CDP delivery, by the numbers
04
- week delivery cycles
- 12-14
When customer data lives in separate systems with no shared identifier, every marketing operation that requires cross-system data becomes a manual process. Churn suppression requires an export, a lookup, a manual list upload. Personalisation requires a data analyst to join tables before the campaign team can brief the creative. By the time the data's ready, the moment has passed.
A CDP makes the unified customer record a live operational asset, not a report built on request. We build CDPs for two audiences: MarTech companies that need a data product at the centre of their platform, and businesses with enough data complexity that assembling it manually is blocking marketing operations.
Capabilities
What we build
01Data ingestion and identity resolution
Event streaming built on Segment, RudderStack, or Snowplow, all producing a consistent schema across web, mobile, and server-side sources. Deterministic matching on hashed email, phone, or customer ID merges records with high confidence; probabilistic matching on device fingerprints links likely-same-person anonymous sessions. Cross-device stitching merges pre-login history at the point of login. The identity graph runs on a real-time Kafka topic so merges propagate within seconds.
- Built with
- Segment · RudderStack · Snowplow · Kafka
02Unified customer profile
A single profile per resolved customer, aggregating behavioural events, transactional history, CRM attributes, and computed properties, schema-designed around your data model rather than a fixed generic structure. Computed attributes (LTV, days since last purchase, engagement score) update in real time via stream processing as events arrive. An XGBoost model trained on RFM features generates a predicted 12-month LTV score feeding acquisition and retention segments.
- Built with
- Kafka Streams · Apache Flink · XGBoost
A drag-and-drop segment builder lets marketing define audiences from any attribute, event, or computed property without SQL or a data team request. Real-time membership updates fire on event-triggered conditions, a purchase exits a win-back segment within seconds. Segment size estimates and overlap analysis show scale and conflict before a campaign is designed, and identity ties to first-party identifiers only, keeping the infrastructure cookieless-targeting-ready by design.
04Activation and downstream sync
Server-side activation to Meta, Google, LinkedIn, and TikTok via hashed identifiers avoids the match-rate decay of pixel-based uploads, typically running 50-80% match rates on first-party hashes. Bidirectional CRM sync with Salesforce and HubSpot keeps suppression and re-engagement current in both directions. Email platform sync to Klaviyo, Mailchimp, and Braze updates lists in real time. Data residency controls keep EU profile data in EU-region infrastructure.
- Built with
- Meta Conversions API · Google Customer Match
05Event pipeline and data quality
Kafka backs the pipeline; every event passes schema validation before reaching profile-update consumers, with malformed events routed to a dead-letter queue rather than silently corrupting the profile store. Deduplication uses event-ID idempotency windows to catch retries and double-fires. PII fields are hashed or masked at the stream-processing layer, with data lineage tracked per profile-attribute update for audit and GDPR minimisation evidence.
- Built with
- Apache Avro · JSON Schema registry
06Analytics and reporting
Segment size trends, profile coverage by source, and activation match rates by destination surface data-quality problems before they cause bad segment membership. LTV distribution by segment shows which audience definitions correlate with high-value customers, and CLTV prediction scores are available as a dimension in every segment-level report, built for marketers to act on, not for analysts to diagnose infrastructure.
How we work
From scope to live CDP
- Week 1
01Data source and identity scoping
We map your data sources, current identity gaps, and activation destinations. You leave week 1 with a written scope document and a fixed-price quote.
- Weeks 2-4
02Schema and identity graph design
Profile schema, identity resolution rules, and computed-attribute logic designed against your actual data model before build starts.
- Weeks 5-12
03Build and integrate
Ingestion, identity resolution, segmentation, and activation built in parallel, tested against real event volume every sprint.
- Final 2 weeks
04Launch and match-rate validation
Activation destinations validated for match rate before full rollout to paid media and lifecycle campaigns.
Why us
Why MarTech teams choose RaftLabs
01Senior engineers build what they scope
The engineers who assess your data architecture also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
02Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts.
039 years and 100+ products shipped
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building data infrastructure and MarTech platforms.
04Schema built around your data, not a vendor's
Custom schema means your product-specific attributes are first-class profile fields, not shoehorned into a generic SaaS CDP structure.
05We'll tell you when SaaS is the better call
If Segment or RudderStack covers your event volume and data model at a reasonable price, we'll say so. Custom is justified by scale or ownership requirements, not built by default.
Have a CDP project?
Tell us your data sources, your segmentation needs, and what your current stack can't do. We'll scope a platform and give you a fixed cost.
