Supermetrics alternatives: what a data connector does

A data connector moves what each platform reports into a sheet, dashboard or warehouse; it does not decide what caused a sale. Supermetrics is listed as data extraction and aggregation with no attribution, from $44/mo billed annually (as read on 2026-09-08). Its alternatives are other connectors, reporting layers and Google's free connectors, and choosing among them comes down to sources, destinations and price basis. More platform data does not add a control group: in 15 Facebook experiments, observational methods often failed to reproduce the randomized result (Gordon et al., 2019).

What does a data connector do, and what does it leave out?

A connector pulls numbers out of ad platforms and analytics tools and writes them where you report: a spreadsheet, a BI tool or a warehouse.

If the data lands as each platform's own count under its own settings, the connector inherits the attribution windows and the overlaps between platforms. Adding rows does not add a counterfactual. Gordon and colleagues (Marketing Science, 2019, peer-reviewed; two of the four authors worked at Facebook) contrasted 15 U.S. advertising experiments at Facebook, with 500 million user-experiment observations and 1.6 billion ad impressions, against observational models. Their abstract says the observational methods "often fail to produce the same effects as the randomized experiments, even after conditioning on extensive demographic and behavioral variables". Those were user-level methods on Facebook data, not connectors, and no source cited here tests a connector's output against a holdout.

Which Supermetrics alternatives exist, and what do they list?

Supermetrics lists plans from $44/mo billed annually ($55 monthly) and Growth from $177/mo ($222 monthly), with extra destinations and users priced separately (pricing, as read on 2026-09-08). The alternatives below are each vendor's listed pricing as read on the date shown. Prices change without notice, so check the vendor's page before you rely on one.

• Other connectors and data hubs. Windsor.ai is listed as multi-touch attribution with data integration, tiered from $19 to $299/mo (pricing, as read on 2026-09-08). Funnel.io is listed as a marketing data hub at $300/mo (Starter) and $600/mo (Business) billed annually, with its Measure add-on for MMM, MTA and incrementality paid separately, from $2,250/mo at $500K to $1M ad spend (pricing, as read on 2026-09-08).
• Reporting layers. Databox is listed as KPI dashboards with no attribution modeling, with a free plan and paid plans on its pricing page (pricing, as read on 2026-09-08). Whatagraph is listed as cross-channel reporting with no attribution modeling, Max from €699/mo billed annually and Prime on custom terms (pricing, as read on 2026-09-08).
• Google's own connectors. Google's documentation says free connectors built by Google cover Google Sheets, Google Ads and Google Analytics, and that community connectors may cost money. It also says Looker Studio is now called Data Studio (documentation). The free-route guide covers what each route leaves out.

Where a connector vendor also sells attribution or measurement, that is a second product with its own method and price. Judge it by the third check below, not by the connector.

How do you check a connector's numbers?

Three checks, one afternoon, in a spreadsheet:

1. Fidelity. Take the last full week. For each platform, compare the connector's purchases and revenue with the platform's own screen for the same dates, time zone, currency and attribution setting. Pass: they match, or differ by a setting you can name. Fail: an unexplained gap, so fix the connector before you build a report on it.
2. Overlap. Add up the purchases the platforms credit and compare the total with Shopify's orders for the same week. For illustration: 120 + 90 + 40 = 250 credited purchases against 160 Shopify orders, and 250 / 160 = 1.56. Pass: the total is at or below the order count, so the columns are at least not claiming more sales than happened. Fail: the total is above it, so some orders are credited more than once or counted under a different definition, and the columns should not be added.
3. Causality. Take the channel the platforms credit most. Cut or pause it in some regions, keep it on in matched regions, and run the test long enough to count orders (the holdout calculator gives the number of days). Pass: total orders in the cut regions fall in step with the orders credited there. Fail: total orders barely move. Treat any connector vendor's incrementality claim the same way.

Where does a one-off causal read fit?

A causal attribution read like Causality Engine's is not a connector. It takes one GA4 export, the Attribution paths CSV, and shows what each channel caused next to what last-click gave it, with Direct split back to the channels that sent those buyers, a data-health score and a next step for each channel. One read is €99, once, excluding VAT, refundable within 30 days; Pro is €299 a month. It works on GA4 channel groups, not campaigns, and takes no spend data.

Sources, 30 September 2026: Supermetrics pricing (Supermetrics, as read 2026-09-08); Windsor.ai pricing (Windsor.ai); Funnel.io pricing (Funnel); Databox pricing (Databox); Whatagraph pricing (Whatagraph); About data sources (Google Cloud Documentation, updated 2026-09-24); A Comparison of Approaches to Advertising Measurement (Gordon et al., Marketing Science, 2019).

Related answers

• Reporting without a connector subscription
• Two different jobs: moving data and estimating what each channel caused
• Attribution discrepancy
• Incrementality testing
• Causal attribution