What is marketing analytics?
Every marketing team has more data than it can use. Marketing analytics is how that data turns into decisions: where to spend, what to change, and what to stop.
It covers a wide range, from a weekly dashboard to a Bayesian mix model. The easiest way to make sense of it is by the question being asked. Each question builds on the one before, and each has its own methods. This page groups the glossary that way.
What questions does marketing analytics answer?
1. What happened?
Reporting and monitoring. The work here is getting numbers everyone agrees on, fast enough to act on.
- Semantic layer. Defines each metric once, so every report calculates it the same way.
- Metrics layer. The part of the semantic layer that holds KPI definitions.
- Data freshness. How current the numbers are, and how long they take to arrive.
- Plan vs. actuals. Comparing what was planned with what ran.
- Anomaly detection. Flagging numbers that move outside their normal pattern.
- Self-service analytics. Letting people answer their own questions without filing a request.
2. What caused it?
Attribution, testing, and causal methods. This is where most measurement debates live, because seeing a sale after an ad doesn't mean the ad caused it.
- Marketing attribution. Assigning credit for conversions to the touchpoints before them.
- Multi-touch vs. last-click. How different attribution rules split the credit.
- Incrementality. The results that wouldn't have happened without the marketing.
- Holdout testing. Withholding marketing from a random group to measure its true effect.
- Geo-lift testing. The same idea, run across regions.
- Causal inference. Methods for estimating cause when a clean test isn't possible.
- Marketing mix modeling. Estimating each channel's contribution from aggregate data.
3. What's it worth?
Efficiency and value. The same campaign can look strong or weak depending on whether you measure revenue, profit, or lifetime value.
- ROAS. Revenue per dollar of ad spend, and the breakeven point.
- Marginal ROAS. What the next dollar returns, which is what budget decisions need.
- Media efficiency ratio. Total revenue over total marketing spend.
- Customer lifetime value. The profit a customer brings over the whole relationship.
- LTV:CAC ratio. Whether acquisition pays for itself, and how fast.
- Brand equity. The value a brand adds beyond the product.
4. What will happen?
Forecasting and modeling. Every forecast is a range, and the most useful ones say how wide it is.
- Marketing forecasting. Predicting sales and results from trend, seasonality, and plans.
- Adstock. How long advertising keeps working after it runs.
- Diminishing returns. Where each extra dollar starts to add less.
- Uncertainty in marketing models. How sure a model is, and how to plan with the range.
- Model drift. Why forecasts lose accuracy as the world changes.
5. What should we do?
Planning and decisions. The earlier answers only matter if they change where money and effort go.
- Media mix optimization. Finding the budget split that does the most.
- Marketing simulation. Testing what-if scenarios before money moves.
- Budget pacing. Keeping spend on plan through the flight.
- Decision intelligence. Connecting what the data shows to the decision in front of the team.
What is an example of marketing analytics?
Following a sales dip through all five questions
A retailer's online sales fall 9% in a month. Reporting confirms the drop is real and not a broken feed. Attribution shows paid social conversions down most, and a holdout test from the quarter before shows social was driving real incremental sales.
Value analysis shows social customers are worth more over time than the average. The forecast says the dip will deepen without a change. The team runs a simulation, moves budget back toward social's efficient range, and watches plan against actuals weekly.
What are the core marketing metrics?
| Metric | Formula | What it tells you |
|---|---|---|
| Click-through rate (CTR) | Clicks / impressions | Whether an ad earns attention |
| Conversion rate | Conversions / visits or clicks | Whether the next step works |
| Cost per click (CPC) | Spend / clicks | What attention costs |
| Cost per acquisition (CPA) | Spend / conversions | What a result costs |
| Average order value (AOV) | Revenue / orders | How much each sale is worth |
| Return on ad spend (ROAS) | Attributed revenue / ad spend | Revenue returned per ad dollar |
These describe what happened. On their own they can't say what marketing caused, which is why the later questions need tests and models.
Why does marketing analytics matter?
Marketing is usually one of a company's largest discretionary costs, and the hardest to prove. Analytics is how it earns its budget, and how a team finds out which half of the spend is working.
- Shared numbers. Teams stop arguing about whose figure is right and start arguing about what to do.
- Better calls. Budgets follow evidence of what marketing caused, rather than what it was near.
- A common language with finance. Value, payback, and ranges are terms finance already trusts.
How does Alli do this?
Alli by PMG's Semantic Layer defines every KPI once, from ROAS to cost per lead, and every dashboard, Data Explorer view, Ask Alli answer, alert, and planning tool in Alli reads from those same definitions, so the numbers agree across teams, regions, and currencies.
From there, teams ask questions in plain English through Ask Alli and dig deeper in Alli Data Explorer, catch unusual movements with Alli Alerts, and test budget scenarios against sales and brand signals in Alli Scenario Planner before money moves.
Explore Alli Data ExplorerMarketing analytics FAQ
What does a marketing analyst do?
Turns marketing data into decisions: building reports, explaining changes in performance, measuring what campaigns caused, and recommending where to spend next.
What is the difference between marketing analytics and marketing attribution?
Attribution is one part of marketing analytics. It assigns credit for conversions. Marketing analytics also covers reporting, testing, forecasting, valuing customers, and planning budgets.
What are the most important marketing metrics?
It depends on the decision. For efficiency, ROAS and cost per acquisition. For growth, new customers and lifetime value. For brand, share of search and brand tracking. Agree on definitions before comparing any of them.
What is marketing science?
The more statistical end of marketing analytics: mix models, experiments, causal inference, and forecasting, used to estimate what marketing actually caused and what it will do next.
Last updated Back to the glossary