DySys
Book Intro
PressureTest
Free · No code needed

What do you believe about your revenue engine? Pressure-test it.

Start with pipeline and forecast beliefs. Demand, retention and expansion are next.

Every leadership team runs on beliefs about why the number moves. Pick one you’ve heard in the room. Each comes with a free evidence plan: what would prove it, what would disprove it, and what to rule out before anyone gets blamed.

Pick a belief Write your own

Have an invitation code? Launch an agent. Prefer to talk first? Book Intro.

Evidence plan · sample

“We just need more pipeline.”

MeasureCoverage against target by period, and conversion on the pipeline you already have
Proves itCoverage short of your own historical need, with healthy conversion
Disproves itEnough coverage, but poor conversion or heavy stall
Rule outConversion, not volume, is the gap. Or the pipeline is inflated.

One of thirteen plans below.

01
How it works

A belief is a claim you haven’t tested yet.

The plan is free and needs no sign-up. Testing it on your own CRM data starts with a short conversation, so the run is set up for your stages and your rules.

  1. 01

    Pick a belief

    Thirteen beliefs that come up in CRO, CEO and board conversations, grouped by who usually says them.

    Free
  2. 02

    Read the evidence plan

    What to measure, what would prove it, what would disprove it, and the other explanations to rule out.

    Free
  3. 03

    Test it on your data

    Ask below. If it’s a fit, a 20-minute call leads to an invitation code to run it in your own words and on your own CRM export.

    After a short call
02
The beliefs

Which one have you heard this quarter?

Open any plan. The bar shows how much of the answer a standard CRM opportunity export can give you on its own.

Not on the list? Write your own

CRO and sales leaders

“We are only ever talking to one person.”

Single-threaded

Deals rest on one contact and rarely reach the economic buyer, so they die when that person stalls.

A CRM export can settle this

Read the evidence plan

What to measure

  • Contacts per open deal by stage band
  • Economic Buyer recorded on mid and late deals
  • Win rate by contact count

What would prove it

  • Many mid and late deals with one contact
  • Win rate rising sharply with contact count

What would disprove it

  • Late deals mostly multi-threaded
  • No win-rate difference by contact count

Rule these out first

  • Contacts not added to opportunities in the CRM
  • Small transactional deals where one buyer decides

Data it takes

  • Contact count or contacts file
  • Economic Buyer field
  • Closed deals

Where to start

Count contacts on your mid- and late-stage deals, then compare win rates by how many people were involved.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CRO and sales leaders

“Our reps are getting ghosted.”

Getting ghosted

We open opportunities and then spend weeks chasing prospects who stop answering. The symptom is obvious; the cause usually sits upstream, in a deal that was never properly qualified.

A CRM export settles part of this

Read the evidence plan

What to measure

  • Open deals where the rep keeps reaching out and the buyer has not replied in 21 days or more
  • The stage at which buyers go quiet
  • Contact seniority and count on ghosted deals against won deals
  • ICP fit (segment, size, lead source) of ghosted deals against won deals
  • Whether the first call confirmed pain, fit, the buying process and an agreed next step

What would prove it

  • Ghosting concentrates in deals that opened with one junior contact, outside the ICP, or with no confirmed pain
  • It clusters by rep, which points to qualification habits rather than buyers

What would disprove it

  • Ghosted deals start out like won deals: same ICP fit, same seniority, same qualification evidence
  • Buyers go quiet late, after a full evaluation, which points to value or competition instead

Rule these out first

  • Buyer replies happen outside the CRM (phone, text, LinkedIn)
  • Opportunities opened at first touch, so the ghost was a lead that never engaged
  • Buyer-side events: budget freeze, reorganization, the champion left
  • Low-intent inbound leads

Data it takes

  • Activity log with direction (rep outreach versus buyer reply)
  • Contacts on opportunities, with title
  • Created date and stage history
  • Segment, size and lead source
  • First-call recordings or notes

Where to start

Find open deals where the rep keeps reaching out and nothing comes back. Then compare how those deals started with how your won deals started: who the contact was, whether they fit your ideal customer, and whether pain was confirmed on the first call.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CRO and sales leaders

“Our reps are too optimistic. The forecast is inflated.”

Happy ears

Probabilities and forecast labels run ahead of what the deals show.

A CRM export can settle this

Read the evidence plan

What to measure

  • Probability above the stage norm
  • Commit or Best Case labels on early-stage deals
  • Close dates the won-deal pace cannot reach

What would prove it

  • Many deals priced above stage, concentrated in Commit
  • Wrong-quarter deals inside the commit

What would disprove it

  • Probabilities inside the stage norm
  • Labels consistent with stage

Rule these out first

  • Stage definitions out of date
  • Manager overrides, not rep optimism

Data it takes

  • Probability and forecast category
  • Stage
  • Stage history or stated durations

Where to start

Compare each deal's probability and forecast label with its stage, and check whether the close date is reachable at your won-deal pace.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CRO and sales leaders

“We just need more pipeline.”

