Demand Generation Dashboard Template: 15 Metrics, Formulas, CRM Fields and the Weekly Review

A demand generation dashboard template holds 15 metrics in three layers: volume, conversion and efficiency, and every metric reconciles to one number, the quarter's pipeline target. Opportunities created anchors the volume layer. Any metric that cannot be traced to the target moves to a reference report. That test matters because DemandScience research cited by The Digital Bloom says two-thirds of marketing dashboards show success that does not translate into pipeline or revenue.

What should be on a demand generation dashboard?

A demand generation dashboard needs 15 metrics in three layers: volume, conversion and efficiency. Every one of them should reconcile to a single number, the pipeline target for the quarter. If a metric cannot be traced back to that target, it belongs in a reference report and not on the dashboard.

Most dashboards fail that test. The Digital Bloom, citing DemandScience's State of Performance Marketing Report 2026, says two-thirds of marketing dashboards show success that doesn’t translate into pipeline or revenue. The same guide reports that only 23% of marketers report confidence that they are tracking the right KPIs, a figure it attributes to Ruler Analytics.

The three layers

The anchor of the volume layer is opportunities created. ORM Technologies describes it this way: This is deal count, and it is the one number that belongs to demand generation. Lead counts sit above it as supply. Pipeline dollars sit below it as value.

How the layers reconcile to the target

Work backward from the target, then check each layer against it. An illustrative quarter:

1. SQL-to-opportunity rate: 40%. Required SQLs: 75 ÷ 0.40 = 187.5, so 188.
2. MQL-to-SQL rate: 15%. Required MQLs: 188 ÷ 0.15 = 1,253.
3. Program budget: $300,000. Cost per opportunity ceiling: $300,000 ÷ 75 = $4,000.

Now the dashboard reads as a set of checks. If MQLs hit 1,253 but opportunities stall at 50, the problem sits in the conversion layer. If opportunities hit 75 at $6,000 each, the problem sits in the efficiency layer. The reverse funnel calculator runs this arithmetic for your own rates.

Compare your conversion rates against published ranges before you lock them in. The Digital Bloom cites Forrester research putting MQL-to-SQL conversion around 13% (with 12 to 18% as the typical B2B range).

Timing matters too. ORM's COO notes that pipeline for a quarter is often built 6 to 12 months before that quarter begins. In our reading, that is why the volume layer needs a weekly read even though the efficiency layer only settles quarterly.

The 15-metric template you can copy

The table below holds all 15 metrics in four layers: input, funnel, pipeline and efficiency. Paste it into a sheet as is. The field names follow HubSpot and Salesforce defaults, so rename them to match your own CRM before you build reports.

Where the fields come from

The Lead Status property holds the sub-stages inside SQL, which is how metric 7 separates worked leads from untouched ones. HubSpot's own example workflow tasks an owner when the SQL entry date is more than 5 days ago. For stage duration, the Cumulative time in stage property records total seconds spent in a stage.

In Salesforce, the default influence model assigns 100% influence to the campaign in the Primary Campaign Source field. That is the field metric 10 groups by. Metric 14 copies the Campaign ROI Analysis formula: net gain divided by Actual Cost, shown as a percentage.

Worked example for metric 14

Before you trust that 200%, check which influence model is the default. Changing the model changes which deals count as won value, so the result changes too.

How to fill each layer from your CRM fields

Each layer of the template reads from a small set of fields. Map them once, write the mapping into the dashboard description, and the weekly review stops arguing about whose number is right.

Volume and conversion: count stage entries, not current stage

Count records by the week their entry date falls in, not by the stage they sit in today. HubSpot also stamps Date exited stage, so you can see who left a stage and when.

Run it by entry cohort so a slow October does not rewrite September.

Watch for manual edits. Default automatic updates only move the stage forward, but a person can set it back. When that happens, the legacy Became a lifecycle stage date property for the later stage is cleared, while the new calculated properties are not. Build conversion reports on the calculated Date entered fields, and check the property history when a count drops without a reason.

Speed metrics carry a plan requirement: a Professional or Enterprise subscription is needed for the latest time and cumulative time properties.

Pipeline: tie stages to deals

The default Opportunity stage means a contact or company that is associated with a deal, and Customer means a record with at least one closed deal. Pipeline value comes from the deal amount, so a contact with no associated deal adds nothing to the pipeline layer. For the sales follow-up row, the Lead Status property holds the sub-stages inside SQL. Agree its definitions in your marketing and sales SLA.

Access: who sees which number

Set the dashboard to Everyone and View only for the wider team, and keep edit rights with the owner. Sharing the dashboard is not enough on its own: dashboard access does not override report sharing, folder permissions or data permissions. Remember that Super Admin and users with Reports & dashboards permission can change any dashboard. When a figure shifts between reviews, the History tab shows the date of each change and the user who made it.

