A practical playbook · Revenue Command Centre

Will we hit the number, and if not, what do we do about it?

A revenue plan is a hypothesis. It's set in January from last year's numbers plus some optimism, and at some point in the year it stops being true. This playbook shows how to see that moment early, find the lever that broke, and act while there's still time.

Book a revenue systems review

The problem

Teams see the missed number, not the reason behind it

Nobody acts on the early signs. The plan itself doesn't drift. The assumptions behind it do. Targets are modelled on last year's averages for deal size, win rate and cycle length, and when those averages move, the results follow, often within weeks. Those shifts are the red flags, and acting on them early is the whole point. Most teams only find out at the end of the quarter. By then the pipeline that could have closed the gap needed to exist months earlier, and the forecast goes back to the board with a new number and no plan behind it.

The numbers arrive too late to decide on. In most organisations, the executive team holds the full picture and marketing sees revenue at the all-hands. The weekly meeting starts with everyone submitting their data at the last minute, so the time goes on reconciling versions instead of deciding what to do next.

Activity gets measured instead of quality. When opening deals is a KPI, teams open deals, and not always the right ones. The pipeline looks busy while the win rate falls.

Results never feed back into the work. Wins and losses don't reshape the ICP, the account tiers or the lead scoring. So marketing keeps targeting the same accounts on the same rules, and sales keeps chasing the same kind of deal.

Who it's for

One view, used in two rooms

The executive team uses it in board prep. Sales and marketing use it in the weekly team session. Access follows role, so each person sees what they need to act on.

RoleWhy it matters
Executive teamDecide early whether to hold the target or reset it, with evidence instead of an end-of-quarter revision.
Sales leadersSee which deals and tiers deserve rep time this quarter, and which to stop opening.
MarketingTie campaigns, scoring and budget to what actually closes, not to what generates activity.
RevOpsKeep one set of definitions clean, so every team trusts the same numbers.

In practice

What happens when activity is the KPI

In one audit I ran, the data showed activity concentrated on the wrong tier. When I spoke to a few sales leads, it emerged that the half-year review had told the sales team two things: they hadn't closed enough to hit target, and they hadn't generated enough activity. That led to a spike in lower-tier opportunities being opened, because those accounts were far more available. When I looked into it, nobody was gaming anything. Opening deals was a KPI, and Tier 3 accounts were the quickest way to show activity. The team was responding rationally to what it was measured on.

Working with the team, we then found the same pattern with SDRs, in demos booked against trials actually started, because the same message about activity had reached them too.

The demo shows the same shape. Tier 3's share of new opportunities climbs every quarter, from 31% in Q1 to 58% in Q3. Over the year it takes 49% of rep time and returns 13% of new ARR won. Once that's on the screen, the conversation changes. It's no longer about whether the team is busy. It's about whether that time will ever get us to the number.

Where we win, where time goes · YTD

Rep timeNew ARR won
Tier 1
20%
54%
Tier 2
31%
33%
Tier 3
49%
13%

Tier 3 takes 49% of rep time and returns 13% of new ARR won.

Tier 3 share of new opportunities

31%Q1
44%Q2
58%Q3 to date

Tier 3's share of new opportunities has risen every quarter and spiked in Q3. It's taking the highest share of rep time without bringing in the revenue to match.

From the demo, Accounts view. Sample data.

Prerequisites

What you need first

  1. 01

    A CRM like HubSpot, with reliable company, contact, deal, stage and outcome data, enriched with fresh company and contact details, and a deal type on every opportunity.

  2. 02

    Call intelligence like Gong, with recordings linked to the right deals.

  3. 03

    Defined targets and forecasts, plus marketing KPIs, channel costs and account tiers.

  4. 04

    A consistent record of why deals are won and lost.

  5. 05

    One person who owns the forecast.

If deal types or close dates are unreliable, fix those first. Every rate downstream inherits the error.

How it works

One shared data layer, four parts on top

Everything reads from one data layer. The parts are built in this order, and each one has a clear test for when it's done.

System diagram

How the command centre fits together

  • Trigger
  • Data collection
  • Agent
  • Dashboard

Dashboard

Weekly view for every team

Dashboard

Weekly view for every team

  1. 01

    Sources

    CRM, call intelligence, marketing data

  2. 02

    Data agents

    Clean, link, check. 30-day engagement rule

  3. 03

    Shared data layer

    One repository, like Notion

  4. 04

    Metrics agent

    90-day rates, lever impact

  5. 05

    ICP agent

    Rescores the tiers

  6. 06

    Insights agent

    What drives it, what to do

Closed deals flow back, and the ICP agent rescores the tiers

Claude runs the analysis and the agents. Codex builds and publishes the dashboard.

Data agents. Bring CRM, call intelligence and marketing data into a data repository like Notion, clean it and link every record to the right account and deal. Engagement only counts when it's continuous: a one-off download doesn't, and a gap of more than 30 days restarts the clock. Done when closed-won matches finance.

Metrics agent. Compare targets with actuals on trailing 90-day rates and price each lever as the forecast with that lever back at plan. As history builds, add seasonality. Done when the lever impacts add up to the gap.

ICP agent. Rescore the account tiers from every win, loss and piece of feedback. Done when you can say where rep time is wasted.

Insights agent and dashboard. Explain what's driving performance, recommend what to do next, and publish it weekly. Done when the weekly meeting opens on the dashboard.

Design choices

Why it's built this way

One definition, held in one place. Guardrails, tone of voice, the ICP, metric definitions and the forecasting model all live as documented pages in one repository. They're controlled centrally and locked, but every team can read them. Each page tells an agent what it governs, so the numbers and rules come out the same whichever team pulls them.

People can still reach different conclusions from the same data. That's the debate a weekly meeting should have, instead of arguing about whose numbers are right.

Built like microservices. You don't need the whole system on day one. Start with the core: your sources, the shared data layer and a simple always-on view. Then add services as the need appears, such as channel views, account scoring or lead alerts in Slack. Each one plugs into the same data and guardrails, so you grow the system instead of rebuilding it.

The first step is always a diagnosis: where the gaps are, and what will move the number for this organisation right now. In a smaller company that core can be in place quickly. The content engine playbook is one of the services that plugs in later.

Build order

Start small, then add services

  • Trigger
  • Data collection
  • Agent
  • Dashboard
  • Added later
  1. Start here 01

    Sources

    CRM, call intelligence, marketing data

  2. 02

    Shared data layer

    One set of definitions and guardrails

    03

    Core view

    The revenue plan against actuals, always on

  3. Later 04

    Add services

    Channel views, account scoring, lead alerts, content engine

A working sample

See it working

The demo is modelled on real data, with names replaced and company anonymised. It has four views, Overview, Marketing, Accounts and Detail, and shows the plan, the drift, and where time goes against where revenue comes from.

FAQ

Questions

Why 90 days?

It's one quarter of closes. That's long enough that a few deals don't swing the rates, and short enough to catch drift while there's still time to act. The view can switch to 30 days or year to date, but the lever impacts always use 90.

What if our CRM is messy?

Most are. The build starts with the cleanup, and the weekly checks block a forecast built on bad data. It also flags any deal whose close date has moved twice.

How is this different from a BI dashboard?

A BI dashboard reports what happened. This one recomputes what's now required, prices each lever, dates the window to act and says what to do next.

Does it work below £10M ARR?

Yes. With fewer deals, one month can swing a win rate, so it uses a longer window and shows ranges instead of single numbers.

Put the system to work

A plan is a hypothesis. Test yours every week.

Let's look at your plan, your CRM and your forecast meeting, then decide where to start.

Book a revenue systems review