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The vocabulary behind every Lab — from match rate to response curves — explained the way you'd explain it to a customer, then again the way you'd explain it to their data team.
First-party data
Data a business collects directly from its own customers — purchases, sign-ups, app usage — as opposed to data bought or licensed from someone else.
Identity resolution
The process of recognising that different pieces of data — an email here, a device ID there — belong to the same real person.
Match rate
The percentage of your customer records that a platform successfully recognises and connects to a real user it knows.
Lookalike audience
A group of new prospects who share characteristics with your existing best customers, generated by a similarity model.
Propensity modelling
Scoring individual customers by how likely they are to take an action — usually converting — based on their behaviour.
Brand lift
The measured change in how people perceive a brand — awareness, consideration, intent — as a result of a campaign.
Footfall attribution
Measuring whether advertising actually increased visits to a physical location, like a store.
Incrementality
The share of results that genuinely happened because of advertising, as opposed to results that would have happened anyway.
Attribution
The practice of assigning credit for a conversion to the marketing touchpoints that led up to it.
Marketing mix modelling
A statistical approach that estimates how much each marketing channel — and other factors like price and seasonality — actually contributed to sales.
Response curves
A chart showing how much extra return you get from a channel as you spend more on it.
Diminishing returns
The principle that each additional dollar spent on a channel tends to generate less incremental return than the dollar before it.
ROAS vs. incremental ROAS
ROAS measures revenue against spend using platform-attributed conversions. Incremental ROAS measures it using only the conversions that genuinely happened because of the advertising.
Control groups
A comparison group that doesn't receive the treatment being studied — like advertising exposure — so its behaviour reveals what would have happened without it.
Correlation vs. causation
Two things happening together doesn't mean one caused the other.
Multi-touch attribution
A family of rules for splitting credit for a sale across every digital touchpoint a customer interacted with — not just the last one.
Customer lifetime value
What a customer is worth over the whole time they buy from you, not just on their first order.
Data clean room
A locked room where two companies can compare notes on their data without either one handing over the raw list.
Holdout group
A group of people deliberately kept from seeing a campaign, so you have something to compare the exposed group against.
Adstock
The idea that an ad's effect doesn't vanish the moment it stops running — it fades out gradually.
Saturation
The point where extra spend on a channel stops meaningfully moving the needle because it's already reaching everyone it's going to reach.
Seed audience
The starting group of known good customers you show a model so it can go find more people like them.
Identity graph
A map connecting the different digital identifiers that all belong to the same real person.
Walled garden
A platform — like Meta, Google, or Amazon — that lets you advertise and measure results inside it, but won't let raw user-level data out.
Triangulating measurement methods
MMM, MTA, and incrementality testing often each give a different answer for how well the same channel performed — triangulating means using all three together instead of trusting whichever number you saw first.