Strategy & Planning

How to set quarterly okrs as Small Marketing Teams

Strategy & PlanningFor Small Marketing Teams2 apps12 steps~24 min to set up

Your three-person team needs to set Q3 OKRs in two weeks. The data you actually need — pipeline contribution by channel, MQL trends, cost per lead by campaign, content engagement rates — lives in four different tools that don't talk to each other. Someone has to pull HubSpot deal data, manually join it against GA4 sessions, export Meta Ads spend into a spreadsheet, and build a slide deck from scratch before the leadership meeting. That process takes one person the better part of a day, the numbers are stale by the time you present them, and the OKRs you end up setting are based more on gut feel than the signal you actually have. The quarter starts before you've agreed on what winning even looks like.

Build this on Starch → See the apps used

Strategy & PlanningFor Small Marketing Teams2 apps12 steps~24 min to set up

Outcome

What you'll set up

A live marketing performance dashboard that joins HubSpot pipeline data, GA4 traffic, and ad spend from Meta, Google, and LinkedIn into one view — so your OKR baselines are grounded in actual numbers, not last quarter's spreadsheet export.

A structured OKR doc in your Notion workspace, auto-populated with current performance baselines and proposed targets, ready for your team to review and the CEO to approve.

A weekly check-in automation that surfaces progress against each KPI and flags off-track metrics — so your OKRs don't disappear into a doc and get ignored until Q4.

The Starch recipe

Apps, data, and prompts

The combination of Starch apps, the data sources they pull from, and the prompts you use to drive them.

Apps used

Knowledge Management Growth Analyst

Data sources & config

Starch connects directly to HubSpot (scheduled sync — contacts, companies, deals, owners refresh on a schedule). Connect Google Analytics 4, Meta Ads, Google Ads, and LinkedIn Ads from Starch's integration catalog; the agent queries them live when your dashboard or automation runs. Starch syncs your Notion data on a schedule to read and write your OKR doc. Slack is connected from the integration catalog for weekly digest delivery.

Prompts to copy

Build me a Q3 OKR dashboard for a 3-person marketing team. Pull HubSpot deals closed this quarter and last quarter, broken down by lead source and channel. Connect Google Analytics 4 to show organic sessions, paid sessions, and conversion rate by channel. Pull Meta Ads and Google Ads spend from the integration catalog and show cost per MQL by campaign. Surface the last 90 days as baselines so we can set realistic targets.

Every Monday at 8am, pull this week's HubSpot pipeline-contribution metrics, GA4 traffic by channel, and ad spend vs. budget from Meta and Google Ads. Compare against the Q3 OKR targets stored in our Notion OKR doc. If any metric is more than 15% off pace, flag it in Slack with a one-sentence summary of what moved.

Create a Knowledge Management space for our Q3 OKRs. Pull the baselines from the dashboard we built, generate a structured OKR doc with Objectives, Key Results, and owners for each one, and save it to Notion. Include last quarter's final numbers as context so we can see what we're building on.

Run these in Starch → or paste them into your favorite agent

Walkthrough

Step-by-step

1 Connect HubSpot to Starch — it syncs your contacts, deals, and pipeline data on a schedule, so every time your dashboard runs it's working from a current snapshot, not last Tuesday's export.

2 Connect GA4, Meta Ads, Google Ads, and LinkedIn Ads from Starch's integration catalog. These are queried live when your dashboard or automations run — no manual exporting.

3 Connect Notion so Starch can read your existing docs and write your OKR output back to a structured page your team can edit directly.

4 Connect Slack from the integration catalog so your weekly check-in automation can post to your #marketing or #leadership channel.

5 Start with the Growth Analyst app and customize it: describe the specific channels, campaigns, and funnel stages your team cares about. Tell Starch your MQL definition, what counts as pipeline contribution, and which campaigns were active last quarter.

6 Pull 90 days of baseline data — sessions by channel, MQL volume by source, pipeline influenced by marketing, cost per MQL by campaign. This becomes the factual starting point for your OKR targets rather than gut feel.

7 Ask Starch to draft your Q3 OKRs: 'Given these baselines, suggest 3 Objectives and 2–3 Key Results each for a marketing team focused on pipeline contribution, content-driven organic growth, and paid efficiency. Include a stretch target and a baseline target for each KR.'

8 Use the Knowledge Management app to save the OKR draft to Notion with the baselines embedded — current MQL volume, channel breakdown, CPL by campaign — so reviewers see the data behind each target, not just a number.

9 Run a pre-read meeting using Meeting Notes to capture the team's edits, decisions, and any targets the CEO pushes back on. Action items come out automatically so nothing falls through the cracks.

10 Once OKRs are locked, set up the weekly check-in automation: every Monday morning, Starch pulls the latest HubSpot, GA4, and ads data, compares it against your Notion OKR targets, and posts a progress update to Slack with any off-track flags.

11 At the 6-week mark, ask Starch to generate a mid-quarter OKR review: 'Summarize progress against each Q3 Key Result, show the trend line, and flag anything that needs a strategy change.' This takes minutes rather than a half-day of data assembly.

12 Export the final OKR summary as a slide using the Presentation Agent — currently in development, request beta access — or paste the Starch output directly into your board update doc.

See this running on Starch

Connect your tools, describe what you want, and the agent builds it. Closed beta is free.

