Built for performance marketers
Most attribution tools are built for CMOs and analysts who look at quarterly dashboards. Performance marketers live somewhere else entirely: inside Google Ads, Meta Ads Manager, LinkedIn Campaign Manager, and TikTok Ads, making budget decisions every day against moving targets. Attriqs is built to feed those workflows, not replace them.
The goal is simple: give performance marketers a single, independent view of what is actually working across every platform, at the grain they optimise (campaign, keyword, audience, creative), fast enough to act on before next week's pacing review.
The daily pain points
Platform numbers do not reconcile
Meta claims 80 conversions. Google claims 60. Your order system shows 95 total. Which number feeds the weekly report?
Last click distorts daily optimisation
Prospecting campaigns get paused for "low ROAS" when they were actually feeding retargeting and branded search wins.
Creative testing is opaque
Platform-reported creative ROAS varies wildly with attribution window. Actual cross-platform impact of a winning ad is invisible in-platform.
Reporting is a full-time job
Pulling data from Google, Meta, LinkedIn, TikTok, and Shopify into one sheet eats hours that should be spent optimising.
What performance marketers need from attribution
- 1. Speed. Daily data, not monthly. Decisions happen on Monday morning, not on quarterly review day.
- 2. Granularity. Campaign, keyword, audience, creative. Channel-level is too coarse to drive action.
- 3. Cross-platform consistency. Every channel measured with the same rules, so comparing Meta against Google is apples to apples.
- 4. Model transparency. The ability to see credit under several models side-by-side, so disagreements become insights instead of arguments.
- 5. In-flow integration. Budget recommendations that can be actioned without a handoff, ideally pushed straight to ad platforms.
- 6. Offline capture. For businesses where phone calls or chat drive conversion, those touchpoints are first-class data, not afterthoughts.
Campaign and keyword-level truth
Channel-level ROAS is fine for a CMO report. It is useless for campaign optimisation. Knowing that Google Ads runs at 3.5x tells you nothing about which campaigns inside Google Ads are hitting 6x and which are losing money at 0.4x.
Attriqs runs attribution at the campaign and keyword level by default, across every connected platform, under every model simultaneously. The optimisation wins sit in the spread, not the average. For the full case, read campaign-level attribution.
Cross-platform ROAS reconciliation
Every platform over-reports its own contribution. Summed platform-reported ROAS routinely exceeds actual revenue by 30 to 80 percent. Performance marketers who act on platform-reported numbers are acting on numbers that do not sum.
Attriqs imports spend from Google Ads, Meta, LinkedIn, Amazon Ads, and more, then runs one attribution model over the combined data. The result is cross-platform ROAS calculated from a single source of truth, with no double-counting. For the deeper treatment, see reported vs true incremental ROAS and how to track ROAS by channel.
Making faster budget decisions
The hardest part of performance marketing is not spotting what should change. It is acting on it fast enough to matter. Attriqs' budget optimisation turns the attributed view into actionable recommendations: which campaigns deserve more budget, which should be paused, and where the next $10k will earn the most.
Recommendations can be pushed directly to connected ad platforms so the loop between insight and action is a click, not a handoff.
Creative and audience analysis
The best-performing creative under last click is often not the best-performing creative under a full multi-touch view. Discovery-oriented creatives open journeys that close elsewhere. Retargeting creatives close journeys that started on a different channel.
Attribution at the creative level requires tagging every ad variant with a unique identifier. Combined with first-touch and linear models, this reveals creatives that start high-value journeys, which platform reporting will rarely show. Apply the same logic to audiences, and the scaling decisions become evidence-led.
Offline touchpoints in a digital workflow
For any business where phone calls or chat conversations drive conversion, digital-only attribution systematically undervalues the channels that generate them. Paid search for local services, for instance, often shows weak digital ROAS while driving most of the phone revenue.
DNI call tracking captures phone touchpoints as first-class data in the same attribution pipeline. Chat auto-detection does the same for live-chat conversations. The result is a performance marketing view that optimises against real revenue, not just the digital slice of it.
A weekly performance marketing workflow
- Monday: Open the cross-platform dashboard. Spot pacing variances, underperformers, and weekend anomalies.
- Tuesday: Drill into campaign and keyword ROAS under last click and position based. Flag disagreements for investigation.
- Wednesday: Creative and audience review. Identify scaling opportunities and fatigue.
- Thursday: Run budget optimisation recommendations, push proposed changes to ad platforms for review.
- Friday: Weekly report out with cross-platform attributed ROAS, channel-level trends, and incremental contribution highlights from MMM.
Frequently asked questions
What is performance marketing attribution?
Performance marketing attribution is the practice of tracing paid marketing spend to the specific campaigns, keywords, creatives, and audiences that drive revenue. It is the daily measurement layer performance teams use to optimise budget, pause underperformers, and scale winners at the campaign and keyword level.
How is performance marketing attribution different from general marketing attribution?
General marketing attribution sits at the channel and portfolio level and informs quarterly budget decisions. Performance marketing attribution is faster, more granular, and feeds into daily and weekly optimisation decisions inside ad platforms. Both rely on the same underlying data but slice it at different levels.
Can performance marketers just use Google Ads and Meta dashboards?
Platform dashboards are fine for in-platform tactical optimisation (bid changes, budget caps, audience tweaks) but they are structurally biased. Each platform over-reports its own contribution and ignores what happened on other platforms. Cross-platform decisions (where should the next $10k go?) need independent, platform-neutral attribution. Attriqs imports spend from Google Ads, Meta, LinkedIn, Amazon Ads, and more, then runs one consistent attribution model across every channel so cross-platform comparisons are apples to apples.
How quickly does attribution pay back for a performance marketer?
Fast. The typical pattern is that 20 to 40 percent of paid media spend is sitting on campaigns or keywords that look profitable under last click but are not incremental. Independent attribution surfaces these within days, and the reallocation wins show up in reported metrics within one to two full cycles.
What metrics should a performance marketer track daily?
Daily: campaign ROAS, CPA, CTR, and spend pacing, all against target. Weekly: keyword ROAS, creative performance, audience performance, cross-platform reconciliation. Monthly: channel-level ROAS, new vs returning customer split, incremental vs attributed ROAS comparison.
How do you measure creative performance with attribution?
Creative attribution requires tagging every ad variant with a unique identifier that flows through to the attribution system. Combined with multi-touch models, this reveals which creatives start journeys, which close them, and which are just vanity impressions. Creative that starts journeys often shows weak last-click ROAS but strong first-touch and linear ROAS.
Does attribution tell me which audiences to scale?
Yes, at the audience layer where tagging permits. Campaigns and audiences can be tagged via UTM parameters and ad platform integrations, and attribution surfaces which audiences drive high-value conversions rather than just high-volume clicks. LTV cohort analysis by audience reveals which audiences scale profitably over time.