Pricekeel
Keep your pricing on an even keel.

Stop giving away price that wins you nothing.

For VP Pricing teams at $10–100M ARR usage-based B2B SaaS

Pricekeel reads your closed deals and shows where discounting buys wins, where it just gives money away, and the discount that earns the most on the next deal. No warehouse, no integration. One CSV.

What you see when you click

The diagnostic, on a live sample of 2,000 closed deals

Below is the actual structure of the page on /sample — KPIs, win-rate curve with the calculated win point, and the deal list to investigate. Your numbers come from your own CSV.

pricekeel.com/diagnostic
Booked value
$67.1M
1,215 won deals
Price realization
81.4%
avg discount 13.8%
Pricing upside
$11.2M
16.7% of booked
Win rate
60.8%
2,000 opportunities
Win rate by discount band
Win point at the 15% band — bigger discounts don’t buy more wins
0%
5%
10%
win point
15%
20%
25%
30%+
Top deals to investigate
Acme CorpEnterprise32%$248K
GlobexEnterprise28%$192K
InitechMid-Market26%$164K
SoylentMid-Market25%$141K
See the full diagnostic on the sample data →

Live numbers from the bundled sample. Your numbers come from your CSV.

What you walk away with

The diagnostic is four artifacts, not a dashboard you have to interpret

Each one is built so a Head of Pricing can forward it to the CFO unchanged. No screenshots-of-screenshots, no copy-paste cleanup.

01
Executive summary

A one-page written narrative your CFO can read in two minutes — booked value, price realization, win point, the headline upside, the top three deals to investigate. Plain English, no jargon.

02
Win-curve report

Win rate by discount band with the calculated reference point (the band where bigger discounts stop buying wins). The chart your Head of Pricing forwards to the CRO.

03
Per-deal guidance

For each open opportunity you send: recommended discount, expected-value lift, win-probability change, and the top three factors the model thinks drive the deal. Decision support for the deal-desk call.

04
Defended-vs-investigate list

Won deals split into 'discount earned the win' (defend in sales review) vs 'discount worth a retrospective look' (route to deal-desk debrief). Sales-friendly framing of the same data.

Back-of-envelope estimator

What might your pricing upside look like?

Move the sliders. The math is the same logic our diagnostic uses, applied to round numbers. Your real number comes from your CSV.

Booked ACV (annual)$60,000,000
$10M$30M$60M$120M$300M
Average discount given14.0%
0%10%20%30%
Estimated pricing upside (annual)
$3,240,000
5.4% of booked value, past the 5% reference. To pursue, not a refund — your real number plus the deal-level list comes from the diagnostic on your CSV.

What Pricekeel surfaced on the bundled sample

2,000 closed deals → three numbers a CFO can act on

$67.1M
Booked value (won)
1,215 won / 785 lost · win rate 60.8%
$11.2M
Pricing upside to pursue
16.7% of booked, past the win point
81.4%
Price realization
avg discount 13.8%

Live figures from the bundled sample. Your numbers come from your CSV.

What it does

Find the leakage

Price realization, win rate by discount band, and three views of discount leakage from a plain description to the strongest claim worth acting on.

Find the win point

The discount level where win rate stops improving. Everything given beyond it is a list to investigate, not a refund.

Guide the next discount

A win-probability model recommends the discount that maximizes expected value on a given deal, with a plain-language why.

Defend the decision

Every Pricekeel recommendation is logged with the math behind it. The Copilot answers CFO questions with citations to the source signal — no LLM-invented numbers.

Built on named methodology, not vibes

Defensible to finance because the math has authors

Every Pricekeel signal — leakage lenses, win point, packaging signal, trade-or-give, decision log — is grounded in a published framework your Head of Pricing can cite when defending a discount approval.

Built for the stack you already run

Salesforce
via CSV today
HubSpot
via CSV today
Stripe
via CSV today
Zuora
via CSV today
Snowflake
Phase 3 connector
Google Sheets
via CSV today

A CSV export covers every CRM and billing system today; native connectors land with the Phase 3 margin layer.

Final phase · coming soon

Margin Enhancement

Next, Pricekeel connects to your contracts and CRM to find margin across the whole book: special pricing agreements, fixed discounts, renewal uplift left on the table, and price-floor breaches. From a retrospective diagnostic to a live margin engine.

📈
ABAdhithya Bhaskar, founder
Built by
Adhithya Bhaskar
Pricing strategist building Pricekeel

M.S. Marketing Analytics with a Pricing specialization from Simon Business School (University of Rochester). Previously led a $626M margin-recovery analysis across 1,200+ opportunities. Building Pricekeel to make that kind of analysis routine for any pricing team — and defensible to finance.

Simon Business SchoolM.S. Marketing Analytics · Pricing$626M margin-recovery1,200+ opportunities analyzedPricing + AIAnthropic Claude certified
Connect on LinkedIn →
ABAdhithya Bhaskar, founder
Design partner program — open
Want me to run the diagnostic on your CSV personally?

I’m taking three design partners this quarter. Free diagnostic under NDA, 30-minute readout with me, and if the upside is real we keep going on a quarterly cadence. If it’s not, you keep the analysis and we part as friends.

Row-level deal data is processed in memory and deleted

The cloud LLM sees only aggregate figures, column headers, document chunks you upload, and your questions — never row-level data. Under zero-retention provider terms.

See it on your own deals

Export a CSV of your closed opportunities and get your own diagnostic. Your data is processed to produce the analysis and is not stored. If you need an NDA first, we will sign one.