Hey {{First Name}},
At the start of Q3, I reviewed a pricing model that looked great in the spreadsheet but brutal in the field. Discounts were all over the place, and expansion lagged. We rebuilt the structure around real buying behavior, and close rates rose from 21% to 33.5% while average contract value moved from $38,700 to $46,360 within two cycles.
You are not alone if price is slowing deals.
The truth is… pricing is a Go-to-Market system choice, not a spreadsheet tweak. Align it to how customers realize value, and pipeline speeds up while renewals get easier. This week, we map the hidden leaks, show working models we see in the field, and outline a 30-day reset you can run without freezing the pipeline
Estimated reading time is 3.6 minutes. Hit reply and tell us what you are seeing on your side.
On Deck:
Why Solid Products Still Miss Revenue Because Of Price
Marketing Tip of the Week – Powered by Decoded Strategies
Build a Real Revenue Engine (Leak-Proof) with Marcus Chan
Why Solid Products Still Miss Revenue Because Of Price
Great products fail to capture value when the price structure does not match how customers buy, use, and grow. Most teams inherit a model from their first 10 deals and then keep adding exceptions. That is how you end up with a price card that looks tidy but does not reflect reality.
Common root causes we see:
Price divorced from value moments
The meter runs on the wrong axis. You charge per user when the real value is tied to transactions or outcomes. Buyers sense the mismatch and drag negotiations out while they try to fix it. Customers anchor fairness to the outcome they feel, not the meter you prefer. When the unit of value is misaligned, procurement escalates, and edits stretch decisions into the next quarter.
Packaging that hides the win
High-value features are locked in tiers that customers do not need, so reps discount to open the door. I just had this happen with LinkedIn Sales Navigator (email me if you want to hear that story). That trains the market to wait for a deal, hurting your ability to expand later. Buyers should not buy three extras to reach one capability that matters. When tiers force tradeoffs, reps discount to unlock value, which trains the market and damages your expansion story for the next renewal.
Discounts that act like policy instead of precision
Reps carry an unofficial discount table. Finance complains. Customers learn to push. Margin erodes, and you still do not win faster. Guardrails should reward behaviors that lower risk and increase predictability. When discounting compensates for weak qualification, you teach buyers to stall for price, and you mask problems in positioning.
No path from first win to larger footprint
Customers cannot see how spend grows as value grows. They feel like they are stepping into a trap rather than a plan. Expansion should feel like the next logical step in a plan they agreed to at kickoff. When that path is invisible, champions avoid internal risk, and your relationship stalls at a small foothold.
If two or more of these feel close to home, the price architecture needs a reset, not another round of approvals.
Signals Your Model Is Misaligned
You do not need a data science team to see the symptoms. Watch how deals move, which exceptions recur, and where expansion slows. When your price does not match how customers realize value, your funnel turns into a friction map and sellers drag spreadsheets into every call to rebuild your offer by hand.
Below are the patterns we keep finding in audits.
When value clarity collapses mid-funnel and price objections erupt late, you have a narrative gap. Loss notes citing confusing tiers pass forty percent, and cleaner ladders win before finance sees the quote.
Top reps stitch together custom bundles twice as often as standard packages, a sign that pricing fit is off. When the field invents pricing, finance loses guardrails, analytics lose signal, and exceptions harden into policy.
Deal desk tickets spike and time stretches past two weeks. Close rates fall to 2 times lower once approvals exceed three hops. Trust erodes as leaders prize scrutiny and reps route around rules.
NRR lags as customers love the product, which signals that your meter tracks the wrong behavior. Tie price to the first value moment, and net revenue per account rises fourteen percent in two cycles.
Where AI Helps And Where It Does Not
Artificial intelligence can make your pricing smarter, but only if you ask it the right questions and feed it honest data. Think of AI as a pattern finder and scenario engine, not a magic price oracle.
