MarTech Software Development Company

Off-the-shelf CDPs handle standard event collection, identity resolution, and audience activation well for most use cases. Custom CDP development makes sense when your data sources include systems the platform doesn't support natively and the integration complexity makes the platform more expensive than building. It also makes sense when your identity resolution logic is specific to your customer model and the platform's default logic produces too many merge errors. If you're building a data product for other companies and need multi-tenant architecture and white-labelling, standard CDP platforms won't cover that. And if your data volume and query patterns make platform pricing prohibitive, building is often the better path. We assess which approach fits your situation before recommending custom development.

Yes. Integration with e-commerce platforms (Shopify, WooCommerce, Magento, custom platforms) and POS systems (Square, Lightspeed, NCR, Oracle Retail) is standard for loyalty projects. The integration approach depends on what API each system exposes. For e-commerce, we typically integrate at the order confirmation event to credit points and at checkout to redeem them. For POS, integration complexity varies. Modern cloud POS systems have clean APIs. Legacy on-premise systems may require middleware. We scope integrations in discovery because they drive project complexity and timeline.

Multi-touch attribution requires two things: complete touchpoint data and a model that assigns credit across those touchpoints. We start by auditing your data collection: which channels are tracked, what events are captured, how sessions and users are identified across channels. Gaps in tracking undermine any attribution model. Once the data layer is clean, we build the attribution model. Rules-based approaches (linear, time decay, position-based) work for simpler use cases. Data-driven models trained on your conversion data produce more accurate credit distribution. The model outputs channel contribution scores that feed your budget allocation decisions.

We've built loyalty platforms for retailers, hospitality groups, restaurant chains, medspa networks, and multi-location service businesses. Loyalty programme complexity ranges from simple points-per-purchase to multi-tier programmes with partner redemption, behaviour-based earning, and coalition mechanics. The engineering scope correlates with programme design complexity. A simple stamp card replacement is a straightforward project. A tiered programme with a branded mobile app, POS integration, and real-time personalised offers is a substantial build. See our Loyalty Programme Development page for the full picture.

Consent is part of the data model, not an afterthought. We capture consent state per channel and per purpose at the point of collection, store it against the unified profile, and enforce it at activation so a suppressed contact never flows to a downstream channel. For GDPR and CCPA, we build data subject access and deletion into the platform: a request propagates across the systems the CDP owns rather than living in a spreadsheet. For email, we maintain suppression lists and honour unsubscribe requests inside the CAN-SPAM window. Where data residency matters, we scope regional storage during discovery.

Most projects launch a validated v1 in 12 to 14 weeks at a fixed cost agreed before work starts. What drives the cost is the number of data sources, the complexity of your identity logic, and how many channels need activation. A CDP on standard sources costs less than one reconciling offline, in-store, and app data with custom identity rules. We scope it in discovery and fix the price before development starts.

The layers they won't give you: identity logic specific to your customer model, attribution you can defend, custom loyalty mechanics, and the data product itself if you're building for other companies. If your gap is a configuration inside software you already own, we'll tell you that instead.

We audit what the current stack holds, migrate historical customer data with identity preserved, and run old and new systems in parallel until the numbers agree. Your team is trained on the data model so they can extend it without us.