What is a marketing data platform?
Marketing data platform means something different to every vendor. Here's a plain definition for B2B teams, how it differs from a CDP, and what to ask before you buy one.
Joe Fusaro
Founder, Datachef
Table of Contents
- What does it actually mean?
- Isn't that just a CDP?
- What should it do for you?
- What should you ask before you buy one?
What does it actually mean?
If you search for "marketing data platform", you'll find CDPs, ETL tools, attribution tools and dashboards all using the same phrase. So here's how I'd define it for a B2B team:
A marketing data platform gets all of your sales and marketing data in the same place and lets marketers work with it without filing a ticket.
One environment for data is crucial so that you can "connect the dots" between isolated metrics like opens, clicks and sessions; on their own these are mostly vanity metrics. They only become useful when you can connect them to pipeline and revenue. (I wrote about why in Marketing analytics has a reputation problem.)
So in practice, it pulls from all of your marketing systems — your marketing automation platform, CRM, ad platforms, web analytics and possibly even your finance systems. All in the same place, you can build powerful data models that enable more informed decision making.
Isn't that just a CDP?
People mix these up a lot, and the vendors themselves don't help.
A customer data platform (CDP) stitches together individual customer profiles so you can personalize what someone sees on your website or in your app. It's mostly a B2C tool, and it's mostly about activation.
B2B marketing questions look different. The unit is usually an account or an opportunity, not a single person. Five people from one company might touch your campaigns over six months before one of them becomes an opportunity contact. The questions are about pipeline: which programs create it, how fast it moves and what it's worth.
That's an analysis problem. It looks a lot more like a data warehouse for marketing than a CDP. Really, it's a full-stack data platform pointed at your go-to-market data.
What should it do for you?
Rather than a feature list, here's what you should be able to do once it's set up:
- Answer a cross-system question yourself. E.g. "what's our cost per opportunity by channel?" means ad spend plus CRM data. If that still needs a data engineer, you don't have a platform yet.
- See history. "What did pipeline look like on March 31?" CRMs mostly store current values, so the platform has to keep its own history.
- Define things once. MQL, sourced pipeline and your attribution rules should be written once and reused, not rebuilt in every report. I've seen this referred to as a "semantic model."
- Show the logic. Anyone looking at a number should be able to see how it was calculated.
- Send results back. A lead score or account tier sitting in a dashboard doesn't change much. It needs to land back in your CRM and marketing automation platform, where reps and campaigns can use it.
If a product only does one or two of these, it's a tool you'll need to wire up to other tools.
What should you ask before you buy one?
- Does it understand B2B data, like campaign members and opportunity contact roles, or does it just see tables?
- Can it sync your custom fields? Most real questions need at least one.
- Who keeps it running? If the answer is "your data engineer," add their salary to the price.
- Is pricing fixed or usage-based?
- How long until you get your first answer, using your data and not the demo data?
Related reading
- Managed data warehouse: What does "managed" actually cover?
- Data warehouse as a service: What it solves, and what it doesn't
Need a full-stack data platform? Watch a demo, or try Datachef free.