The QBR Slide Call: Revenue-Predictive Metrics vs Vanity Metrics

Marketing Academy · Field Work●Product Marketing

All projects·From: Developer Marketing and DevRel: Reaching Technical Buyers

CoreAudit· 55 minutes

Adyen

Objective: Given a synthetic monthly developer-platform dashboard and a synthetic onboarding funnel, decide which 2 metrics deserve the QBR headline slide and identify the single biggest onboarding-friction fix.

You're the developer marketing manager at Adyen preparing the quarterly business review deck. Leadership wants a headline metrics slide, and the draft currently leads with GitHub star count.

Evaluate the current month's dashboard against the lesson's revenue-predictive metrics, replace the vanity headline, then use a synthetic onboarding funnel to find and fix the biggest single drop-off step.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeBuild the metrics triage table and the onboarding funnel drop-off table

Free and sufficient to rank metrics and quantify funnel drop-off before handing a fix to engineering

The process

2 steps

Step 01 of 02

Distinguishing revenue-predictive metrics from vanity metrics

GitHub stars correlate weakly with revenue because they measure curiosity, not commitment; documentation NPS, SDK download growth rate, time-to-first-API-call, developer-to-paid conversion, and API call growth are what actually predict revenue.

This month's dashboard shows GitHub stars +180 MoM, doc NPS 52, SDK downloads +3.1% WoW, TTFC 4m 20s, dev-to-paid conversion 2.8%, and API call growth +11% MoM. Which 2 metrics earn the QBR headline slide, and which one gets demoted to a footnote?

Google Sheets— List all 6 metrics with their current value and trend direction, then rank them by revenue-predictive strength.

Procedure

List all 6 metrics with this month's value and month-over-month trend
Mark each metric as revenue-predictive or vanity based on the lesson's guidance
Rank the revenue-predictive metrics by trend strength: API call growth +11%, dev-to-paid conversion 2.8% (trending up), doc NPS 52, SDK downloads +3.1%
Select the top 2 for the QBR headline slide and demote GitHub stars to a context footnote

Sample output

Metric                     Value     Trend        Category
GitHub stars               12,400    +180 MoM     Vanity
Doc NPS                    52        flat         Predictive
SDK downloads               -        +3.1% WoW    Predictive
Time-to-first-API-call     4m 20s    -            Predictive (friction)
Dev-to-paid conversion     2.8%      +0.3pt QoQ   Predictive
API call growth            -         +11% MoM     Predictive

Headline slide: dev-to-paid conversion, API call growth
Footnote: GitHub stars

Healthy

The QBR headline leads with dev-to-paid conversion and API call growth trends, not raw star count.

Unhealthy

The deck opens with '12,400 GitHub stars, +180 this month' as the top-line win.

What this means

Stars measure curiosity, not commitment — swapping the headline metric for the conversion trend tells leadership something they can act on.

So what do I do about it?

Symptom	Action	Effort
QBR deck opens with GitHub star count as the top metric	Replace the headline slide with dev-to-paid conversion rate trend and API call growth, demote stars to a footnote	30 min

YouYou can do this yourself, no engineering access required.

Step 02 of 02

Time-to-first-API-call as an onboarding friction proxy

The lesson's sandbox strategy treats friction as the enemy: the most effective developer funnel lets a developer send their first API call in under two minutes with no email form, sales call, or credit card in the way.

Current TTFC is 4m 20s against a 2-minute target. Given a 4-step onboarding funnel with a drop-off at each step, which single step should be cut or delayed first?

Google Sheets— Build a 4-step funnel table with percent of signups continuing at each step.

Procedure

Record the funnel: signup form (100% start) -> email verification (78% continue) -> API key generation (74% continue) -> first API call (61% continue)
Compute the percentage-point drop at each individual step
Identify the step with the single largest drop-off share
Propose a fix that removes or defers that step's friction without removing the gate entirely

Sample output

Step                    % Continuing  Drop from Prior Step
Signup form             100%          -
Email verification       78%          22 pts
API key generation       74%           4 pts
First API call           61%          13 pts

Largest single drop: email verification (22 pts)

Healthy

No single onboarding step accounts for more than roughly 10 points of drop-off.

Unhealthy

One step (here, email verification) accounts for more than double any other step's drop-off.

What this means

Email verification is the biggest single leak in the funnel, and it's also the step adding the most wait time before a developer can act.

So what do I do about it?

Symptom	Action	Effort
22% of signups abandon during the email verification wait	Issue a rate-limited sandbox key immediately at signup, and require verification only before a production key is issued	dev ticket

EitherYou or a developer can handle this, depending on your access.

Final deliverable

A revised QBR metrics slide (2 headline metrics, GitHub stars demoted to a footnote) plus one onboarding-funnel fix ranked by drop-off share.

See a reference example

Sample output

Squarespace developer platform, Q2 dashboard review (excerpt)

Headline metrics: dev-to-paid conversion 3.4% (+0.3pt QoQ), API call growth +14% MoM
Demoted: GitHub stars (context footnote only)

Funnel fix: remove mandatory email verification before sandbox key issuance — recovers roughly 22% of signups per cohort

Success criteria

You're done when you can:

Correctly demotes GitHub stars from the headline slide based on the lesson's revenue-correlation guidance
Selects the 2 strongest revenue-predictive metrics for the headline slide
Identifies email verification as the largest single drop-off step and proposes a fix that keeps the gate before production access