How do I calculate cost per opportunity?

Every efficiency metric on the dashboard has the same shape: campaign cost on top, a funnel count underneath. Change the denominator and you move from lead volume to pipeline. Use the same cost figure for all four so the ratios can be compared.

Salesforce's Campaign ROI Analysis Report already "calculates the return on investment (ROI) and average costs for your campaigns." It works from the Actual Cost field and the won-value formula covered in the CRM section. It can also include campaign-hierarchy statistics that roll a parent campaign and its children into one figure. That rollup lets you report cost per opportunity by program as well as by tactic.

The four cost formulas, with one worked campaign

Salesmotion defines cost per qualified meeting (CPQM) as Total Sales & Marketing Costs for a Campaign / Number of Qualified Meetings Booked. Its own example: a $50,000 campaign that books 100 qualified meetings has a CPQM of $500. The same article lists cost per lead (CPL) as the vanity version of the cost metric, with CPQM as the revenue version.

In our reading, cost per lead stays useful as a diagnostic. It tells you whether media got more expensive. Cost per opportunity tells you whether that media turned into pipeline. Keep both lines on the chart and review the gap between them each week.

Tie it to the New Customer CAC Ratio

Campaign costs leave out salaries and tools. The company-level check is the CAC ratio. Benchmarkit gives the formula as Total Sales & Marketing expenses / New Customer ARR, where ARR is annual recurring revenue. In 2024 the ratio rose 14% to a median of $2.00 of sales and marketing expense per $1.00 of new customer ARR. The fourth quartile spent $2.82 at median for the same dollar.

A worked example with our own numbers: $1.2 million of quarterly sales and marketing expense against $600,000 of new customer ARR gives a ratio of 2.0. Benchmarkit says the ratio should be read in the context of annual contract value (ACV), so compare against your ACV band before you treat any ratio as high. For payback math built on the same inputs, see CAC payback benchmarks.

Worked example: one quarter's counts, stage by stage

Here are one quarter's raw counts and spend, run through all 15 metrics. Every rate divides one stage count by the count at the stage before it, and every cost divides the quarter's $450,000 program spend by the count at that stage.

In this example quarter, cost per stage climbs from $150 per lead to $500 per MQL, $1,429 per SQL and $2,647 per opportunity.

Worked example: cost at each stage from one quarter's counts

Item	Value
Cost per lead	150 US dollars
Cost per MQL	500 US dollars
Cost per SQL	1429 US dollars
Cost per opportunity	2647 US dollars

Source: Take the Market worked example (illustrative quarter)

How each row is computed

• Cost per stage: $450,000 / 3,000 leads = $150. The same spend over 900 MQLs gives $500, over 315 marketing qualified leads that became sales qualified leads (SQLs) gives $1,429, and over 170 opportunities gives $2,647.
• Stage rates: 900 / 3,000 = 30.0%. 315 / 900 = 35.0%. 170 / 315 = 54.0%. 37 / 170 = 21.8%.
• Deal size: $1,110,000 / 37 = $30,000.
• Efficiency: $5,100,000 pipeline / $450,000 spend = $11.33 of pipeline per program dollar.
• Coverage: $5,250,000 open / $1,500,000 target = 3.5x. The pipeline coverage calculator runs the same division.

Checking the rates against published ranges

Lead to close here is 37 of 3,000 leads, about 1.2%. Forrester research (via Marqops) puts the overall lead-to-close rate below 1%, so this quarter sits slightly above that line.

MQL to SQL at 35.0% is well above the typical range cited earlier. The final stage is further out. Only 6 to 9% of opportunities reach closed-won in the HubSpot analysis that source cites, and this quarter closed 21.8%.

In our reading, a gap that wide is more often a definition problem than a performance result. Before you report it, confirm three things:

1. What creates an opportunity. Check whether an opportunity is opened at first sales meeting or only after qualification.
2. Which deals the 37 includes. Check whether expansion or renewal deals were counted alongside new business.
3. Whether the cohorts match. Check whether the closed deals came from this quarter's opportunities or from earlier quarters.

A rate that sits far outside a benchmark is a prompt to audit the field, not a figure to celebrate. Two-thirds of marketing dashboards show success that doesn’t translate into pipeline or revenue, according to DemandScience research cited by The Digital Bloom. For stage-by-stage comparisons, see our funnel conversion benchmarks and win rate benchmarks.

Make every number reconcile to the pipeline target

A dashboard earns trust when the sourced column adds up to the pipeline number in the plan. That depends on which influence model feeds each view, and on knowing what the customer relationship management (CRM) system does and does not record.