Useful applications we see working right now:
Elasticity insights at the segment level
Train a simple model on deal outcomes, proposed price, discount given, time in stage, and segment. You will learn that mid-market security tech tolerates a 7.8% higher list price than your general card, while SMB fintech balks at a smaller increase. Adjust list and guardrails by segment rather than dragging the entire market up or down.Bundle detection from usage patterns
Cluster customers by the features they use together over time. You will find new natural bundles and a few zombies that no one touches. Retire those and move popular pairs into the next tier where they belong.Proposal simulation before you publish
Feed last quarter’s pipeline to a pricing simulator with your new card. See how many deals would have moved faster, which discounts would have disappeared, and where the margin would land. You do not need perfect data. You need directional confidence.
AI has limits. It will not fix poor qualification, and it cannot add value when the packaging is wrong. If your RevOps standards are loose, it will happily amplify bad data and make weak decisions look credible.
Use AI to sharpen human judgment and to stress test scenarios before you put them in front of real buyers.
Your 30-Day Pricing Reset Without A Big Bang
You do not need a new pricing engine or a 4-month task force. You need a focused sprint to clean the data, clarify the ladder, and tighten the guardrails while the field keeps selling.
Here is the action plan we run with teams:
Week 1
Data pull and harmonize
Export quotes, discounts, usage, and win-loss notes for four quarters, normalize fields, expose SKU sprawl, and agree on ASP so variance is real.
Week 2
Tier rationale document
Write one page per tier tying three outcomes to buyer jobs with examples, then pin it in enablement and CRM so sellers stop rebuilding offers.
Week 3
Guardrail change
Publish floors and a small discretion band by role, require one hop below floor, and track exceptions visibly so trust rises and approval time drops.
Week 4
Field test and retro
Pilot the ladder with ten to twenty deals, hold weekly retros across sales finance product, lock what works, retire misses, and publish changes every Friday.
The Bottom Line
If your pricing was born from the first ten deals and then patched with exceptions, you are leaving money on the table and adding drag to every motion. Treat pricing like the GTM system decision it is.
Align it to value moments, package the first win and the next step, set guardrails that reward the behavior you want, and use AI to test rather than guess.
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Marketing Tip of the Week - Powered by Decoded Strategies
Replace Case Studies With Mini Movies
Nobody reads PDFs anymore. Turn one customer success story into a short LinkedIn carousel or 60-second video that shows the before/after arc like a movie‚ problem, turning point, resolution.
Keep it visual, quote-driven, and emotional. Post it to your company page and have reps share it personally within 24 hours. Measure which post format gets the most inbound replies.
Episode #120: Build a Real Revenue Engine (Leak-Proof) with Marcus Chan
Are you “fixing pipeline” while millions leak out the bottom of your funnel?
In this episode of Bridge the Gap, we sit down with Marcus Chan, founder & CEO of Venli Consulting, creator of Revenue Engine OS, and WSJ bestselling author, to show you how to build a real revenue engine that scales on systems, not heroics.
Marcus has led a $195M org and helped 700+ companies recover nearly $1B in hidden revenue. We break down discovery that actually converts, management cadences that raise the floor, and why “more pipeline” is the wrong answer to a win-rate problem.
🔑 Key Highlights
✓ The #1 hidden leak: low win rates from weak discovery (not “pipeline”)
✓ Discovery vs. qualification: stop using BANT like a script
✓ Frameworks that scale thinking (POWERFUL, RIM) without turning reps into robots
✓ Diagnose before you prescribe: audits, scorecards, and root-cause analysis
✓ Build assets (playbooks, CRM fields, enablement) that compound
✓ What to measure (and what to ignore): win rate, cycle time, conversion, not vanity coverage
Agree? Disagree? Have Questions?
Want a quick read on where your model is leaking? Reply and we will work it with you.
Talk soon,
Adam, Dale, & Jake
Helping companies bridge the GTM Gap™.