MarTech Software Development Services for US & EU Marketing and Growth Teams

Both. We deliver custom CDPs on top of Snowflake, BigQuery or Databricks with reverse-ETL, and we extend or replace Segment, mParticle, Bloomreach and RudderStack when vendor costs or governance limits stop scaling.

We deploy a TCF v2.2-certified CMP, wire Consent Mode v2 signals into GA4, Google Ads, Floodlight and Meta CAPI, and run server-side tagging so tags only fire on the consents you actually have.

Yes. We collect first-party event streams, run rules-based and Markov or Shapley models, and reconcile against MMM and incrementality tests so attribution is defensible to finance, not just convenient for marketing.

We design lawful-basis flows, deploy a consent management platform, automate DSAR and opt-out, log consent server-side, and keep ePrivacy-aligned cookie storage in the EU and CCPA opt-out flows for US visitors.

Yes. We engineer to CAN-SPAM and CASL: confirmed opt-in, unsubscribe handling, sender authentication with DKIM/DMARC, and suppression list sync across providers.

We build OpenRTB-compliant bid request and response handlers, integrate with DSPs, SSPs and identity providers, and respect TCF v2.2 and GPC signals end-to-end.

Consolidation makes sense when vendor overlap creates conflicting customer profiles, consent records are duplicated across tools, or licensing costs have outpaced the incremental value each point solution delivers. We audit your current stack against these criteria during discovery, map the data flows and cost curves, and recommend consolidation only where the total cost of ownership — including integration maintenance — justifies the migration risk. Best-of-breed wins when each tool genuinely leads its category and your data team has capacity to govern the connections.

A DMP (Data Management Platform) is built around anonymous third-party audience segments — it excels for programmatic ad targeting but has no persistent cross-device identity and is rapidly losing signal as third-party cookies disappear. A CDP unifies known and pseudonymous first-party data across channels (web, app, CRM, POS) into a persistent customer profile that can power personalization, automation and analytics long after cookies are gone. Almost every growth team we work with needs a CDP today. A DMP is only relevant if you run large-scale programmatic media and have a mature consent framework to feed it clean signals.

First-party data strategy has four pillars: collection (server-side event tracking and direct measurement rather than relying on browser cookies), identity (login walls, preference centres and value exchanges that turn anonymous visitors into known contacts), activation (reverse-ETL from your warehouse to ad platforms via Customer Match and similar APIs), and measurement (incrementality tests and media mix models that do not depend on deterministic cross-site tracking). We design all four in sequence during the architecture phase, with a prioritised roadmap based on which revenue streams lose the most signal first.

Last-click is fast and simple but systematically over-credits bottom-funnel channels like branded search and retargeting while starving awareness investment of credit. Multi-touch models (linear, time-decay, data-driven) give a more honest picture of the path but require sufficient conversion volume and clean event data. Media mix modelling (MMM) is channel-agnostic and does not depend on user-level tracking, making it resilient to consent drop-out and cookie loss — but it is slower to produce results. We usually implement all three in parallel so you can triangulate: MTA for week-by-week optimisation, MMM for quarterly budget allocation, and incrementality tests to calibrate both.

Lifecycle automation ROI varies significantly by starting point. Brands migrating from batch-and-blast to behavioural triggers typically see a 15–30% lift in email revenue within 90 days of deploying win-back and post-purchase flows with proper segmentation. Lead-scoring automation in B2B contexts usually reduces sales cycle length by 10–20% once the scoring model is trained on two to three months of closed-won data. Time-to-value is fastest when the CDP is already feeding clean, real-time signals to the automation platform — which is why we sequence the data layer before the orchestration layer, not the other way around.