Marketing & Growth

How to set up pipeline attribution as DTC Brand Founders

Marketing & GrowthFor DTC Brand Founders3 apps12 steps~24 min to set up

You're running Meta and Google campaigns, pulling attribution reports from three different dashboards, and still not actually sure which ad drove the Shopify purchase. Your 'source' column in the orders export says 'direct' for half your revenue. You built a UTM tracking spreadsheet once, it broke after a site migration, and nobody fixed it. Klaviyo claims credit for the same order Meta already claimed. You're making creative and budget decisions based on vibes because your real attribution data is spread across Meta Ads Manager, Google Ads, Shopify analytics, and a Klaviyo flow report that nobody pulls consistently. You need one view that shows where pipeline actually started — and you need it before next month's ad spend decision.

Build this on Starch → See the apps used

Marketing & GrowthFor DTC Brand Founders3 apps12 steps~24 min to set up

Outcome

What you'll set up

A unified attribution dashboard that pulls your Meta, Google, and Shopify data into one view — so you can see which campaigns and creatives are actually generating orders, not just clicks

Automated weekly attribution summaries that tell you which channels drove the most first-purchase revenue, what your blended CAC looked like by source, and where spend is leaking

A live pipeline view that connects ad spend to order value, so when you reallocate budget toward a campaign you can immediately see how downstream revenue responds

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

Ads Agent Growth Analyst CRM

Data sources & config

Connect Meta Ads and Google Ads from Starch's integration catalog — the agent queries both live when your attribution dashboard runs. Connect Shopify from Starch's integration catalog for live order and revenue data. Starch syncs your Gmail data on a schedule for any email-touch context in your CRM view. Starch connects directly to PostHog if you're tracking on-site conversion events, and the Growth Analyst reads that data to build your weekly digest. For any attribution data living in a Google Sheet you've been maintaining manually, Starch can pull from Google Sheets via the integration catalog or you can describe the schema and Starch rebuilds the view natively.

Prompts to copy

Build me a pipeline attribution dashboard that shows spend by channel (Meta, Google) alongside Shopify orders and first-purchase revenue, grouped by UTM source and campaign name. I want to see CAC by channel and ROAS by campaign for the last 30 days.

Every Monday at 8am, send me a digest that summarizes last week's ad spend, which campaigns drove the most Shopify orders, and where my blended CAC moved week over week. Flag any campaign where spend went up but orders went down.

Create a view that shows my top 20 customers from the last 90 days, what campaign they were first attributed to, and their total LTV so far — pull from Shopify order history and match to UTM source where available.

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

Walkthrough

Step-by-step

1 Connect Meta Ads and Google Ads from Starch's integration catalog. Once connected, the agent can query campaign spend, impressions, clicks, and conversion events live whenever your attribution app runs.

2 Connect Shopify from Starch's integration catalog. This gives Starch access to your orders, customers, revenue by SKU, and UTM parameters captured at checkout — the source-of-truth for what actually converted.

3 Tell Starch what your attribution model looks like today. Type something like: 'I want first-touch attribution by UTM source, and I want to see last-touch too so I can compare. My Shopify orders have UTM parameters in the order notes.' Starch will build the schema around your actual data structure.

4 Start the Ads Agent beta request (currently in development) to get notified when cross-channel budget management launches. In the meantime, use the integration catalog connections to build your read-only attribution view.

5 Describe the pipeline attribution dashboard you actually want: 'Show me a table of all campaigns from the last 30 days, with columns for spend, orders attributed, revenue attributed, CAC, and ROAS. Group by channel first, then campaign name.' Starch builds this as a live app, not a static export.

6 Add a Klaviyo connection from Starch's integration catalog so email-driven revenue gets its own attribution row. This is the step that stops Klaviyo and Meta from double-counting the same order — you can see the overlap and decide how to handle it.

7 Set up the Growth Analyst app. It connects to PostHog for on-site behavior and Gmail for email context, and sends you a weekly digest. Customize the prompt: 'Include a section on which acquisition channels drove signups and first purchases last week, and flag any channel where conversion rate dropped more than 10% week over week.'

8 Build a 'leaky spend' alert: 'Every Friday afternoon, check my Meta and Google campaigns. Flag any ad set where spend this week is more than $200 and attributed Shopify orders are zero. Send me a Slack message with the list.' Starch automates this as a scheduled action, no manual check required.

9 Add a customer LTV layer to the attribution view. Prompt: 'For each acquisition source in my attribution dashboard, show me the average order value of first purchases and the average number of repeat orders within 90 days.' This turns CAC into a payback period calculation, not just a cost number.

10 Use the CRM app to track your highest-value cohorts. Tell Starch: 'Create a segment of customers whose first order was attributed to Meta prospecting campaigns and who have placed more than two orders. I want to see their email, total spend, and last order date.' This is the input for your retention and lookalike strategy.

11 Build a board-ready attribution summary automation. Prompt: 'On the last Friday of each month, generate a one-page attribution summary showing total ad spend by channel, total revenue attributed, blended CAC, and top 5 campaigns by ROAS. Format it as a table I can paste into a Google Doc.' Schedule it to run automatically so your monthly close doesn't require a manual data pull.

12 Iterate the model as you go. When a new campaign type launches or you add TikTok spend, just describe the addition: 'Add TikTok Ads to my attribution dashboard — same columns as Meta and Google.' Starch extends the app without rebuilding from scratch.

