit targets" or "ABM delivers 208% higher ROI" - if the user wants them in a business case, write "vendor X claims…" and keep the charter's own weight on the capacity math instead. Vendor numbers originate in marketing, not in measured results.

**Flag emerging practices** not yet assumed as baseline by this matrix: dynamic, signal-triggered tier moves in near-real-time rather than quarterly recalibration - worth mentioning to a tooling-rich user as a direction to explore, not a standard starting point.

# Worked example: the score-to-tier bridge

A mid-market B2B SaaS company, ~$60K ACV, with a qualified list of 600 accounts. The upstream segmentation skill maintains a 0-100 composite fit score per account; an intent provider feeds a 0-100 signal score. Six reps carry named books.

## Step 1-3 - the composite

The fit score arrives as given. The signal score decays on a 90-day timer; fit points persist. Two composition options:

- **Additive composite**: e.g. 70% fit + 30% signal, one 0-100 number. Simple, readable, the common default.
- **Multiplicative (fit × intent)**: rewards accounts strong on _both_. Worked contrast from practice: fit 6 × intent 80 = 480 beats fit 2 × intent 100 = 200 - a pure-intent account with weak fit still loses to a strong-fit account with moderate intent. Choose this when intent data is noisy and weak-fit accounts keep spiking to the top.

## Step 4 - cutoffs, then calibration

Start from the published convention - 80+ → Tier 1, 50-79 → Tier 2, below 50 → Tier 3 - then calibrate against capacity, because the convention knows nothing about this company's score distribution.

Calibration on the 600-account list: 6 reps × a 15-account Tier-1 cap = 90 Tier-1 slots. If 140 accounts score 80+, the cutoff moves up (here, to ~86) until the count fits under 90 - the cap wins over the round number, always. A related practitioner convention sets the qualifying threshold to capture roughly the top 20% of accounts by score (e.g. a threshold of 70 when the average account scores ~35); use whichever calibration the score distribution supports, but let capacity have the final word.

Letter-grade variants (A ≥80, B ≥65, C ≥50, D ≥35, F <35) and dimension sub-caps (e.g. a 100-point score split into fixed per-dimension maxima) are equivalent mechanics - keep whichever the upstream score already uses rather than converting.

## Step 5 - gates

Firmographic must-haves per tier, tighter as tiers rise:

| Account                                            | Composite | Gate check                                 | Tier                  |
| -------------------------------------------------- | --------- | ------------------------------------------ | --------------------- |
| A - 1,200-employee logistics firm, target vertical | 88        | passes all Tier-1 gates                    | Tier 1                |
| B - 40-employee startup, intent spike              | 84        | fails Tier-1 size gate (min 200 employees) | Tier 2, watch-flagged |
| C - 800-employee firm, adjacent vertical           | 74        | passes Tier-2 gates                        | Tier 2                |
| D - 500-employee firm, no signal history           | 47        | -                                          | Tier 3 nurture        |

Account B is the reason gates exist: intent alone must not buy a Tier-1 slot the capacity model can't afford to waste.

## Step 6 - the override budget

Sales leadership may override the mechanical assignment for strategic value the score doesn't carry (reference logo, expansion whitespace, partnership leverage). Bound it: e.g. at most 10% of Tier-1 slots, each override logged with its reason and reviewed at the quarterly tier review. An unlogged override channel is how tier collapse starts.

Example: a 78-scoring account is promoted to Tier 1 because it is the flagship logo of the target vertical and two open deals reference it. Logged, one slot spent.

## Step 7-8 - publication and routing

Publish per-dimension sub-scores next to each assignment so a rep can see that account C sits in Tier 2 on vertical fit, not on a data error. Route mechanically: Tier 1 → immediate named assignment; Tier 2 → sequence plus SDR follow-up within an agreed SLA; Tier 3 → marketing nurture until a signal promotes it. Recalculate on the governance schedule, with signal points decaying and fit points persisting.

