← BUILD LOGPRICING + PACKAGING
BUILD LOG · PRICING AND PACKAGING

A price increase usually costs you win rate and speed. This one bought all three.

Conventional wisdom says raise price and you win fewer deals, slower. We raised price, won more, and closed faster, because the risk was never the price. It was the model.

+30%
average deal size, after value-based repackaging
40% to <15%
active users on free plans, cut by the PLG motion
1st
pricing and packaging change in company history: off seats, onto value and usage
2 moves
for the base: strategic renewals first, migration only after the motion was proven
THE STORY, FROM THE SEAT

Here is one I actually shipped. What happened, what I did, and the stack if you want to run it yourself.

This one started with the market moving under us. The golden era of license-based SaaS pricing was ending, and usage-based models were becoming the expansion engine across the industry. Ours was still a pure per-seat license. That sounds stable until you see the exposure: when revenue is a function of seat count, every contraction at a customer is a contraction in your revenue, with no other lever. A team shrinks, your ARR shrinks with it. You are not pricing the value, you are renting chairs. And underneath it, nearly half our active users sat on the free plan, using the product every week, never paying.

And this was not a tune-up. It was the first pricing and packaging change since the company started: off good, better, best, onto a multi-product, usage-based model. The upside was obvious. So was the downside, which is why half the work was mitigation: sizing the migration impact on customers, and running a tight loop between testing, building, and deploying before anything touched the base.

There were three ways to go: raise the per-seat price and hope, chase the free base with a harder paywall, or repackage around value and redraw the free-to-paid line. The paywall is the lazy read. It punishes the symptom. We fixed the model underneath first, and the conversion followed.

The existing base got handled deliberately, in two moves. The new packaging became leverage for strategic renewals and expansion conversations first. The rest of the base migrated after the new-customer motion was optimized, so the hardest conversations happened with proof in hand.

The Sales Operator
Keep Building,
Heath
FOUNDER, THE SALES OPERATOR
01 / HOW WE APPROACHED THE PROBLEM

Ran the loop.

License pricing was ending across SaaS and usage models were becoming the expansion engine, while our commercial model still rented chairs: pure per-seat, with nearly half the active base using the product weekly and never paying. The lazy read was to chase free users into a paywall, and that does not hold. We fixed the model underneath first, the first repricing in company history, and the conversion followed.

  1. 1
    Reprice to value, not seats

    I repriced and repackaged so revenue was tied to the value delivered, not the number of seats occupied. That de-risked the base: a customer could reorganize without the contract falling apart, and we could grow an account on something other than headcount.

  2. 2
    Redesign the free-to-paid boundary

    I redesigned the line so real usage had a reason to convert, instead of living free forever.

  3. 3
    Fix the model, not just the paywall

    The lazy read is to chase free users into a wall. That does not hold. We fixed the model underneath first, then the conversion followed.

02 / WHAT IT TOOK CROSS-FUNCTIONALLY

Nobody ships this alone. Here is who had to move.

The first repricing in company history is not a sales project. The packaging, the migration math, the billing mechanics, and the base conversations all lived with other functions, and the loop between testing, building, and deploying ran across all of them.

PRODUCT
The packaging itself: the tiers, the usage gates, and the free-to-paid line.

Going from good, better, best to a multi-product, usage-based model is a product decision as much as a pricing one. The boundary got designed and tested in the same loop as the build.

FINANCE
The downside math: migration impact, revenue exposure, what the base could absorb.

The upside was obvious; the job was mitigating the downside. Migration scenarios got sized and measured before anything shipped, so the risk was a number, not a fear.

REVOPS AND BILLING
The price book and the migration mechanics.

New packaging is a website update until the quote, the contract, and the renewal all carry it. The plumbing moved in the same testing loop as the model.

CS
The existing-base conversations.

The new packaging ran as leverage in strategic renewals and expansion conversations first, and the rest of the base migrated after the new-customer motion was optimized.

The frictions were the predictable ones, and all of them showed up: reps feared the price before buyers did, grandfathering was a fight, the forecast wobbled mid-migration, and comp needed a look once deal sizes moved. Each got handled as part of the launch, not after it.

03 / HOW WE TURNED IT INTO A SALES MOTION · THE PEOPLE PART

New packaging raised the price. Coaching the team to sell value is what protected the win rate.

A price increase is only safe if the reps can defend it. The leadership half was arming every rep to hold the number with a value story instead of folding to a discount the moment a buyer flinched.

THE FRAMEWORK BEHIND IT
VALUE Business Case, plus LEARN on price

Validate what they get today, articulate what good looks like, link the package to their outcome. Then when the price objection comes, LEARN it: listen, empathize, anchor to value, respond, next step, instead of reaching for the discount.

  1. 01
    Trained the value story before the price conversation

    Reps led with what the tier unlocks, so price landed as a consequence of value, not a number in a vacuum.

  2. 02
    Made discount the last lever, not the first

    Coached the objection turn so a flinch got a value reframe, not an immediate cut to the price.

  3. 03
    Rehearsed the price pushback

    Reps practiced the exact objection they would get on the new packaging before they got it live.

  4. 04
    Inspected the launch weekly

    Free-plan active users, free-to-paid conversion, downgrades and churn, average lift in checkout, and 90-day self-serve retention. The reprice was watched like a launch, not assumed like a policy.

Handle the price objection
04 / WHAT I LEARNED

Not proof. Just what the build taught me.

  1. 01
    The risk was never the price

    Conventional wisdom says raise price and win fewer deals, slower. We raised price, won more, and closed faster, because the exposure lived in the model. The market had already moved: license pricing was ending and usage was becoming the expansion engine. The reprice caught the model up to how customers already bought.

