Most marketing teams are sitting on a data goldmine and extracting almost nothing from it.
Not because they lack the right tools or because they need more AI. But because they haven’t done the unglamorous work of making the data they already own actually talk to each other.
At Somebody Digital, we run bi-weekly Marketing IQ Live workshops where we dig into the real challenges marketing leaders are navigating right now. And right now, one challenge keeps coming up: the gap between generating data and generating revenue from it.
The CMOs who are closing that gap aren’t doing anything exotic.
They’re doing three things consistently that most of their competitors aren’t. We call it data enrichment, and here’s what it actually looks like in practice.
First, a problem you’ll recognise
Your paid media platform reports one conversion number while your analytics platform reports another. At the same time, your CRM reports a third. When you present to the board, and someone asks which number is correct, you can’t give them an accurate response.
This is an enrichment problem, and not actually a data problem. The three systems aren’t drawing from the same source, so they’re answering slightly different questions. And the fix isn’t another dashboard or a new tool; it’s doing the work to connect them.
Enrichment Type 1: Enrich the Algorithm
Most B2B marketing teams are still optimising their campaigns for the lead, even though they know it’s wrong. Turn on LinkedIn on any given day, and everyone’s nodding vigorously about optimising to MQL. But when we audit accounts (and we do audit a lot of them), eight out of ten still aren’t doing this very thing.
The principle is simple: whatever stage of the funnel you’re currently measuring, just go one step deeper.
Measuring to lead? Do the work to measure the MQL. Already there? Push to sales-accepted lead, then to SQL, then to pipeline. Each step deeper sharpens how your ad platforms allocate budget. When Google or LinkedIn knows which clicks eventually become pipeline, not just leads, it starts finding more of those people. The algorithm gets smarter because you gave it smarter inputs.
This doesn’t entail onboarding a new tool; it’s just about managing CRM integration. “It is essentially connecting Salesforce (or whatever you’re using) to your paid platforms so the feedback loop closes properly,” says Cristiano Winckler, Director of Digital Operations at Somebody Digital. “The native integrations exist. The competitive advantage is just in actually doing it, because right now, less than 10% of the market is.”
The practical challenge: it’s cross-functional, slightly cumbersome, and requires marketing and sales to agree on what a quality lead actually looks like. That’s why it doesn’t happen as frequently as it should. But the teams that push through it consistently find budget they didn’t know they had, by stopping spend on lead sources that were never going to close.
Enrichment Type 2: Enrich the Dashboard
Once you’re measuring deeper, you need somewhere to see it clearly, and not in a platform-specific report. A single source of truth that shows the full funnel, from click to closed, in one place, for every stakeholder in the room.
This matters for two reasons, the first of which is operational.
When paid media, analytics, and CRM are all pointing to the same numbers, your team makes faster, better-aligned decisions, and the silos dissolve.
The second is credibility. Marketing teams lose board confidence when they can’t connect their activity to revenue. This usually isn’t because the activity isn’t working, but because the measurement stops at the channel level. Impressions, CTR, cost-per-click: these are useful internal metrics, but they mean nothing to the person who signs the budget.
“When you can show the board a visualisation that maps the journey from lead to MQL to SQL to pipeline, and show how that’s trending over time, the conversation changes completely,” says Cristiano. You stop defending channel performance and start presenting business outcomes. “That’s how you protect your budget, and how you make the case for growing it.”
One more thing worth saying here: boards can be just as guilty as anyone of chasing the wrong metric. Enriching the dashboard also gives you the tools to educate upward to show, clearly, that every lead is not an MQL, and that the job isn’t lead volume, it’s pipeline quality.
Enrichment Type 3: Enrich the Click
The third enrichment type is the most underused, and arguably the highest ROI activity a full-service digital marketing team can run.
The idea is to use everything you already know about a visitor before they land on your site, to personalise what they experience when they get there.
Have they visited before? What pages did they look at? What vertical are they in? What problem are they likely trying to solve? In B2B, this data is often sitting in your CRM, your analytics platform, and your ad pixels, but connected to nothing.
When you wire it up, you can dynamically adjust landing page content, surface the most relevant case study, or fast-track someone to the specific service page that matches what they’ve been researching. The conversion rate impact is significant. Across every programme we run, conversion rate optimisation (which is ultimately what this is) consistently delivers the strongest return on investment.
Instead of buying more traffic, you’re making your existing traffic work harder.
The uncomfortable truth about AI and data
There’s a widely-cited Gartner projection that more than 40% of AI automation projects will fail by 2027. If you read the full report, data quality is one of the primary reasons.
Automating broken data doesn’t fix the data, but it does make the mistakes faster.
The fundamentals haven’t changed. The robustness of your tracking setup, the consistency of your data governance, and the granularity of what you’re capturing are as important now as they were twenty years ago. Possibly more so, because AI amplifies whatever you put into it, good or bad.
Before you layer the latest AI insight tool on top of your analytics stack, ask an honest question: Is the data underneath actually reliable? If not, you don’t have an AI problem. You have a data problem, and the solution is the same one it’s always been.
What to do on Monday
Wherever you’re measuring to right now, go one step deeper.
If you’re measuring to lead, find out what it takes to get your CRM data feeding back to your campaigns. If you’re already measuring to MQL, push to SQL. If you have a multi-touch attribution model, look for the correlations you haven’t found yet. (One of our clients discovered that visitors to their documentation subdomain are nine times more likely to request a demo. That single insight reshaped their content strategy.)
Reports don’t make money. Enriched data, data that feeds forward into better decisions, better-targeted spend, and better-personalised experiences, does.
Marketing IQ Live is Somebody Digital’s bi-weekly workshop series for marketing leaders. We cover the tools, tactics, and strategies we’re seeing work across our client base. Join the next session
The core problem is the gap between generating data and generating revenue from it, because teams haven’t done the work to make the data they already own “talk to each other”.
The three types of data enrichment are Enrich the Algorithm, Enrich the Dashboard, and Enrich the Click.
It involves measuring deeper in the funnel (e.g., from lead to MQL, SQL, or pipeline) to give ad platforms smarter inputs, which sharpens budget allocation and helps find clicks that eventually become pipeline, not just leads.
It uses everything known about a visitor before they land on your site (like pages they looked at or their vertical) to personalize their on-site experience. This leads to a significant conversion rate impact and delivers the strongest return on investment by making existing traffic work harder.
Data quality is a primary reason why many AI projects are projected to fail. Automating broken data doesn’t fix it; it just makes mistakes faster because AI amplifies whatever you put into it, good or bad.
