Every ad platform claiming credit for the same conversion, leaving your marketing team unable to trust any channel's reported ROAS for budget decisions?
Running multi-channel campaigns but your attribution model is still last-click because building something better requires data engineering work your team doesn't have bandwidth for?
Marketing Attribution Platform Development
A platform that collects every touchpoint across paid media, email, SEO, and offline channels -- resolves identity, applies the attribution model that fits your business, and tells you where each conversion actually came from.
We build custom attribution platforms for marketing teams and MarTech companies that have outgrown last-click and need a system their budget decisions can actually rely on.
Multi-touch attribution models including Shapley value
Cross-channel data collection and identity stitching
Incrementality measurement and conversion lift testing
Budget optimisation tools built on attribution outputs
A custom marketing attribution platform collects cross-channel touchpoint data, resolves identity across anonymous and logged-in sessions, applies multi-touch attribution models, and outputs budget recommendations tied to real conversion credit. RaftLabs builds attribution platforms for marketing teams and MarTech companies that need more than last-click. Most builds deliver in 10 to 14 weeks at a fixed cost.
100+Products shipped
·Multi-touchAttribution
·FixedCost delivery
·10-14Week delivery
Last-click attribution is a budget allocation problem
When every channel takes credit for the same conversion, the marketing team can't make a reliable budget decision. Paid search claims the conversion because it was the last click. Paid social claims it because it drove the first visit. Email claims it because the user opened a message three days earlier. Each platform's dashboard looks good. The business has no idea which channel actually moved the needle.
Multi-touch attribution does not just distribute credit differently. It changes what budget decisions you can make with confidence. When you know the marginal contribution of each channel to each conversion -- and you can test that contribution through incrementality measurement -- you can allocate spend toward channels that produce outcomes rather than channels that produce last-click credit.
We build attribution platforms for two audiences: MarTech companies building attribution as a core product feature, and marketing teams at established businesses whose budget decisions depend on cross-channel data they cannot get from ad platform dashboards alone.
What we build
Multi-touch attribution models
Rules-based models -- linear, time decay, U-shaped, and W-shaped -- available alongside a data-driven Shapley value model that distributes conversion credit based on each channel's marginal contribution to the outcome. Attribution window configurable by channel and conversion type so a brand awareness display campaign uses a longer window than a retargeting click. Comparison view showing how credit shifts across model types so the marketing team can see the tradeoffs before committing to a model for budget reporting.
Cross-channel data collection
Server-side event tracking to collect touchpoints without relying on browser cookies or client-side JavaScript that ad blockers can strip. Hashed email matching to stitch a user's paid social click to their email open and their organic search visit into a single journey. Offline conversion import from in-store POS systems, phone sales records, and CRM closed-won events. Ad platform conversion API integration -- Meta CAPI, Google Enhanced Conversions, TikTok Events API -- so platform-reported data and your attribution system draw from the same event feed.
Identity resolution and journey stitching
Anonymous session to known user merge at the point of email capture or login, so pre-identification touchpoints are included in the attribution journey rather than dropped. Cross-device journey assembly using probabilistic signals for anonymous sessions and deterministic matching on login events. Customer journey visualisation showing every touchpoint from first visit to conversion with timestamps, channel, and campaign. De-duplication of cross-channel touch events so a single visit that fires both a pixel and a server-side event is not counted twice in the attribution model.
Incrementality measurement
Geo holdout test design and execution to measure whether a channel is driving incremental conversions or claiming credit for purchases that would have happened anyway. Conversion lift study integration with Meta and Google to layer platform-native incrementality data alongside your own holdout results. Test cell and control cell matching on historical behaviour to reduce selection bias. Statistical significance reporting so the team knows when a result is conclusive rather than acting on noise. Incrementality-adjusted ROAS calculation by channel so budget decisions reflect true lift, not reported conversions.
Marketing mix modelling
Bayesian or regression-based MMM built on your historical spend and revenue data to model channel contribution at a level of aggregation that is immune to cookie loss and ad blocker interference. Seasonality and external factor controls so the model separates channel contribution from baseline demand shifts. Spend saturation curve by channel showing where marginal returns start to fall. Long-run contribution analysis for channels with delayed effects -- SEO, TV, and out-of-home -- that last-click and multi-touch models consistently undervalue. Scenario planning tool for budget reallocation based on modelled channel response.
Budget optimisation and reporting
Attribution output converted into recommended budget allocation by channel, using marginal ROAS curves to show where each additional dollar produces the most return. Campaign performance dashboard with attribution-adjusted metrics -- not platform-reported conversions but modelled contribution from your own data. Weekly automated report for marketing leadership with top-line channel contribution, budget vs. actual, and incrementality results. Integration with Google Looker Studio or Tableau for self-serve analysis by the media planning team without requiring a data analyst for each report run.
Frequently asked questions
Last-click gives 100% of conversion credit to the final touchpoint before purchase. This means paid search captures credit for conversions where the customer first discovered the brand through a social ad, read three blog posts, and opened two emails -- because the last step was a branded search click. Budget follows the reported credit, so awareness and mid-funnel channels are systematically underfunded. The alternative is multi-touch attribution, which distributes credit across all touchpoints in the journey. Rules-based models like linear or time decay are a step up and are fast to implement. Data-driven models like Shapley value go further by calculating each channel's marginal contribution based on which journeys convert with and without that channel present.
Cookieless attribution relies on three approaches used together. Server-side tracking collects event data at the server level rather than through a browser pixel, so it is not affected by cookie restrictions or ad blockers. Hashed email matching uses a common identifier -- a hashed email address -- to link touchpoints across channels where the user has identified themselves, connecting a Meta ad click to an email open to a purchase without a third-party cookie. Modelled attribution -- including marketing mix modelling -- works at an aggregate level using spend and outcome data rather than individual-level tracking, so it does not depend on any cookie at all. A custom attribution platform can combine all three rather than relying on ad platform cookies that are increasingly restricted.
On the ad platform side: Meta, Google Ads, LinkedIn, TikTok, Pinterest, and programmatic platforms via their APIs and conversion APIs. On the owned channel side: email platforms (Klaviyo, Mailchimp, Braze), CRM (Salesforce, HubSpot), and your own website and app via server-side event tracking. On the offline side: in-store POS systems, phone sales call records, and CRM closed-won events imported on a scheduled basis. On the data warehouse side: BigQuery, Snowflake, and Redshift for storing the full event history and running attribution model queries. The specific integrations depend on your channel mix and stack -- we scope the integration list before pricing the build.
Cost depends on the number of channels, the attribution models required, whether incrementality testing infrastructure is in scope, and whether MMM modelling is included. A core platform covering multi-touch attribution across your main paid and owned channels, cross-channel data collection, identity stitching, and a reporting dashboard typically delivers in 10 to 14 weeks at a fixed cost. Adding incrementality test tooling, MMM, or a budget recommendation engine extends the scope. We scope every build before pricing it so you get a fixed cost tied to an agreed feature set, not a time-and-materials estimate. Talk to us and we will scope your specific requirements.
Tell us your channel mix, your current attribution setup, and what budget decisions you can't make reliably today. We'll scope a platform and give you a fixed cost.