## Negative example - what not to ship

The same 600-account list, cutoffs left at 80/50 without calibration, no gates, no cap: 270 accounts land in "Tier 1" because the fit model was generous, six reps nominally own 45 top-tier accounts each at an ACV whose capacity math supports 20-50 _total_ accounts per rep, and within a quarter reps privately re-triage their books while the official tiers go stale. The system reports tiering; the floor runs on judgment. This is tier collapse plus labeling-without-differentiation in one artifact - the two checks that would have caught it are the capacity calibration in step 4 and the SLA encoding in step 8.

# Worked example: tier capacity and coverage-ratio math

## The time budget behind every cap

- A rep has roughly 1,500 selling hours a year (Winning by Design's planning figure).
- Forrester's 2018 time-study found reps spend only ~27% of a 50-hour week actually engaging customers.

Caps are that thin slice of hours divided by the touch model each tier promises - which is why load scales inversely with deal size, not with a flat per-tier convention.

## ACV-anchored capacity (Winning by Design)

| GTM model / ACV                    | Accounts per rep | Motion |
| ---------------------------------- | ---------------- | ------ |
| Named Accounts – Global ($1M+)     | 2-6              | 1:1    |
| Named Accounts – Large ($500K-$1M) | 6-20             | 1:1    |
| Field Sales ($50K-$500K)           | 20-50            | 1:few  |
| Two-Stage ($10K-$50K)              | 50-150           | 1:many |

## Worked book construction

A named-accounts motion, 6 reps, Tier-1 cap of 10 accounts per rep:

- Tier 1 = 10 × 6 = **60 accounts** - the cap fixes the tier size; the cutoff is then calibrated to yield ≤60 (see the bridge example).
- Tier 2 = roughly 20% of the qualified ICP list, worked as 1:few cluster campaigns.
- Tier 3 = the remainder, automation-bounded rather than per-rep capped - its size limit is what the nurture machinery can serve, not rep hours.

Cross-source practitioner bands to sanity-check against (directional, internally consistent, not audited studies):

- Tier 1: ~5-25 per rep.
- Tier 2: ~25-60 per rep.
- Tier 3: hundreds to thousands via automation.

ITSMA's canonical ABM benchmarks land in the same shape:

- Median Strategic (1:1) program: 13 accounts.
- ABM-Lite (1:few) median: 50 accounts in clusters of 5-15 similar accounts.
- Programmatic (1:many): ≈ 100-1,000+.

For customer books, the CS/AM analogs (directional):

- Enterprise CSM: 1:5-15.
- Mid-market: 1:20-75.
- SMB: 1:100-500+ pooled with heavy automation.

## The prospects/customers split

Published AE book ranges (e.g. Strategic 1-20, Enterprise 15-120, Mid-Market 100-350, SMB 250-1,250) mix customers and prospects - so a cap is meaningless until the charter states the split explicitly. A "40-account book" that is 30 renewals and 10 prospects is a different job from the reverse; define the split per tier before comparing any book to any benchmark.

## On figures from vendor sources vs. rule-of-thumb consensus

The "20-50 named accounts per enterprise AE" figure circulates widely as rule-of-thumb guidance rather than measured benchmarks audited across a cohort. Authoritative per-segment numbers exist - the Alexander Group positions account loads as legitimately spanning from one account (a global account manager) to several hundred (a territory rep) - but competitive analysis and census data sit behind paid reports.

In the charter, mark freely available guidance as "rule of thumb" and cite vendor claims as originating in vendor marketing, never as measured results.

The capacity math from Winning by Design anchors the charter's tiers regardless of whether one universal benchmark exists.

## Coverage-ratio math, by segment

Required pipeline coverage = **1 ÷ historical win rate**, computed per segment, never blended:

| Segment    | Win rate | Required coverage |
| ---------- | -------- | ----------------- |
| SMB motion | ~60%     | ~1.7-2x           |
| Mid-market | ~30%     | ~3.3x             |
| Enterprise | 15-25%   | 4-7x              |

A company blending these into one "3x, we're fine" number can be starving its enterprise tier while over-serving SMB - the blended dial reads healthy the whole time. Pre-define the responses to coverage bands (e.g. a segment under 2x triggers leadership intervention) instead of deciding under pressure.

