In the previous lesson we argued that delivering a personalized shopping experience — at scale, across multiple channels — is the job of a technical marketing department: a team whose expertise sits at the intersection of marketing and IT.
That raises an obvious question: what does that expertise actually consist of?
This is the map we use to answer it. We call it the Technical Marketing Pyramid, and it structures the key areas of expertise into three tiers. Explore it below — hover over (or tap) each tier — and then read on for our commentary.
Why a pyramid
The shape isn't decoration. Each tier is built on the one below it, and each tier sets a real limit on what can be built above it. Analytics can only be as good as the data being collected; activation can only be as smart as the understanding beneath it. The wider the base, the taller the pyramid can become.
The arrow along the side gives the whole structure its reading direction: from raw data at the bottom to revenue at the top. Everything below the peak exists to make the peak possible.
Tier 01 — Data Collection
Think of it this way: to personalize a customer's shopping experience, some data about that customer has to exist first. That data falls into three categories, and for each of them we've developed distinct strategies and use-cases to maximize collection.
First-party data. Observed customer behavior — what they browse, what they add to the cart, what they end up buying. The customer generates it simply by shopping; the expertise lies in capturing it completely and keeping it clean.
Zero-party data. Information the customer gives directly, by answering a question — a microsurvey on the website about their preferences, for instance. Nobody has to infer anything; the customer simply told you.
Email & SMS collection. Contact information more generally — capturing an email address or phone number so the customer becomes reachable through those channels at all. Without it, half the channels in Tier 03 don't exist for that customer.
This tier is the base of the pyramid for a reason. Every weakness here — a tracking gap, a consent problem, a thin address book — propagates upward and quietly caps everything above it.
Tier 02 — Analytics
This tier represents the ability to observe and understand the data that's already been collected. That ability isn't binary — much like eyesight, it's not that a team either sees perfectly or is blind. There's a whole spectrum in between, and most teams sit somewhere on it without knowing exactly where.
The way we bring structure to this tier is by thinking in terms of the objects being analyzed. In eCommerce, analytical questions almost always attach to one of five:
- Shop — the store as a whole: revenue, orders, traffic, conversion — and the trends behind them, so growth or decline can be traced to its source.
- Product — what sells, what gets browsed but never bought, what gets returned, what pulls other products along with it.
- Customer — who buys, how often, at what value, and how those patterns split across segments and cohorts.
- Subscriber — the reachable audience: how the email and SMS lists grow, engage, and decay.
- Campaign — what the marketing activity itself produces: which campaigns and use-cases actually move revenue, and which just make noise.
For each of these objects we've built analytical frameworks and reports, so understanding them doesn't depend on someone remembering to ask the right question.
Tier 03 — Activation
This is the layer where money is made, through revenue-generating use-cases.
We tend to think about this layer as one coherent customer experience delivered across multiple channels — the theme of lesson one. Even a single use-case like cart abandonment isn't an "email play"; it can be deployed across several channels at once, each surface carrying its part of the same experience. These are the channels we support:
- Email marketing
- SMS marketing
- Website personalization & CRO
- Mobile push notifications
- Mobile app personalization & CRO
- Loyalty mobile wallet cards
- Ad audiences
Reading the pyramid honestly
Here's how we tend to use this map in practice: as a diagnostic, not a checklist. When activation feels stuck — campaigns underperforming, personalization feeling generic, tests going nowhere — the cause usually lives a tier lower than where the symptom shows up. A weak campaign is often an analytics gap; an analytics gap is often a collection gap. The pyramid tells you where to look.
The second thing worth saying about this map: AI has reshaped how we work at every tier of it. Two examples from our own practice:
- In Analytics, the reporting piece has changed a lot. Where we would once have built reports in Power BI or Tableau, we now build them with Claude Code as HTML and host them in a custom-built, Tableau-like solution of our own, refreshed on whatever schedule each report needs — faster to build and fully tailored to each object's framework.
- In Activation, we've developed frameworks for AI-created weblayers and email assets that have skyrocketed the number of use-cases and A/B tests we can deploy every month.
That's not a coincidence, and it will come up again in the next lesson, where AI appears as a working part of the stack itself.
Where this course goes next
The pyramid maps what a technical marketing department must be able to do. The natural follow-up is what it does that with. In the next lesson we'll look at the technology stack — the tracking, activation, and storage infrastructure this expertise runs on.
Datacop is a fractional, AI-enabled technical marketing department for $25M–$2B eCommerce brands. The Academy is where we write down how we work.