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Case Study - Expert Review

40 salespeople. Their full book. Same afternoon.

A third-party logistics provider needed per-product TAM for every account in the book - the kind of number only the AE who owns the account actually knows. They got structured estimates back the same day. No custom Excels. No explanatory calls.

CustomerB2B Logistics · 40-person Sales Org

Before

A master Excel filtered into 40 per-seller tabs. A long email explaining which columns to cross-reference. A week of 1:1 walkthroughs to show each AE what the task even was. Submissions came back late, inconsistent, and incomplete - and the sellers resented the time they didn't spend selling.

After

Each AE opens a page per account. Their customer's spend history, firmographics, and third-party logistics signals are already on it. One structured question - per-product TAM with confidence - sits next to the context. Fat-fingers get flagged in the moment. The whole book came back in an afternoon.


Results

The whole book, back the same afternoon.

40

AEs reviewed their book

~800

per-product TAM estimates

0

explanatory 1:1s

1 afternoon

end to end

Every estimate is typed, contextual, and ready to feed the TAM model - not buried in a spreadsheet someone has to re-parse.


One account, one ask

All the context. Right next to the question.

Here's what a single AE sees for a single account. Internal spend, firmographics, third-party signals - already on the page. The only thing left is the part only they know.

QuikMart Distributors

Regional grocery chain · 14 DCs across the Midwest

ReviewerJordan · AE, West

Context on the page

  • Spend with us (last yr)

    $412K

  • Public revenue band

    $80M – $120M

  • 3rd-party logistics spend est.

    ~$3.8M / yr

  • Products we don't sell them

    Cold chain · Last-mile · Reverse logistics

Footprint

14 DCs · Midwest

quikmartdistributors.com

About us

Fresh groceries, every aisle. Every day.

Family-owned since 1987. Serving the Midwest from 14 distribution centers with a frozen and chilled-heavy SKU mix.

What Jordan is asked

“For each product below, estimate QuikMart's annual TAM and your confidence.”

Cold chain

Your estimate
$TAM / yr
LowMedHigh

Their SKU mix is ~35% frozen / chilled.

Last-mile delivery

Your estimate
$TAM / yr
LowMedHigh

14 DCs covering dense metros - likely buyer.

Reverse logistics

Your estimate
$TAM / yr
LowMedHigh

Private label returns currently handled in-house.

Jordan owns QuikMart. No one else gets asked about it.

Why it works

The reviewer never leaves the page to answer.

Every number they'd otherwise hunt down is already on the page, scoped to the one account they own. The answer comes back in the exact shape your model expects - not a paragraph someone has to re-interpret, not a spreadsheet cell buried three tabs deep.

1 afternoon

From master sheet to model-ready estimates


Live validation

Catch the fat finger before it ships.

An agent reads each answer against the context on the page and flags what doesn't add up - in the moment, while the reviewer still remembers why they typed it. No more finding errors three weeks later when the model produces nonsense.

Corner Stop Convenience

Single-location retailer · ~$5M revenue · $4K spend with us last year

Reviewer entry

$2,000,000/yr

Worth a second look

That's 500× their spend with us last year and roughly 40% of their entire annual revenue. Confirm if you meant it, or update the number.

Confirm, I meant itEdit estimate

What the agent checked

  • The entry is 500× the account's spend with us last year.
  • The entry is ~40% of the account's public annual revenue.
  • Peer accounts in the same segment average under $25K TAM.

The reviewer stays in control. The agent never overrides - it just surfaces the check while the context is still fresh.


How it works

Four moves, end to end.

Every expert-review flow follows the same arc - from the raw decision to a structured artifact your models and agents can consume directly.

Assemble the context the reviewer needs

Pull the internal record, the firmographics, and the third-party signals onto the question itself. The reviewer never tabs away to answer - every number they'd look up is already on the page.

ExampleAn AE reviewing QuikMart sees last year's spend, revenue band, DC count, and third-party logistics estimate right next to the ask - no CRM spelunking.

Route one decision to one expert

The AE sees only their accounts. The budget owner sees only their line items. No blast, no shared sheet, no cross-referencing someone else's filter - just the slice each person actually owns.

ExampleJordan gets his 22 accounts. Rina gets hers. Neither has to figure out which rows are theirs.

Validate at entry, not at the end

An agent reads each answer against the context on the page and flags the ones that don't add up - in the moment, while the reviewer still remembers why they typed it. Fat fingers and misreads die at the source.

ExampleEnter $2M TAM on an account that spent $4K last year? The form asks you to confirm before moving on.

Return a structured artifact your models can read

Every answer comes back typed, comparable, and queryable. You feed it straight into the TAM model, the scoring system, or the agent that drafts the next round - no parsing, no cleanup, no re-asking.

Example800 TAM-by-product estimates with confidence scores land in one table, ready to model the same afternoon.


Who it's for

Decisions only the people with the context can make.

Revenue & sales ops

TAM models, account scoring, territory enrichment - without asking AEs to fight a 40-tab spreadsheet or booking a week of walkthrough calls.

Finance, FP&A & strategy

Zero-based budget reviews, vendor renewal calls, SKU-level spend decisions - each line item routed to the owner with the context already on the page.

AI & ops teams needing ground truth

Your agent drafts; your experts confirm, correct, or veto with structured reasons. Human-in-the-loop review that actually gets completed.


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