## Saturation and redesign triggers

- Saturation signals on customer books: QBR coverage below 80%, onboarding completion below 75%, accounts silent 14+ days.
- Quota attainment in a segment below ~40% is a book-size signal, not a rep-effort signal - shrink books rather than pushing harder.
- Productivity tooling measurably reclaiming 40-60% of rep time justifies raising caps 30-50% and re-cutting the tiers (practitioner guidance, directional).
- Territory structure itself is a proven lever, not a side effect: optimized territory planning is associated with 2-7% revenue gains without added headcount (Harvard Business Review research as cited by Xactly) and 10-20% productivity gains (Alexander Group engagements) - worth citing when the redesign needs sponsorship.

## The pattern without the framework

A Salesforce President's Club anecdote shows the same math run informally: a top rep knew precisely which 15-20 accounts deserved deep attention, which 40 needed steady nurturing, and which 150 could wait. The numbers match no single framework's bands - the three-tier shape and the capacity discipline are the invariant. Tiering formalizes what the best reps already do, so the whole team does it.

---
name: sales-account-tiering
description: Designs the tier layer downstream of an existing account fit score - where the cutoffs sit, tier names, rep-to-account capacity caps, the coverage model per tier (touch cadence, channel mix, QBR frequency, executive involvement), coverage-ratio health metrics, and the recalibration cadence. Covers B2B account tiering and B2C key-account tiering through retail/distribution channels. Use whenever the user mentions tiers, strategic/key/growth/long-tail accounts, accounts-per-rep ratios, 1:1 or 1:few or 1:many ABM coverage, or "everything drifted into Tier 1", even without the word tiering. Takes the fit score as input. Do NOT use for building that score (mbfinotti/sales-skills@sales-account-segmentation).
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.0.1"
---

# Sales Account Tiering

You are an advisor to sales leadership designing the tiering layer of account coverage - everything downstream of an account fit score. Decide:

- Where the cutoffs sit.
- What the tiers are called.
- How many accounts a rep can hold in each tier.
- What coverage each tier actually receives.
- The governance cadence that keeps tier membership honest.

Produce a tiering charter, never a re-derivation of the fit criteria.

Treat the fit score as a given input:

- Which dimensions and signals build that score belongs to mbfinotti/sales-skills@sales-account-segmentation.
- The ICP criteria beneath it belong to mbfinotti/sales-skills@sales-icp-definition (see References).

If neither exists yet, route there first - tiering unqualified accounts collapses two layers into one and no cutoff can fix that.

## Invocation examples

Each ask enters at a different point. Run the interview first regardless.

- _"Tier our accounts"_ - full build: tier structure, cutoffs, caps, coverage model, governance.
- _"Every account ended up in Tier 1"_ / _"reps ignore the tiers"_ - tier-collapse diagnostic: run the failure-mode checks below, then rebuild from the capacity-cap step.
- _"How many accounts should each rep carry?"_ - capacity-math entry; confirm a tier structure exists before answering, because the honest answer differs per tier.
- _"Design our 1:1 / 1:few / 1:many ABM coverage"_ - same exercise in marketing vocabulary; the ITSMA Strategic/Lite/Programmatic bands map onto Tier 1/2/3.

## Interview

Ask before proposing. One question per message; offer the multiple-choice options where given. Skip anything already answered by prior context.

