Admetrics vs Polar Analytics: attribution that moves the budget

Polar doesn't publish a price list. The pricing page puts figures behind an annual GMV selector and routes you to a demo, with a Core plan that bundles the products at a discount and a Custom plan that prices them individually. Cost scales with monthly tracked orders rather than seats, and every plan includes the Snowflake database, the first-party pixel, unlimited users, unlimited history and a dedicated success manager. The number that matters is the one outside the entry plan. Business intelligence, incrementality testing, Klaviyo Audiences, Advertising Signals and the MCP layer are separate products with separate prices, and intraday refresh, SQL access, demographic enrichment and custom connectors are add-ons on top of those.

Polar tracks profit. Contribution margin runs CM1 to CM3, with COGS, 3PL, shipping, payment fees and returns feeding the stack, and margin lands at both the campaign and the SKU; CM4 is something you build once yourself in the semantic layer. Admetrics calculates POAS down to SKU net of COGS, shipping, fees and returns, and carries the P&L through to EBITDA on your own cost data, refreshed intraday — with returns broken out by campaign and product rather than only netted into the margin line.

Ten, viewable side by side: first click through time decay, a paid-overlap view and a Shapley-based Full Impact model, with the lookback window and cash-vs-accrual basis set by you. Admetrics runs nine out of the box plus custom rules, so you can encode how your business actually splits credit instead of choosing the closest option from a fixed list. Model count matters less than what feeds it — Polar's runs on pixel and fingerprint data, Admetrics' on first-party server-side collection.

No. Polar has no MMM inside the product — incrementality comes as holdout tests a Polar data scientist configures on Meta, Google, TikTok or TV, which is a service engagement rather than a model you re-run each time the plan changes. Admetrics ships PRISMA, machine-learning MMM that sizes each channel's real impact including offline and upper-funnel, and feeds the Budget Allocator directly.

Yes, through the CAPI enhancer, and it covers Meta and Google. It's priced as a separate product, and there is no TikTok or Snapchat destination. Admetrics streams enriched conversions to six platforms — Meta, Google, TikTok, Snapchat, Outbrain and Taboola — as part of the core tracking, after first-party server-side collection recovers the 20–30% of conversions browser pixels lose.

Consolidation is the theme reviewers keep returning to. They describe retiring a stack of Sheets, Looker Studio connectors and freelance data work in favour of one platform, and setup speed and support both come up as reasons the switch stuck — support as named people rather than a ticket queue. The complaints are just as consistent: refresh latency, custom connectors that need a support specialist to build, a missing Amazon Ads connection, and cost relative to store size at the smaller end. The pattern is a measurement layer people trust and an operating layer they wait on.

For ecommerce and DTC teams that need the number to turn into an action, yes. Polar is a strong reporting layer — a dedicated Snowflake database on every plan, a 400+ metric semantic layer, CM1 to CM3 margin at campaign and SKU. What it doesn't do is close the loop: the CAPI enhancer reaches Meta and Google only and is sold separately, there is no MMM in the product, the agent recommends budget changes instead of making them, and there is no cross-store identity, no influencer ROI and no view inside the ad managers. Admetrics returns enriched signal to six platforms, sizes each channel with PRISMA, and executes the reallocation — and it starts with a free data audit and a trial, neither of which Polar offers.