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Technology and Enablement is the fourth of the six capabilities in the Customer Relationship Maturity Model (CRMM), developed by Kate Barrett of eFocus Marketing, and the third in the Grow stage. It measures how well an organisation’s technology, data and automation enable customer relationship growth: whether the platforms and the data in them support the strategy that has been designed, or are the reason parts of it cannot be delivered.
This is the capability the market most often confuses with maturity itself. Every vendor maturity model scores adoption: is the system in, is the data in it, is it integrated. The CRMM scores something narrower and more useful, which is whether the technology you have enables the decisions you want to make.
Your technology can be excellent while your capability to use it is Level 2, and the reverse is also true: I have seen teams on a modest ESP with clean data out-deliver teams on an enterprise stack that nobody trusts.
Start with the honest questions. How much of what you want to do can your technology and data actually do? What was the last idea your team shelved because “the platform can’t do that”? How many manual steps sit between a customer doing something and your systems knowing about it? And does anyone in marketing trust your customer record enough to make a decision on it without checking somewhere else first?
The last of those is the one that matters most and gets asked least. Data trust is a capability question rather than a data quality metric (a record can be technically accurate and still be one nobody will act on), and every time I have looked at it the manual workarounds a team runs are a better measure of fragmentation than any integration diagram.
The capability also includes automation, and here the CRMM takes a position most platforms will not: automation should exist because customers need it, not because marketers want it. A journey that runs because it can, for customers who no longer fit it, is a Technology and Enablement failure however sophisticated the build.
The CRM Priority Check asks one question about this capability, with five statements describing how well technology, data and automation actually support the work. The levels below describe the evidence at each point.
Customer data and systems are fragmented and disconnected. Most CRM activity depends on manual effort just to make anything happen, and the team spends more time moving data than using it.
A core CRM platform and some automation are in place, but integration across the wider technology ecosystem is partial and requires ongoing manual effort. It works, as long as someone keeps holding it together.
Technology supports the strategy already designed. It is not the reason something cannot be delivered, and that one line alone puts an organisation ahead of most at this stage.
The infrastructure supports AI-assisted decisioning at scale, with data available at the speed effective customer decisions need, and it scales with growth in data or complexity without needing to be rebuilt.
Technology capability is treated as a strategic asset that evolves proactively in anticipation of future customer and business needs, rather than reacting to current limitations.
At Level 1, do not start with a platform business case; you will lose that argument without evidence. For two weeks, log every manual workaround your team does to move data between systems or to make a send happen. That log is your evidence for what fragmentation costs in hours, and it is a far stronger case than “our tech is behind”.
At Level 2, map which connections are still manual and rank them by how often someone has to touch them rather than by how technically hard they would be to fix. The highest-frequency manual step is usually where automation pays back fastest, even when it is not the most obvious gap on paper.
At Level 3, keep a running list of the strategic ideas your team has shelved because the technology cannot do them yet. It is easy to stop investing once things finally work, and that list is your genuine forward roadmap, and it stops technology quietly becoming next year’s constraint.
At Level 4, make sure there is a real kill switch: a fast, well-understood way to pause or roll back automated decisioning, tested occasionally rather than assumed, because speed and scale raise the cost of a bad decision.
At Level 5, keep a standing link between this capability and where the business is actually heading commercially, so “ahead of need” means ahead of your organisation’s need rather than ahead of the market’s.
Technology and Enablement is capability four of six in the Customer Relationship Maturity Model and the third in the Grow stage. It exists to enable the CRM Strategy and to make Execution Excellence possible at scale, against the lifecycle defined in Customer Progression Design.
Whether the data it holds can prove anything is a Measurement question, and whether it can run a genuine test is an Optimisation question.
When this scores lowest in a CRM Priority Check it is flagged as the capability to fix first, and the first conversation is usually about the data rather than the platform. ESP selection and audit work is where eFocus supports it.
Rarely on its own. Most Level 1 and 2 scores come from fragmentation and manual effort around your platform rather than from your platform itself, and a migration carries every data problem across with it. The two-week workaround log tells you which it is before anyone shortlists vendors.
A Martech model scores adoption and integration of tools. This capability scores whether the tools and data enable the customer decisions the strategy needs. You can score highly on adoption and Level 2 here, which is exactly the profile of an organisation that has bought well and cannot use what it bought.
Whether the people making CRM decisions will act on your customer record without checking another source first. If your team exports to a spreadsheet before every campaign, your platform may hold the data but nobody trusts it, and that is a Level 2 signal regardless of what your integration diagram says.
Volume of automation is not the measure. Level 3 is when technology is no longer the reason something cannot be delivered; Level 4 is when data is available at decision speed and the infrastructure scales without a rebuild. A lot of journeys running on stale data with manual list pulls in between is Level 2 with extra steps.
No, and the model is deliberately built that way. AI-assisted decisioning at Level 4 sits on top of clean, connected, trusted data at Level 3. Layering AI onto fragmented data does not skip a level; it automates the fragmentation.
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