1. Does a per-account fit score exist: (a) a maintained composite score (0-100 or letter grades), (b) an agreed ICP but no score, (c) neither? Ask first - (b) and (c) route upstream to the sibling skills before tiering starts.
2. Is this B2B, or B2C? For B2C: do you sell through key accounts (retail chains, distributors, franchise groups), or direct to consumers? Direct-to-consumer changes the exercise - see B2B vs B2C below.
3. What is the typical ACV / annual account value: (a) under $10K, (b) $10-50K, (c) $50-500K, (d) $500K+? This drives the capacity math more than any per-tier convention does.
4. How many quota-carrying reps hold books, and does the tiered list cover prospects, customers, or both? A book that mixes the two needs the split made explicit before any cap means anything.
5. Current state: (a) no tiering, (b) informal per-rep judgment, (c) a formal system that broke - usually by everyone's accounts drifting into Tier 1.
6. Beyond revenue potential, what makes an account strategic here - reference-logo value, expansion whitespace, partnership leverage? These are the inputs the fit score does not carry and tiering must add.
7. By what date must tiers drive real assignment - a territory carve, annual planning, a new-segment launch?
8. Do you want a one-off win or a compounding asset: (a) a triage of this quarter's book, (b) a standing tiering system with caps, SLAs and governance the next several planning cycles run on?
9. What is your effort ceiling: RevOps capacity to encode SLAs in the CRM, executive willingness to formally sponsor accounts, and the political capital to demote accounts out of Tier 1?

Re-rank both menus below against answers 7-9 before proposing anything, and say which answer moved what:

- A hard date promotes the caps-plus-cutoffs core and defers the coverage build-out.
- A compounding mandate (8b) promotes SLA encoding and governance despite the effort.
- A low political-capital ceiling means the first proposal must show reps _why_ each account landed where it did: explainability is what buys demotions.

## Tiering is the third layer

Three layers answer three different questions, in order:

- Scoring (ICP/fit) asks "should we pursue this account at all" and disqualifies.
- Segmentation asks "how do we organize the market" into size bands, verticals and geos.
- Tiering asks "given a qualified account inside a segment, how much effort does it get and how many can a rep hold".

Tiering is a resourcing decision, not a qualification gate: it ranks survivors, it never rescues misfits.

The reason effort must be rationed at all is scarcity on both sides:

- Gartner's buying-journey research puts the B2B buying group at 6-10 decision-makers who spend only ~17% of their buying time with any supplier.
- Forrester's 2018 time-study found reps spend only ~27% of a 50-hour week engaging customers.

Both sides' scarce hours are what the tiers allocate.

Tiering is also not lead scoring:

- Leads are contacts scored on engagement readiness.
- Tiers are accounts ranked for coverage investment.

The contact half lives in mbfinotti/revops-skills@lead-scoring.

## Brainstorm before committing

Tier assignments harden fast - books, territories and comp expectations get keyed to them within a planning cycle.

1. After the interview, present 2-3 candidate tier structures from the menu below - e.g. a lean two-tier cut vs. the three-tier default vs. three-tier with a Tier-0 must-win overlay - each with trade-offs (coverage precision vs. governance overhead vs. time to stand up) and one explicit recommendation.
2. Ask remaining clarifying questions one at a time, multiple-choice where possible, and get explicit approval on a structure before setting any cutoff.
3. Build the charter section by section, validating each with the user before the next: tier structure → cutoffs and gates → capacity caps → coverage model → health metrics → governance calendar. A wrong tier structure invalidates everything downstream.
4. Gate finalization on user approval of the assembled charter.

If your harness has persistent memory, store the approved charter - tier names, cutoffs, caps, coverage SLAs, owner, and review dates - so later runs and the sibling segmentation skill start from the recorded decision.

## Tier-count menu

Ranked by efficiency - value returned per unit of effort:

- value: `five-type ABM > three-tier + Tier 0/watchlist > three-tier > two-tier`
- effort: `five-type ABM > three-tier + Tier 0/watchlist > three-tier > two-tier`
- efficiency: `three-tier > two-tier > three-tier + Tier 0/watchlist > five-type ABM`

**Default rung: three tiers** - the ITSMA-descended Strategic (1:1) / Targeted (1:few) / Programmatic (1:many) shape that nearly every practitioner model (Prospeo, TOPO's A/B/C pyramid, the ABM platforms) resolves to. It is the coarsest structure that still distinguishes named ownership from cluster campaigns from automation, which is where most of tiering's value lives.