  2. 02
    Fix the model before you chase free users

    Chasing free users into a paywall does not hold. The model got fixed underneath first, then the conversion followed.

  3. 03
    A price increase is only safe if reps can defend it

    Reps were armed to hold the number with a value story instead of folding to a discount the moment a buyer flinched.

  4. 04
    Per-seat revenue is a bet on their headcount

    When revenue moves one-for-one with seat counts, a customer reorg becomes your contraction. Package around the value and the base survives the reorg.

05 / THE WORKFLOW

The runnable version. Copy it into your stack.

AmplitudeSalesforceSnowflakeLookerDeepline
[ SALES OPERATOR ]

Reprice to De-risk

Heath Barnett · Sales Operator
G / Groundthe problem that started all of it, with the receipt and the cost
Risk

You were renting chairs, not pricing value.

Pure per-seat pricing meant every contraction at a customer was a contraction in your revenue, with no other lever.

Leak

Half the active base never paid.

Nearly half of active users sat on the free plan, using it weekly, living free forever.

A / Assignthe build, block by block, and who owns each one. Open a step to see it run.
HUMAN + AI, IN THE LOOP
Pricing is the operator's call; AI reads the base, models the impact, and matches the tier to the buyer.
Read the base
Amplitude01
Pull free-plan active users
Details
Find the weekly-active users sitting on free who never had a reason to convert.
AI
Looker02
Analyze seats vs value across the base
Details
Where revenue tracked chairs instead of the value delivered.
AI
Redesign the model
Salesforce03
Reprice to value, not seats
Details
Tie revenue to value so a customer can reorganize without the contract falling apart.
Human
Deepline04
Match the tier to who actually buys
Details
ICP to packaging, so the tier fits the buyer, not a headcount band.
AI
Convert
Amplitude05
Redesign the free-to-paid line
Details
Give real usage a reason to convert instead of living free forever.
Human
Snowflake06
Model the usage-and-revenue impact
Details
The data behind the repackage, tested before it shipped.
AI
N / Normalizethe motion that made it stick
A workflow without a motion is dead. This is how it became the way the team works.
1

Value story before the price conversation

Reps led with what the tier unlocks, so price landed as a consequence of value, not a number in a vacuum.

2

Discount became the last lever

The objection turn was coached so a flinch got a value reframe, not an immediate cut to the price.

3

Rehearsed the pushback before it went live

Reps practiced the exact objection the new packaging would draw before they got it on a real call.

4

Watched the launch weekly

Free-plan actives, conversion, downgrades and churn, checkout lift, and 90-day self-serve retention. The model earned trust with numbers, not confidence.

T / Tie backthe result it drove, and how they know
RESULT · 01

+30% average deal size

after the repackage.

RESULT · 02

40%+ to <15% unmonetized

active users the PLG motion converted.

RESULT · 03

Expansion up

land-and-expand off value, not headcount.

OUTPUT · 01

Value-based packaging

Revenue tied to value, not seat count.

OUTPUT · 02

A new free-to-paid boundary

Real usage has a reason to convert.

OUTPUT · 03

Tier-to-ICP map

The package matched to who buys.

The Sales Operator
06 / HOW YOU DO IT TOO

Here is what I built. Here is how you build it.

The whole thing installs as one plugin. Or grab the skills a la carte. Everything on this page, runnable, on what you paste today.

EVAL PASS · 4/4
THE PLAYBOOK · ONE INSTALL
Reprice to de-risk your revenue

4 skills chained into one runnable play. Installs as a single plugin, no copy-pasting each skill. It runs on what you paste; connect your stack to go live.

SEE THE FULL PLAYBOOK →
SEE IT RUN IN CLAUDE
EXAMPLE CHATElena, Founder, running the loop in one sitting
E
We charge per seat and our revenue swings every time a customer does layoffs. I want to reprice so it is not tied to their headcount. Where does the model break?
S
Step 1· Pricing Packaging
Per seat bets your revenue on their headcount, so a layoff hits you too. I mapped where the model breaks, proposed a value metric that grows with the outcome you deliver, drafted clean tiers around it, and sketched a migration path, all as a hypothesis to test with buyers before anything ships.
Receipt
Proposed value metric: workflows automated; draft tiers and a 12-month grandfather path; modeling suggests contraction risk could drop from 18% to 7% of ARR, pending buyer validation.
E
Before we commit, what do buyers actually think is fair to pay for?
S
Step 2· Customer Research
Before that metric ships, I built interviews to test it with real buyers and turn what they say into the final pricing input. The point is to hear what they believe they are buying, not to lead them to our answer.
Receipt
Guide plus 10 customers interviewed; they anchor on outcomes, not seats, confirming the proposed metric before it gets finalized.
E
Help me frame the new packaging so it lands on value, not as a price hike.
S
Step 3· Positioning Brief
I wrote the positioning for the new packaging around the value it captures, with the pillars, the proof, and reframes for the pushback you will get. The story is what you deliver, not what you charge per head.
Receipt
Message house built; lead pillar is cost per automated workflow, with 3 proof points.
E
Now give me something to justify the change to the accounts it hits hardest.
S
Step 4· Business Case
I built a one-page case in each account's own numbers: the cost of the problem, the value model, and the risk of doing nothing. It is aimed at the handful of accounts where the change moves the most money.
Receipt
Case for the top 12 affected accounts; the median lands as a 14% increase justified by a sub-6-month payback.
THE OTHER HALF · LEAD THE TEAM
Handle the price objection
I write up one of these a week. Free, receipts only.