Not enough pipeline

Coverage is the constraint; more pipeline will fix the number.

A CRM export settles part of this

Read the evidence plan

What to measure

  • Coverage against target by period
  • Conversion and stall rates on the pipeline that exists

What would prove it

  • Coverage short of the team's own historical need, with healthy conversion

What would disprove it

  • Adequate coverage, poor conversion or heavy stall

Rule these out first

  • Conversion, not volume, is the gap
  • Pipeline is inflated (Happy ears)

Data it takes

  • Target or quota by period
  • Open pipeline by close period
  • Closed history

Where to start

Start with the target. Coverage only means something measured against the number, and against how much of your pipeline you usually convert.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CRO and sales leaders

“Deals stall in early stage (Qualify/Discovery).”

Early-stage stall

After the first call the deal waits. The rep sets no process, does not ask for the second meeting, or lets a deferral stand, so deeper discovery and qualification do not happen.

A CRM export settles part of this

Read the evidence plan

What to measure

  • Days from the first meeting to the second meeting on the calendar (target 14 days)
  • Share of first calls that end with a second meeting booked
  • Early-stage deals with no activity for 30 days or more (the trigger)
  • Whether the rep sets an upfront contract on the first call: agenda, process, next steps
  • How the rep handles a deferral (bring others in, check calendars) and how they chase

What would prove it

  • Many early-stage deals with no second meeting calendared inside the window, concentrated by rep or stage
  • Won deals show the second meeting booked early; stalled ones do not

What would disprove it

  • Second meetings booked within the target on most deals
  • The stall sits at later stages, not here

Rule these out first

  • The second meeting happened but is not logged (calendar not synced to the CRM)
  • Low-intent or unqualified leads
  • Buyer-side delay: bringing others in, calendars
  • The opportunity was created at first touch, not after a real first call

Data it takes

  • Meeting dates in the activity log (first and second meeting)
  • Date entered current stage
  • Stage history
  • Call recordings or transcripts

Where to start

Find early-stage deals with no activity for 30 days. Then listen to ten first calls: did the rep set the process up front and get the second meeting on the calendar?

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CRO and sales leaders

“We propose too early. (Or: too late.)”

Proposal timing

Proposals go out at the wrong point in the deal, which hurts conversion.

A CRM export can settle this

Read the evidence plan

What to measure

  • Days from created to proposal stage against the won-deal median
  • Contacts engaged before proposal
  • Conversion from proposal by timing bucket

What would prove it

  • Early-bucket proposals convert well below on-pace ones, often with one contact engaged

What would disprove it

  • Conversion flat across timing buckets

Rule these out first

  • Buyer-requested early pricing in RFP-driven deals
  • Proposal stage used loosely in the CRM
  • Small deals that legitimately skip steps

Data it takes

  • Stage history with the proposal stage mapped
  • Contacts on opportunities
  • Closed won and lost deals

Where to start

Compare time to proposal on won and lost deals.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CEO

“A few reps carry the whole team.”

Hero reps

Bookings depend on a small number of reps, so the number is fragile.

A CRM export can settle this

Read the evidence plan

What to measure

  • Share of won value by rep
  • Spread of win rates by rep with n shown

What would prove it

  • Top quartile of reps holds most won value

What would disprove it

  • Bookings spread in line with headcount

Rule these out first

  • Account assignment, not skill
  • Tenure mix

Data it takes

  • Owner on closed deals
  • Amount
  • Account or territory

Where to start

Split won value by rep, and show how many deals sit behind each rep's win rate.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
CEO

“Deals are taking longer than they used to.”

Cycles getting longer

Sales cycles are stretching, which pushes revenue out.

A CRM export can settle this

Read the evidence plan

What to measure

  • Median won cycle by quarter of close, by size band

What would prove it

  • Median won cycle rising across cohorts within a size band

What would disprove it

  • Flat cycles once size is held constant

Rule these out first

  • Larger deal mix
  • More stakeholders in the buying group

Data it takes

  • Created date and actual close date
  • Amount

Where to start

Compare the median won cycle for the last two halves, inside one deal-size band.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
Board and investors

“We cannot trust anything in the CRM.”

CRM is garbage

The data is too poor to manage from, so every number in the room is suspect.