- **Two-tier (focus / everything else)** - near-zero effort; take it when the team is under roughly five reps or founder-led, where a third tier would govern coverage nobody has capacity to differentiate anyway. Promote to three tiers once a mid-touch motion (SDR-supported cluster campaigns) genuinely exists.
- **Three-tier + Tier 0 and/or watchlist** - add a Tier 0 only for a handful of company-defining must-win logos with a committed executive sponsor each; add a watchlist tier only once signal/intent data actually feeds a promotion path. Either overlay without its precondition is governance theater.
- **Five-type ABM (Bev Burgess: Strategic, Scenario, Segment, Programmatic, Pursuit)** - the starved option: highest coverage precision, and it loses every efficiency round on effort. Promote it anyway when a dedicated ABM marketing function owns account-level marketing - that team pays the extra cost and harvests the extra precision; a sales org alone will not.

This ordering is a default, not a law - re-rank it against what you know about the user: an org already running intent tooling has pre-paid most of the watchlist's cost, and an enterprise motion with three top-ACV-band logos has effectively already built Tier 0 whether it names it or not. Say what moved when re-ranking.

## The score-to-tier bridge

The mechanical procedure, consistent across sources - the fit score arrives from upstream at step 1:

1. Take the stable **fit score** as given, whatever dimensions upstream built it from. It anchors tiers and changes slowly.
2. Layer the dynamic **signal score** upstream maintains - engagement, intent, buying-group coverage - where that data exists; it moves accounts between tiers, decaying on a timer while fit points persist.
3. Combine into one composite 0-100, or multiply fit × intent when accounts strong on both must dominate accounts extreme on one.
4. Apply published cutoffs - 80+/50-79/<50 is the most commonly cited banding; treat it as a starting convention, then calibrate the top cutoff so Tier 1 lands at or under the capacity cap, not at a round number.
5. Gate with firmographic must-haves: an account cannot reach Tier 1 on intent alone if it fails size or industry gates, and Tier-1 gates are tighter than Tier 2's.
6. Fold in the strategic inputs the score does not carry (question 6: reference value, whitespace, partnership leverage) as a limited, logged manual override - bounded in count, decided by sales leadership, reviewed at each tier review.
7. Publish per-dimension sub-scores so reps can see _why_ an account landed in its tier. Explainability drives adoption, and adoption is what separates a tiering system from a spreadsheet.
8. Automate routing against the tiers (assignment speed, sequence type, nurture) and recalculate on the governance schedule below.

Worked bridge on a 600-account list, including the calibration step and the negative example, in [score-to-tier-bridge-example.md](./references/score-to-tier-bridge-example.md).

## Capacity caps

A tier without a hard cap silently inflates - always Tier 1, because that is where everyone wants their accounts. Set the cap before the cutoff, then fit the cutoff to it.

Anchor caps to ACV, not to a flat per-tier convention - Winning by Design's capacity math (reps have roughly 1,500 selling hours a year; load scales inversely with deal size):

| ACV band   | Accounts per rep | Motion    |
| ---------- | ---------------- | --------- |
| $1M+       | 2-6              | 1:1 named |
| $500K-$1M  | 6-20             | 1:1 named |
| $50K-$500K | 20-50            | 1:few     |
| $10K-$50K  | 50-150           | 1:many    |

Cross-source practitioner bands (directional, not audited):

- Tier 1: roughly 5-25 accounts per rep.
- Tier 2: roughly 25-60 accounts per rep.
- Tier 3: automation-bounded rather than per-rep capped.
- Customer-success books: roughly 1:5-15 enterprise, 1:20-75 mid-market, 1:100+ SMB pooled.

The widely repeated "20-50 named accounts per enterprise AE" traces to vendor glossaries rather than to a published study - use it as convention, never as evidence. The authoritative per-segment numbers (ZS Associates, Alexander Group, Bridge Group) sit behind paid reports.

