How to Track Marketing Activity vs Outcomes (Without Drowning in Spreadsheets)

Most SaaS marketing teams have the same blind spot: they can tell you what they did last month, and they can tell you what the metrics did last month, but they can't connect the two. Activity lives in Notion or Asana. Outcomes live in GA4, Stripe, and a hand-rolled spreadsheet. The question that matters — "did the work move the metric?" — falls into the gap between them.

This post is about how to close that gap without buying a $40k attribution platform. We'll cover why teams default to spreadsheets, what an activity-vs-outcome view actually looks like, and how to set one up with the tools you already have.

Why "activity OR outcomes" is the default

Two systems, two audiences. That's the trap.

The activity system — Notion, Asana, Trello, Linear — exists to make the team feel coordinated. Its audience is the people doing the work. Its job is "what's next?" It tracks tasks, owners, due dates, statuses. It says nothing about whether any of it worked.

The outcome system — GA4, Stripe, Mixpanel, a Google Sheet pulling from a few APIs — exists to make leadership feel informed. Its audience is the founder, the board, the investor update. Its job is "are the numbers going up?" It tracks MRR, signups, traffic, conversions. It says nothing about why.

Both systems are honest, and both are useless on their own. The team running tasks doesn't see the metrics. The founder reading metrics can't trace them back to specific work. Decisions get made on vibes — "the SEO push felt like it worked" — rather than evidence.

What an activity-vs-outcome view looks like

The minimum viable version is a single screen, per campaign, with three things on it:

1. The tasks you ran in a period (week or month), grouped by cadence, with who did them and when.
2. The outcome metrics for that same period — signups, MRR delta, traffic, email engagement — pulled from the source-of-truth tool (GA4 for traffic, Stripe for revenue, your email tool for opens/clicks).
3. Period-over-period deltas on both. Did task volume change? Did the metrics change? You're not running a regression — you're looking for visible alignment.

That's it. No attribution model. No multi-touch fancy math. The point isn't to prove causality with statistical rigor — for most SaaS at most stages, you don't have the volume for it anyway. The point is to make patterns visible so a human can spot them.

After three months of this view, you'll start saying things like: "We shipped 12 long-form posts in March. April traffic was up 14%. Worth doing again." Or: "We ran 8 paid experiments in Q2. Signups didn't move. Stop." That's the level of inference you need. It costs about an hour to set up and saves you months of guessing.

The data sources you need (and don't)

You don't need a CDP. You don't need Segment. You don't need Reverse ETL. You need three to five sources, each ingesting daily, into a single store. Here's the minimum useful set:

• GA4 (or Plausible / Fathom / Umami). Daily sessions and conversions per campaign. Free.
• Stripe. Daily MRR, new MRR, churned MRR. The numbers your investors care about. Free.
• X (Twitter) or LinkedIn. Followers, impressions, engagement on your business handle. Free if you have an API key.
• Email tool (Mailgun, ConvertKit, Resend, Customer.io). Daily sends, opens, clicks, unsubscribes. Free for most plans.
• (Optional) A search-volume tool — Ahrefs, Semrush, Search Console — for rankings. Skip until you have an SEO strategy worth tracking.

Each of these has an API. Each ingests in less than 100 lines of code. The hard part isn't the ingestion — it's having one place to put the data so the activity layer and the outcome layer sit side by side.

The schema that makes this trivial

Once you treat outcomes as just another time-series table — (source, metric, date, value) — the rest is glue. A row looks like:

• (ga4, sessions, 2026-04-19, 1247)
• (stripe, mrr, 2026-04-19, 12340)
• (twitter, followers, 2026-04-19, 5821)
• (email, opens, 2026-04-19, 412)

Optionally tag each row with a campaignId, so the same metric (sessions) can be scoped to "the AcmeApp landing page" vs the homepage. That single piece of structure is what unlocks per-campaign ROI views.

On the activity side, the structure is similar: a task has a periodKey(2026-W17 for weekly, 2026-04 for monthly) and one or more submissions for that period. Both sides share the same period-key vocabulary. That's the joining seam.

With both halves keyed on period and campaign, the view is one query: "for campaign X, week W, show me submitted tasks alongside metric deltas." No magic, no attribution model — just a filtered table.

How to read the view (so you don't fool yourself)

Three traps to avoid once you start looking at activity and outcomes side by side:

• Don't conclude after one period. A week of task volume up + signups up isn't causation; it's noise. Look at trailing 4–8 weeks before declaring anything.
• Don't blame metrics on the work done that same week. SEO investments take 90 days to land. Email investments take 1–2 weeks. Cold outreach takes 2–4 weeks. Match the activity window to the metric's known lag.
• Don't kill a channel after one bad month. Channels are seasonal. Customer conversations have year-long compounding effects. The decision rule is "does this channel show any signal over a quarter?" not "did it move the needle this week?"

With those guardrails, the view becomes diagnostic rather than performative. You stop arguing about which channel is best. You start running quarterly experiments and reading the results with intellectual honesty.

What this changes about how you work

The biggest change isn't the dashboard — it's the meeting. Once activity and outcomes live in the same view, weekly marketing standups stop being status reports ("here's what we did") and start being analysis sessions ("here's what we did, here's what moved, here's what to do about it").

A second change: prioritisation gets sharper. When you can see that 80% of last quarter's MRR delta came from one channel, you stop spreading effort across five. When you can see that an entire month of one activity moved nothing, you stop doing it — even if it felt productive.

A third change: hiring conversations get easier. "We need a content marketer" is hard to defend. "We need a content marketer because the 6 months we've spent on content have produced these specific signups and that specific traffic" is fundable.

The simplest setup, today

If you want this view tomorrow morning, here's the shortest path:

1. Pick one campaign. Just one. Your homepage / main product / whatever.
2. List the marketing tasks you actually ran in the last 4 weeks — even retroactively. Be specific: "wrote 3 blog posts," "sent 2 newsletters," "ran 8 cold outreach DMs."
3. Pull last 4 weeks of your three most-important outcome metrics — MRR, signups, sessions. Just numbers in a Google Sheet, weekly granularity.
4. Put both in one table. Five columns: week, tasks, MRR delta, signups, sessions. One row per week.
5. Do this for 12 weeks. Look at it monthly. Make decisions.

That's the floor. It's worse than a real tool, but it's better than nothing — and it'll teach you what fields you actually need before you invest in a system.

Or skip the spreadsheet

Cadence handles both halves out of the box: tasks per cadence per campaign on one side, daily ingestion from GA4 / Stripe / X / email tools on the other, joined on a shared period-key. The per-campaign view shows submitted tasks next to metric deltas. The weekly and monthly reports roll it up across every product you run.

We built it because we kept rebuilding this same dashboard for every SaaS we worked on. It shouldn't be a weekend project; it should be the default.

Related reading: The complete SaaS marketing checklist · The solo SaaS founder's marketing routine