A CRM export can settle this

Read the evidence plan

What to measure

  • Field completeness on key fields
  • Template or placeholder qualification fields
  • Past close dates on open deals

What would prove it

  • Low completeness on fields the forecast depends on
  • Template MEDDPICC across the pipeline

What would disprove it

  • Key fields filled and consistent

Rule these out first

  • Data lives in another system
  • Export pulled the wrong fields

Data it takes

  • Any CRM opportunity export

Where to start

Check how complete the fields your forecast depends on are, before you trust any other read.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
Board and investors

“The quarter rides on two or three big deals.”

Whale dependence

Forecast risk is concentrated in a few large late-stage deals.

A CRM export can settle this

Read the evidence plan

What to measure

  • Share of late-stage value in the top three deals

What would prove it

  • Top three hold more than half of late-stage value

What would disprove it

  • Value spread across many late deals

Rule these out first

  • A normal shape for an enterprise motion; risk is in the deals, not the count

Data it takes

  • Amount and stage

Where to start

Add up how much of your late-stage value sits in the top three deals.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
Board and investors

“Sales leadership cannot call the number.”

Cannot call the number

The forecast misses often enough that the board cannot plan on it.

A CRM export settles part of this

Read the evidence plan

What to measure

  • Forecast at week 1 against actual, by quarter
  • Deals in commit that slipped
  • Wrong-quarter value in today's commit

What would prove it

  • Repeated misses beyond a tolerance, driven by slipped commit deals

What would disprove it

  • Misses inside tolerance, or driven by one event

Rule these out first

  • Plan set too high, not a forecasting failure
  • Market shock in the period

Data it takes

  • Forecast snapshots by week
  • Closed deals by quarter
  • Stage history

Where to start

Pull the last four quarters of week-one forecasts and compare them with what actually closed.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
Board and investors

“Everything closes in the last two weeks.”

Hockey stick

Bookings bunch at quarter end, which drives discounting and forecast risk.

A CRM export can settle this

Read the evidence plan

What to measure

  • Share of won value in the last two weeks of the quarter
  • Open close dates bunched at quarter end

What would prove it

  • Most won value lands in the final two weeks

What would disprove it

  • Bookings spread across the quarter

Rule these out first

  • Buyer fiscal calendars
  • Annual contract timing

Data it takes

  • Actual close date
  • Close date on open deals

Where to start

Bucket won deals by week of the quarter.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief
Sales managers

“They wait for the customer to call back.”

They wait

Reps are slow to follow up, so deals and leads go cold.

A CRM export can settle this

Read the evidence plan

What to measure

  • Days since last activity on open deals
  • Time in stage against the won-deal band
  • Time from lead created to first touch

What would prove it

  • High share of deals with stale activity, concentrated by rep or stage

What would disprove it

  • Activity recent on most open deals

Rule these out first

  • Activity happens outside the CRM
  • Buyer-side pauses (budget cycles, holidays)

Data it takes

  • Last activity date or activity log
  • Date entered current stage
  • Lead file for speed to lead

Where to start

Measure days since last activity on open deals, by rep and by stage.

A sample plan. A run in your own words adapts it to your market, your stages and the data you hold.

Pressure-test this belief

Your belief isn’t here? Write it in your own words below. You’ll see the closest plan right away, and we can prepare one for your words. Any belief about the revenue function works, from lead management to closed-won. Write your belief

03
Your plan

Get an evidence plan for your own belief.

Write it the way your team says it. You’ll see the closest plan right away. Leave your work email and we’ll prepare a plan for your words, review it, and send it within one business day.

  1. 1Write or pick your beliefFree
  2. 2See the closest plan, right hereFree, nothing sent
  3. 3Get a plan for your words by emailFree, work email

Step 1 · Your belief

Pick one and add your own detail, or skip the list and write your own. Roles and company names are fine; please leave out contact details and personal numbers.

Step 2 · Where should we send your plan?

We’ll send your plan here.

Could you share a CRM opportunity export?

Please don’t send any data with this form. If it’s a fit, you upload it later, inside the tool.

Prefer to talk first? Book Intro.

How we prepare it. Claim Analyzer, our AI agent, drafts your plan from your belief, your role and your company. We don’t send it your name or email, and we review every plan before it’s sent. See the Privacy Notice.

Got it. We’re preparing your plan.

We’ll send it from duke@dysys.ai within one business day, with an offer of a 20-minute readout. If you’d rather talk first, book a 20-minute intro.

  1. 1We prepare your plan. Claim Analyzer drafts it from your words, and we review it before it’s sent.
  2. 2You get it by email within one business day, with an offer of a 20-minute readout.
  3. 3Test it on your data. If it’s a fit, an invitation code to run Claim Analyzer and Pipeline Intelligence on your own CRM export. Files and results are deleted within 120 days of a run.