Vendor lock-in is dependence on a platform whose data, integrations, or business logic make replacement difficult, costly, or disruptive. In marketing technology, lock-in grows when audience criteria, personalization fields, and experiment configurations cannot move easily to another system. section 4.4.11 section 4.2.1
Assess lock-in by asking:
- What happens if the solution must be offboarded? section 4.4.11
- Can business logic and first-party data be accessed in the marketing data lakehouse, or are they trapped in the platform? section 4.4.11
- Can the organization change downstream email, CRM, messaging, or other marketing systems independently? section 4.2.1
- Does the architecture favor open and extensible systems around common use cases? section 4.4.11
Table 4.12 Technology stack agility and lock-in considerations
| Solution |
Considerations |
| Composable CDPs |
(+) Composable CDPs are easier to offboard than most solutions because they snapshot most of their data back into your marketing data lakehouse. Like all CDPs, they also help you migrate downstream systems more easily. |
| Marketing clouds/engagement platforms |
(-) MCs are difficult to offboard because you build your business logic and first-party data into them in ways that cannot be easily ported to other systems. The limited integrations do not facilitate agility in changing out other solutions in your marketing technology stack. |
| Monolithic CDPs |
(+/-) Monolithic CDPs are difficult to offboard because you build your business logic and first-party data into them in ways that cannot be easily ported to other systems, but they do offer you flexibility to migrate or change downstream systems. |
| In-house builds |
(+) Builds are specifically designed to avoid third-party lock-in, though migrations away from in-house builds can be challenging. |
Compare lock-in across composable CDPs, marketing clouds or engagement platforms, and monolithic CDPs before choosing a first-party data solution. section 4.4.11
A lower-lock-in architecture
A composable architecture centralizes audience definitions, personalization fields, calculated attributes, and experiment configurations in the marketing data lakehouse. Downstream platforms receive audience membership lists and personalization fields, allowing the organization to change channels while retaining its marketing intelligence. section 4.2.1