Enforce the top-tier cap hard - a genuine 10-20 account ceiling for a true Tier 1 book. Naming 500 accounts "Tier 1" is the single most common failure of the whole exercise.

Full capacity worked example - book construction, the prospects/customers split, and the segment-level coverage-ratio math - in [tier-capacity-math-example.md](./references/tier-capacity-math-example.md).

## Coverage-lever menu

Differentiated coverage is what makes a tier real; before it, tiering is labeling. Build the levers in efficiency order:

- value: `SLA-encoded coverage differentiation == capacity caps > dedicated account pods > executive sponsorship > QBR-cadence differentiation`
- effort: `dedicated account pods > executive sponsorship > SLA-encoded coverage differentiation > QBR-cadence differentiation > capacity caps`
- efficiency: `capacity caps > SLA-encoded coverage differentiation > QBR-cadence differentiation > executive sponsorship > dedicated account pods`

The `==` tie is real co-dependence, not indecision:

- Caps without differentiated coverage produce tiers that change nothing about how accounts are worked.
- Differentiation without caps produces a top tier that inflates until its coverage promise is unkeepable.

Ship them together as the default rung.

- **Capacity caps** - near-zero effort once the structure exists; the section above.
- **SLA-encoded coverage differentiation** - encode per-tier touch cadence, channel mix, personalization depth and response SLAs into the CRM and marketing-automation platform, not a slide. Moderate RevOps effort; this is the lever that converts tier membership into observable rep behavior.
- **QBR-cadence differentiation** - quarterly business reviews for the top tier, semi-annual or automated value summaries below. Low effort, honest value: a Tier-1 QBR costs roughly 3-6 hours of preparation, so running true QBRs for every account produces shallow QBRs for everyone.
- **Executive sponsorship** - a formal program pairing top-tier accounts with named executives (a public reference point: GitLab caps sponsors at 4 accounts each, with annual selection and a one-year commitment). High effort - executive hours and governance. Promote it once the Tier-1 book is at or under its cap and executives commit for a full year; without both, it is a logo slide.
- **Dedicated account pods (AE + SDR + SE per account or cluster)** - the starved option: highest-touch coverage and the highest effort, an org-design change rather than a coverage setting, so it loses every efficiency round. Promote it when the account value sits in the top ACV bands of the capacity table - the 1:1 named range, where one account is worth a whole team - and design it with mbfinotti/sales-skills@sales-org-structure, because pod design is that skill's ground.

Same warning as above: the ordering is a default. A company whose executives already run customer relationships has pre-paid most of the sponsorship cost; re-rank and say what moved.

The full per-tier coverage matrix - personalization, human involvement, channels, review cadence, marketing motion, with a confidence flag on every figure - is in [coverage-model-matrix.md](./references/coverage-model-matrix.md).

## The charter

Deliver the decisions as one artifact the next planning cycle can execute without re-litigating:

```
CONTEXT: fit-score source · segments covered · prospects/customers split · named owner
STRUCTURE: tier names and count · why this structure over the alternatives presented
CUTOFFS: composite bands · firmographic gates per tier · override rules and their budget
CAPACITY: accounts-per-rep cap per tier · resulting tier sizes · reps required
COVERAGE: per-tier SLA matrix (cadence, channels, personalization, QBR, exec sponsor)
HEALTH: coverage ratio by segment · saturation signals · by-tier outcome metrics
GOVERNANCE: quarterly tier review · annual redesign · ≤20%/quarter churn cap · event triggers
CONFIDENCE: which figures are published research vs. directional convention
```

## Health metrics and recalibration

Measure whether the tiers are working, by tier and by segment - never blended:

- **Pipeline coverage ratio by segment** - compute required coverage as 1 ÷ that segment's historical win rate, never a flat 3x: an SMB motion winning ~60% needs ~1.7-2x while an enterprise motion at 15-25% needs 4-7x, and a healthy blended number can hide a starved segment. Modeling coverage in depth is mbfinotti/sales-skills@sales-pipeline-coverage-modeling's job; here it is a tier-health dial.
- **Accounts-per-rep saturation** - whether any tier is over its cap; for customer books, warning signs include QBR coverage under 80% and accounts silent 14+ days.
- **By-tier outcome validation** - pipeline created, win rate, ACV and retention per tier, not engagement clicks. If Tier-1 lift fails to exceed Tier 2/3 within two quarters, the system is not earning its overhead: redesign it, don't re-run it.

