Marketing that measures what matters
Vanity metrics, misread A/B tests and attribution each tool counts its own way. What each marketing profile should know about data, the most common mistakes and a 30-day training plan.
The 5 most common mistakes
Marketing has more data than almost any other area. The risk is not a lack of data but reading it badly.
Measuring what is easy, not what matters
Impressions, clicks and followers grow, but nobody knows whether they bring customers.
How to avoid itLink every campaign to a business indicator: qualified leads, sales or cost per customer.
Calling an A/B test too early
A version is declared the winner with little data or after two days.
How to avoid itSet the sample size and duration in advance, and do not stop the test at the first result.
Believing each tool’s attribution
Every platform claims the same sales and the total exceeds reality.
How to avoid itUse one common source (the CRM) and compare channels on the same basis.
Segmenting without knowing whether the data is reliable
Audiences are built on old or incomplete data.
How to avoid itFirst check completeness and last-update date of the fields you use.
Using personal data without thinking about permission
Data is combined or lists are uploaded to tools without checking the legal basis.
How to avoid itKnow which data can be used, for what and with what permission.
Sound familiar?
Measure your team’s level with the free self-assessment: 20 statements scored by dimension.
Download the self-assessment →What each profile should know
The levels come from the data literacy framework: basic, use, analysis and management. Not everyone needs to reach the same one.
| Profile | What they should be able to do | Level |
|---|---|---|
| Campaign specialist | Set up tracking properly, tag campaigns and read the results of each action | Basic / Use |
| Channel manager | Compare channels on the same basis, read A/B tests and optimise with data | Use / Analysis |
| Marketing analyst | Attribution, segmentation and measuring the impact on sales | Analysis |
| Head of marketing | Link spend to business results and set the budget with data | Management |
Your data training, in three steps
Short training using the team’s real data. Each step links to the guide and the template.
Measure the level
Give each profile the free self-assessment and pick the two or three gaps that most affect the area’s work.
Train with your campaigns
Short sessions with real campaigns: which metric matters, how to read an A/B test and how to compare channels.
Make it stick
A common report with a few business indicators, a naming convention and a go-to person.
Frequently asked questions
What is a vanity metric?
A number that grows and looks good but does not show whether the business is improving: followers, impressions or visits unrelated to sales. They are not useless; they just should not drive decisions on their own.
How long should an A/B test run?
Long enough to reach the sample size calculated before starting, and at least one full week. Stopping at the first favourable result is the most common mistake.
Does the team need to know statistics?
The basics: sample size, normal variation and the difference between correlation and cause. Advanced analysis can stay with an analyst.
What does it have to do with the AI Act?
If the team uses generative AI for copy, images or segmentation, Article 4 deals with their AI literacy: knowing what the tool does, its limits and which data must not be entered.
What level of data literacy does your team have?
Free five-minute assessment: your score in the four dimensions and what to work on first.