What Salesforce actually counts

In Salesforce, influence models scan active campaigns to identify members who are also assigned a contact role on an open opportunity. Four behaviors shape the totals:

• Closed-stage cutoff. Once an opportunity is closed, won or lost, influence records are no longer created. A touch logged after close never reaches the dashboard.
• Member status is ignored. Influence considers every campaign member, regardless of their member status, so a registrant who never attended still counts.
• Custom models are manual. Users enter the influence percentage by hand, and a locked custom model accepts updates via API only (application programming interface). Auto-association rules do not apply to custom models.

Our rule: one sourced total, influence beside it

We keep Primary Campaign Source as the default model and treat it as the only sourced number. Influenced pipeline sits in its own column and is never added across campaigns.

Here is a worked example with a quarterly target of $3.0M created pipeline:

The influenced column adds to $5.3M because one deal appears under several campaigns. Summing it is the error that inflates marketing's share.

Each week, run one check. The sourced total must equal the opportunity report total for the same created-date window. If it does not, find opportunities with a blank primary source before the review. For the definitions behind each column, see marketing-sourced vs. influenced, and carry the reconciled total into pipeline coverage.

How often should you review demand gen metrics?

Set the cadence by how fast each metric can move. ORM's glossary puts it plainly: leading metrics move within weeks of a program, and lagging metrics settle a quarter or more later. Reading a lagging number every Monday produces noise and no decisions. Reading a leading number once a quarter means you find a broken handoff too late to fix it.

The lag runs long. ORM notes that pipeline for a quarter is often built 6 to 12 months before that quarter begins, and it cites an average B2B SaaS sales cycle of 84 days. Revenue you review this quarter mostly reflects spend from earlier quarters.

Cadence and owners

Salesmotion's advice is to organize them by funnel stage, give each one an owner. The owner column below is how we would assign them.

The 30-minute weekly review, copyable

1. Minutes 0 to 5: reconcile. Marketing operations confirms this week's opportunity count matches the CRM report and the pipeline target tracker. If the numbers disagree, fix that first.
2. Minutes 5 to 12: opportunities created by source. The demand gen lead names the two sources furthest off their weekly pace and states one reason for each.
3. Minutes 12 to 18: the handoff. The sales development lead reports MQL-to-SQL conversion and any leads that missed the response window in your service level agreement.
4. Minutes 18 to 24: cost and stalls. Review cost per opportunity by channel and list stalled opportunities by owner. Run the stalled count through the pipeline velocity calculator if the stalled count changed since last week.
5. Minutes 24 to 30: decisions. Record each decision with an owner and a date: pause, shift budget, or change a qualification rule. A meeting that ends without a written decision gets skipped next week.

One test keeps the weekly list short. ORM's rule for any vanity metric is: if it moves before opportunities do in your own history, keep it as a leading signal. If a metric fails that test, move it to the monthly read or drop it. For cycle length context by segment, see our sales cycle length benchmarks.

Which metrics to stop reporting, and the traps to avoid

The rule for moving a metric off the dashboard: if it can rise while opportunities stay flat, it goes in a reference report that channel owners read. It does not go on the page the pipeline review runs from. Only 23% of marketers report confidence that they are tracking the right key performance indicators (KPIs), so expect some resistance when the familiar tiles disappear.

ORM's glossary says impressions, clicks and open rates "prove a program ran. They do not prove it made pipeline." Keep one chart in the reference report that puts impressions beside opportunity count. The Digital Bloom notes that a rising impressions chart next to a stagnant opportunity count goes undetected when the two live in separate reports.

Four traps that break reconciliation

Mixed date bases. ORM defines pipeline-to-revenue conversion as closed-won value divided by pipeline created, same cohort. Label every tile with its date field, created date or close date. Never divide a close-date count by a created-date count.

Double-counted influence. Sourced pipeline credits the first touch. Influenced pipeline credits any touch before close, and The Digital Bloom warns that reporting only one distorts the story. Show both. Present influence as a share of deals, and never add campaign-level influence totals together, because one deal appears under every campaign that touched it.

Platform-reported conversions. Attribution windows and conversion definitions built for consumer sales produce misleading signals in long, multi-stakeholder B2B cycles. Ad platform conversion counts stay in the reference report. The dashboard counts only opportunities in your CRM system. To test whether a channel adds pipeline, use an incrementality test.

Blended cost figures. ORM's formula calculates CAC twice: (Sales cost + marketing cost) ÷ new customers, then marketing cost alone. Here is an illustrative case. Carry the blended figure into CAC payback.

What this page does not cover

This page does not cover building custom multi-touch attribution models, setting channel budgets, or comparing reporting tools. The benchmarks are reference points for checking your own field definitions. CAC, payback and ROI figures here are planning metrics, not financial advice.

Related on Take the Market: Marketing and Sales SLA Template: Stage Definitions, Response Times and Handoff Rules; Lead Scoring Model Template: Fit and Engagement Scores, Decay, and a Threshold Set from Closed-Won Data; Pipeline coverage calculator: the ratio you need from your own win rate and slippage.