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

April 2026 — Spring campaign attribution close

Sample numbers from a real run

Meta Prospecting (Broad Lookalike)	8,400
Meta Retargeting (Abandoned Cart)	3,100
Google Shopping (Brand)	2,200
Google Shopping (Non-Brand)	4,700
Klaviyo Welcome Flow (email-only touch)	1,900
Total attributed Shopify revenue (first-touch)	61,000

You spent $18,400 across Meta and Google in April. Without attribution, you'd be looking at a blended ROAS of roughly 3.3x and guessing which campaigns to scale. With Starch pulling live data from Meta Ads and Google Ads via the integration catalog, and matching it against Shopify order UTM parameters, the picture is different: your Meta Retargeting abandoned cart campaign generated $14,800 in attributed revenue on $3,100 spend — a 4.8x ROAS — while your Google Non-Brand Shopping spent $4,700 and drove $7,200, a 1.5x that barely covers cost of goods after fulfillment. The Growth Analyst digest that landed Monday morning flagged this automatically: 'Non-Brand Shopping CAC is $47 vs. your 90-day LTV of $89 — marginal at current margins. Retargeting CAC is $9.80 against the same LTV.' You moved $1,500 of the Non-Brand budget into retargeting before Tuesday's campaign review. The Klaviyo welcome flow shows $1,900 in attributed revenue, but your Starch attribution dashboard shows 60% of those customers also touched a Meta ad within 7 days — so you're crediting email with assists, not full conversions. That's the number you bring to your next board update instead of the Klaviyo dashboard screenshot that claimed 100% email attribution.

Measurement

How you'll know it's working

CAC by acquisition channel (Meta prospecting, Meta retargeting, Google Shopping brand vs. non-brand, email)

First-purchase ROAS by campaign — not blended across all spend

90-day LTV by acquisition source cohort — are Meta customers worth more than Google customers over time?

Attribution overlap rate — what percentage of orders have both a paid ad touch and an email touch within the conversion window

Payback period by channel — how many weeks of repeat purchases to recover CAC at current AOV and gross margin

Comparison

What this replaces

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

Triple Whale

Strong DTC attribution out of the box, but it's a fixed product — you can't describe a custom pipeline view or build an automated Friday spend-check alert; you pay for a dashboard, not a surface you control.

Northbeam

More sophisticated multi-touch modeling than most tools at this price point, but starts at a spend threshold that prices out early-stage brands, and you still have to reconcile it manually against your Shopify actuals in a sheet.

Native Meta + Google + Shopify dashboards stitched in a Google Sheet

Free and flexible, but the sheet breaks on every UTM naming change, nobody owns it after the first month, and it can't trigger alerts or automate your monthly close summary.

Klaviyo attribution reports

Accurate for email-driven revenue if you live entirely inside Klaviyo's ecosystem, but it inflates email contribution by claiming credit for any order where an email was opened in the last 5 days — even if a Meta ad drove the conversion.

Looker Studio (formerly Google Data Studio)

Can pull from Google Ads and Shopify with the right connectors, but you're building and maintaining the data model yourself, there's no AI layer to flag anomalies or write your weekly digest, and adding Meta requires a paid connector.

On Starch RECOMMENDED

One platform — ads agent, growth analyst, crm 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

My Shopify UTM data is messy — a lot of orders show 'direct' or have blank source fields. Can Starch fix that?

Starch can surface the problem clearly and help you build better tracking going forward, but it can't retroactively reconstruct attribution for orders that weren't tagged. What it can do: show you exactly what percentage of your orders are unattributed, flag the UTM patterns that are breaking (usually a landing page that strips parameters), and help you build a checklist prompt — 'every time I create a new campaign, remind me to generate a UTM link using this naming convention.' The historical gap stays a gap; the fix is clean data from here forward.

Does Starch store my ad spend and Shopify data, or is it just querying live?

Meta Ads, Google Ads, and Shopify are all queried live from Starch's integration catalog — the agent pulls current data when your dashboard runs, rather than storing a historical archive in Starch. That means your attribution view always reflects the current state of your campaigns and orders. If you need long-term historical trend data (e.g., CAC by quarter going back two years), you'd want to export and store that separately; Starch is built for live operational surfaces, not a data warehouse.

The Ads Agent sounds like exactly what I need for budget reallocation — when is it available?

Ads Agent is currently in development. You can request beta access to get notified when it launches. In the meantime, you can build a read-only attribution dashboard today using Meta Ads and Google Ads via Starch's integration catalog, and set up automated alerts (like the Friday spend-check) without the Ads Agent — those run as scheduled automations, not through the agent app itself.

Klaviyo always shows higher revenue attribution than Meta does for the same orders. How does Starch handle the overlap?

Starch doesn't pick one attribution model and hide the others — it shows you what each source claims and lets you build your own logic on top. A prompt like 'show me orders where both a Meta click and a Klaviyo email open occurred within 7 days of purchase, and let me decide which gets credit' gives you the raw overlap data. You set the rule; Starch applies it consistently. That's more honest than any single-platform report that always crowns itself the winner.

Is Starch SOC 2 certified? I'd be connecting my Shopify store and ad accounts.

Not yet — Starch is not currently SOC 2 Type II certified. That's worth knowing before you connect revenue-sensitive systems. It's on the roadmap. If SOC 2 is a hard requirement for your team or investors, that's an honest reason to wait or check back on certification timing.

Can I track TikTok Ads attribution too, or just Meta and Google?

TikTok Ads is reachable through Starch's integration catalog of 3,000+ apps, so you can add it to your attribution dashboard. The setup is the same pattern: connect TikTok Ads from the catalog, describe the columns you want alongside Meta and Google, and Starch extends the existing view. If you already have a working dashboard, just tell Starch: 'Add TikTok Ads as a new channel row using the same spend/orders/CAC/ROAS columns.'