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Grading your own Martech stack

Grading your own Martech stack

Every scoring framework in Martech is published by someone selling something, mine included. Here's what each dimension is worth, and the one I'd argue with.

You already grade your stack. You do it every time someone asks how it's going, and the answer changes depending on who's asking.

The board gets "maturing, nothing structural." Your data engineer gets "held together with string." Both are honest. Neither is written down.

Writing it down is the only thing that closes the gap. And the trouble starts there, because every framework you can reach for was published by someone with a position.

Everybody's framework is selling something

I went at this in July, looking at how the agentic CDP got defined.

The definition ranking top

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“Open CDP & Local AI CDP” Oh my…

“Open CDP & Local AI CDP” Oh my…

Two launches move the CDP's compute and its model inside your own boundary. Sovereignty doesn't make it cheaper, and the open question is who it's actually for.

In a regulated company, the question that stalls an AI project on customer data rarely comes from marketing. It comes from risk and legal, and it's simple enough.

Where does the customer data get processed, and where does the model run?

For a long time "in the vendor's cloud" was enough.

Then composable came along and "it never leaves our warehouse" became the better reply.

For banks, insurers and telcos, that reply is getting harder to approve too, because the warehouse belongs to somebody, and so does the model the agents call.

I'll be upfront about where

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What your Martech vendor's filings tell you before you renew

What your Martech vendor's filings tell you before you renew

Your vendor's annual report is free, public and full of things that change a renewal conversation. Including the one date nobody tells you they care about.

A bank buying a customer data platform has somebody, somewhere in the building, whose job is to check whether the company selling it can afford to still be there in three years.

A retailer buying the same platform on the same three-year term usually has no one doing that at all.

Same product, same paper, same length of commitment.

The difference is which industry you happen to work in, and whether anyone thought to open a document that's been sitting in public the whole time.

Does anybody actually do this?

In one corner of the market, yes, and

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Use cases should lead. Lead with what?

Use cases should lead. Lead with what?

Everyone agrees use cases should lead a platform decision. I agreed in print, then reached for the requirements template anyway. Here is what was missing.

Some time ago I wrote about why skipping the RFP isn't always an option. The piece opened on a comment Jay Calavas from Tealium had left under one of my LinkedIn posts:

"Skip the RFP and focus on use cases and measurable outcomes!"

I said he was right. Use cases should lead. I meant it, and would say it again tomorrow.

Then I sat down in the next selection and couldn't act on it.

I reached for the requirements template, because, look, it was there and the clock was running. The plan was that the use cases would

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What real time actually costs

What real time actually costs

Real time is several different speeds, with very different bills. What each one is for, and the cost nobody likes to mention.

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Sponsored by Databricks. Databricks commissioned and funded this guide and reviewed it before publication for factual accuracy. They did not get to change the conclusions.
Full disclosure at the foot of this post.

Somebody says "real time" in a meeting and the room nods.

It is the most agreeable phrase in martech, because everybody in the room assents to something slightly different and nobody finds out until the CFO starts looking for you.

I wrote about the architecture of this in July, in real-time from a warehouse:

How quickly context goes stale, where the work should sit, and
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Natural-language audience builders: my advice for vendors

Natural-language audience builders: my advice for vendors

Natural-language data interfaces let marketers skip the BI ticket, and the data engineer with it. Some advice for the product teams building them.

I talk to a lot of martech product teams. Part of my advisory work is getting an early look at features before they go GA, sometimes months ahead… well, weeks, more often, and giving feedback while it can still change something. Most of those previews have little in common. Lately one pattern keeps showing up, across companies that don't know they're building the same thing.

A marketer types a question in plain language. An LLM queries the warehouse and comes back with insights, an audience, and sometimes a full activation plan. No SQL, no BI ticket, no waiting

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