Marketing Attribution & Analytics

Marketing attribution is the process of assigning credit to the marketing touchpoints that influenced a conversion. It matters because budget decisions depend on it. If your attribution model credits the wrong channels, you defund what’s actually working and invest more in what’s merely claiming credit. Most businesses are running on last-click attribution by default, which means every conversion is credited to whichever ad or email was clicked immediately before purchase, regardless of what drove the decision to buy. A customer who saw five touchpoints across three weeks looks identical to one who found you through a single search. That’s not attribution. That’s survivorship bias dressed as measurement.

Last-click attribution assigns 100% of conversion credit to the final touchpoint before purchase. Data-driven attribution uses machine learning to analyse your actual conversion paths and distribute credit proportionally based on each touchpoint’s statistical contribution. The difference in budget decisions can be significant. Channels like display, social awareness campaigns, and email often look underperforming in last-click models because they influence early in the journey. They rarely close sales, but they start them. Data-driven attribution surfaces that contribution. It requires sufficient conversion volume to be statistically reliable (typically 300+ monthly conversions), so for lower-volume businesses we’ll recommend the appropriate model for your situation.

Marketing mix modelling (MMM) is a statistical approach to attribution that works across all channels, including those where direct tracking is difficult or impossible, such as TV, radio, out-of-home, and organic social. Rather than relying on tracking pixels and cookies, MMM uses regression analysis to identify relationships between marketing spend, external factors (seasonality, competitor activity), and business outcomes. You need it if a significant portion of your marketing investment sits in channels that are genuinely hard to track, or if you want to model budget scenarios and forecast the likely impact of reallocating spend. For most businesses running primarily digital channels with solid tracking in place, multi-touch attribution is the right starting point. We’ll tell you honestly which approach fits your situation during discovery.

Incrementality testing measures whether a marketing channel is genuinely causing conversions or simply present when they happen. The method involves running controlled hold-out experiments: a portion of your audience doesn’t see a particular campaign or channel, and you compare conversion rates between the exposed and unexposed groups. The difference represents the true incremental lift. It’s the most reliable way to prove brand awareness spend is working, to validate whether a retargeting campaign is driving new purchases or just following people who were going to convert anyway, and to give finance teams defensible evidence of marketing’s contribution. We scope and run incrementality tests as part of our attribution engagements.

Every attribution project is different, so we provide exact pricing after understanding your specific requirements during discovery. Cost factors include your current tracking implementation quality, number of marketing channels, conversion volume, whether marketing mix modelling is required, and the complexity of your customer journey. During our discovery call, we’ll assess your situation and provide transparent pricing tailored to your needs. No surprises, no hidden costs. Just honest pricing based on the actual scope of work required.

Typical projects run four to six weeks from kickoff to delivery. The first two weeks involve auditing your existing tracking, identifying gaps, and mapping your customer journey across channels. Weeks three and four cover model configuration, cross-channel data integration, and validation testing. The final phase delivers your attribution framework, dashboard setup, and a training session covering how to read and act on the outputs. Projects requiring marketing mix modelling or significant tracking remediation take longer. We’ll scope accurately before we start.

Yes, and it’s more common than you’d think. Attribution quality is only as good as the underlying tracking, so we always audit your GA4 implementation, GTM setup, and platform integrations before configuring attribution models. If we find gaps, missing conversion events, broken cross-domain tracking, or consent configuration issues, we’ll identify what needs fixing and scope the remediation work separately. We won’t build attribution on top of broken foundations and pretend the outputs are reliable.

We work across Google Ads, Meta, LinkedIn, TikTok, Pinterest, programmatic display, email platforms (Klaviyo, Mailchimp, HubSpot, Dotdigital), and CRM systems including Salesforce and HubSpot CRM. For each platform we configure proper conversion imports, audience sharing, and data integration so your attribution models draw on complete data rather than platform-reported metrics alone. If you’re running a channel not listed here, get in touch. We’ll confirm compatibility during discovery.

ROAS (return on ad spend) is a platform-reported metric showing revenue divided by ad spend within a single advertising platform. It only counts conversions that platform claims credit for, using its own attribution model. Marketing attribution takes a channel-agnostic view across your entire marketing mix, applying a consistent model to all touchpoints rather than trusting each platform’s self-reported performance. The difference matters because every platform has an incentive to show strong ROAS. They apply attribution windows and models that favour themselves. Cross-channel attribution using your own data removes that bias and gives you comparable performance metrics across every channel you invest in.

Yes. This is one of the most common problems we solve. Brand awareness channels, including display, social, YouTube, and content, rarely drive direct conversions. Last-click models make them look worthless. Multi-touch attribution starts to surface their contribution, but incrementality testing is the most reliable method for proving genuine causal impact. We scope and run these tests as part of our attribution engagements, giving your team defensible numbers rather than directional estimates.

Often, yes, at least partially. Reliable attribution requires complete conversion tracking, consistent UTM parameter usage across all campaigns, cross-domain tracking configured correctly, and ideally server-side event collection to reduce cookie-based data loss. We audit your current setup as part of every attribution engagement and identify exactly what needs to change. Some clients have solid foundations needing minor adjustments. Others need more significant remediation. Either way, you’ll know what’s required before any work begins.

Yes. Many clients benefit from ongoing monitoring and model refinement after initial setup. Attribution isn’t set-and-forget. Channel mix changes, conversion volumes shift, new platforms get added, and attribution models need recalibration as your business evolves. Ongoing support typically includes monthly performance reviews, model accuracy checks, anomaly investigation, and strategic recommendations based on attribution data. Some clients prefer project-based work with periodic check-ins; others benefit from a monthly retainer for continuous support. We’ll discuss which approach fits your needs during discovery.

We validate attribution models before delivery by running back-testing against historical data, comparing model outputs against known outcomes to assess reliability. We also configure ongoing accuracy monitoring so you can see when model confidence changes. For data-driven attribution, we track the conversion volume feeding the model and flag when it drops below statistical reliability thresholds. We’re honest about uncertainty: attribution modelling involves statistical inference, not mathematical certainty. What we deliver is the most accurate, defensible picture of channel contribution available from your data, alongside clear documentation of model assumptions and limitations.

We combine analytics expertise with development capability. That matters for attribution because the most common attribution failures aren’t model problems. They’re tracking problems. Broken GTM implementations, missing conversion events, consent configuration blocking data collection, cross-domain journeys fragmenting into separate sessions. Most attribution specialists inherit whatever tracking exists and work around its gaps. We fix the foundations first, then build models on data you can trust. You also get honest scoping: if your conversion volume or channel mix doesn’t support a sophisticated attribution model, we’ll tell you and recommend the approach that actually fits your situation.