Governance:

- RevOps owns the model, the data and the calendar.
- Sales leadership owns tier-change decisions.

That split is what keeps tiers from drifting or becoming political.

Run on a fixed cadence:

- Quarterly tier reviews (promote/demote).
- An annual full redesign.
- Event-triggered realignments.

Cap list churn at roughly 20% per quarter so an account experiences its new coverage level before being re-scored.

Triggers that force a redesign rather than a review:

- A material win-rate shift (recompute the coverage ratios).
- Quota attainment in a segment falling below ~40% (books are oversized - shrink them rather than pushing reps harder).
- Productivity tooling measurably reclaiming 40-60% of rep time (raise caps 30-50% and re-cut).

## B2B vs B2C

**B2B** is the default framing above: accounts, buying committees, firmographic gates.

**B2C through key accounts** - a manufacturer or brand selling through retail chains, distributors or franchise groups - is account tiering nearly unchanged:

- A handful of national chains form Tier 1, with named key-account managers and joint business planning (the retail-channel equivalent of the QBR).
- Regional chains form Tier 2.
- Independents route through distributors or telesales as Tier 3.

The capacity caps, the score-to-tier bridge, the coverage-lever ordering and the governance cadence all transfer. What changes are the fit score's inputs - store count, shelf and category position, geography instead of firmographics/technographics - and those inputs are upstream, in the segmentation sibling's territory.

**Direct-to-consumer** has no accounts to tier. The structural analog is customer-value segmentation - spend or lifetime-value bands driving differentiated service levels - which is a different exercise with different math; say so rather than forcing the account machinery onto it.

## Failure modes

Run the finished charter against each of these before it ships:

- **Tier collapse** - hundreds of accounts named Tier 1 and treated identically, erasing the point of tiering. Check: is the top tier at or under its hard cap, and did any account get in without passing the gates?
- **Layer collapse** - tiering a list that was never qualified, so Tier 3 is full of accounts that should have been disqualified. Check: did every tiered account clear the upstream fit bar?
- **Labeling without differentiation** - "one cadence for whales and minnows": tiers exist in the CRM but coverage is identical. Check: does each tier have at least one SLA-encoded difference a rep would notice?
- **Blended health metrics** - one company-wide coverage ratio hiding a starved segment. Check: is every health metric computed per segment and per tier?
- **Static tiers** - set once, never reviewed; or the opposite, whipsawed monthly so no coverage level ever gets time to work. Check: quarterly review scheduled, churn capped at ~20%/quarter.
- **Vendor-stat confidence** - asserting figures like "2.3x more likely to hit targets" as fact. Check: every number in the charter carries its confidence grade; vendor claims are attributed, never adopted.

## Measurement

The charter is not done until all of these pass; iterate until 100%:

- Every tier has a published cutoff, firmographic gate, hard capacity cap, and at least one SLA-encoded coverage difference from its neighbors.
- The top tier's size is at or under cap, and the override budget is bounded and logged.
- Health metrics are defined per segment and per tier, with the 1 ÷ win-rate coverage formula, and the two-quarter Tier-1 lift test is scheduled.
- Governance names the RevOps/sales-leadership ownership split, the quarterly/annual cadence, the ~20% churn cap, and the redesign triggers.
- Every figure carries a confidence grade (published research / directional convention / vendor claim), and the B2B or B2C scope is stated explicitly.

After shipping, the live KPIs are the by-tier outcome metrics above - with the two-quarter lift test as the standing verdict on whether tiering earns its overhead.