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Worked example

Q3 2026 OKR Kickoff — July 1 Deadline

Sample numbers from a real run

HubSpot MQLs (Q2 actuals)	312
Organic sessions / month (Q2 avg)	48,200
Meta + Google Ads CPL (Q2 avg)	187
Marketing-influenced pipeline (Q2)	1,840,000
LinkedIn Ads spend (Q2 total)	24,000

It's June 18. You have two weeks before Q3 starts and a leadership sync on June 26 where the CEO wants to see the marketing team's OKRs with baselines attached. Normally this means one person spending Tuesday pulling HubSpot reports, another exporting GA4 into Sheets, and a third trying to reconcile Meta and Google Ads numbers that don't match because the attribution windows are different. This time, you describe what you need in Starch: a dashboard that joins HubSpot deal data (scheduled sync, refreshed daily) against GA4 channel sessions (live query from the integration catalog) and Meta/Google Ads cost data (live query). Starch builds it. You see that Q2 MQL volume was 312, organic sessions averaged 48,200 per month, and your blended CPL across paid channels was $187 — down from $224 in Q1 because you paused two underperforming LinkedIn Ads campaigns mid-quarter. Marketing influenced $1.84M in pipeline. From those baselines, you ask Starch to draft OKRs: Objective 1 is growing marketing-influenced pipeline to $2.5M, with KRs around MQL volume (400/quarter), CPL reduction to under $160, and one net-new channel test completed. The draft goes into Notion via the Knowledge Management app with the baselines embedded. At the June 26 meeting — captured in Meeting Notes — the CEO approves two of the three objectives and pushes back on the CPL target as too conservative given the budget freeze. That decision is captured automatically. By June 30, the OKRs are locked and a Monday morning Slack automation is running, comparing actuals against targets every week.

Measurement

How you'll know it's working

Marketing-influenced pipeline ($ and % of total company pipeline)

MQL volume by lead source and channel

Cost per MQL by paid channel (Meta, Google, LinkedIn)

Organic sessions and conversion rate by landing page

Email-driven revenue or pipeline contribution (for lifecycle/nurture programs)

Comparison

What this replaces

The other ways teams handle this today, and how the Starch version compares.

Spreadsheet + manual export (HubSpot CSV + GA4 export + Meta Ads CSV)

Works but takes 4–6 hours to assemble every quarter and is stale the moment you build it; no automation, no alerting, no single source of truth.

Looker Studio / Google Data Studio

Good for visualization once connected, but requires someone with connector and schema knowledge to set up; no natural-language authoring, no OKR doc generation, no Slack alerting built in.

Notion AI (OKR templates)

Great for structure and collaborative editing, but Notion doesn't pull live data from HubSpot or your ad platforms — you're still manually entering the numbers it formats.

Lattice or Betterworks (OKR software)

Purpose-built for OKR tracking across a whole org, but priced for HR teams at 100+ people, requires IT setup, and still doesn't auto-populate baselines from your marketing data stack.

On Starch RECOMMENDED

One platform — knowledge management, growth analyst all running on connected data. Setup in plain English; numbers stay current via scheduled syncs and live agent queries.

Try it on Starch →

FAQ

Frequently asked questions

We use Customer.io for email, not Mailchimp. Can Starch pull email performance data?

Customer.io is reachable from Starch's integration catalog — the agent queries it live when your dashboard runs. If it's not available directly in the catalog, Starch can automate your browser to pull campaign reports from the Customer.io interface — no API needed. Tell Starch what metrics you want (open rate, click rate, revenue attributed) and it figures out how to get them.

Does Starch actually write into Notion, or just read it?

Both. Starch syncs your Notion data on a schedule (reading pages and databases), and the agent can write new pages or update existing ones when you ask it to — like saving your OKR draft or appending weekly progress notes. The OKR doc it creates is a real Notion page your team can edit like any other.

Our HubSpot data is messy — deals are miscategorized and lead source fields are inconsistent. Will the baselines be accurate?

Starch surfaces what's in HubSpot — it won't clean up data hygiene problems in the source. The honest answer: your baselines will be as accurate as your HubSpot. The upside is that once Starch is connected and your dashboard is running, you'll see the gaps faster and can use them to argue for a cleanup sprint before OKRs lock.

Is Starch SOC 2 certified? We'd be connecting HubSpot and ad accounts.

Starch is not SOC 2 Type II certified today. If that's a hard requirement for connecting your marketing stack, it's worth flagging — it's on the roadmap but not available now.

Can Starch track OKR progress automatically throughout the quarter, or is it just a setup tool?

It does both. After OKRs are set, you configure a weekly automation: every Monday, Starch pulls the latest actuals from HubSpot, GA4, and your ad platforms, compares them against the targets in your Notion OKR doc, and posts a progress update to Slack. You can set thresholds — 'flag anything more than 15% off pace' — so you're not reviewing a wall of numbers, just the things that need attention.

What if we want to track OKRs for a channel Starch doesn't have a direct integration for — like a podcast or a partner program?

If the data lives in a web-accessible dashboard, Starch can automate your browser to pull it — no API needed. For things that are purely manual (a partner spreadsheet someone emails you), you can connect Google Sheets from the integration catalog and have the team update one sheet that Starch reads as a data source for the dashboard.