A healthy brand is visible, understandable, and trusted. Measuring brand health is not secret math — it’s a simple ritual of looking at a few smart numbers, asking clear questions, and running small, focused experiments to learn faster. Below I walk you through a monthly brand health routine, explain what to track and why, and translate experiment planning into plain language you can use without a PhD.
If you want to reference the campaign emblem used in our examples, the file is available at this path: /mnt/data/A_branding_emblem_displays_the_word_”ENTREPRENEURS.png.
The monthly brand health ritual — what it actually looks like
Picture the first team check-in each month. The group opens one dashboard and moves through three steps: check awareness, diagnose activation, and watch retention. Awareness tells you if people are seeing the brand. Activation tells you if they act when they see it. Retention tells you if they come back.
When something moves — up or down — the team picks one reason to test. The test is small, measurable, and designed to teach. After a few tests the team learns what messaging or experience actually changes behavior.
The three pillars to measure (and why each matters)
Pick one clear metric from each pillar and use them as your compass.
Awareness
Why it matters: people can’t buy from you if they don’t know you exist.
What to watch: impressions and reach, direct traffic growth, and simple brand-lift indicators (did people recall seeing your ad or message?). You don’t need fancy surveys to start — track volume in searches, direct visits, and spikes in social mentions.
Activation
Why it matters: awareness without action is noise.
What to watch: click rates on campaign links, landing-to-add-to-cart conversion, and checkout starts. These show if your creative and landing experience are doing their job.
Retention
Why it matters: returning customers sharpen unit economics and long-term value.
What to watch: repeat purchase rate, simple cohort revenue curves, and subscription churn if you have recurring products.
If you only track three numbers each month, choose one from each pillar and tell a short story about why they moved.
Building a simple brand dashboard (what belongs where)
You don’t need a heavy BI stack to be useful — but you do need one clear visual that answers: what changed, why, and what we’ll try next.
Top of the dashboard — executive snapshot
A handful of headline numbers: reach, new customers this month, average CAC, 30-day repeat rate. These are the signals to start your conversation.
Awareness panel
Show impressions by channel, direct vs referral split, and any brand-lift notes you have. Annotate big campaigns so spikes make sense.
Activation funnel
Show the key steps a visitor takes: view → click → add-to-cart → checkout. Display conversion rates between steps and a simple line for CPR or CAC.
Cohort and retention view
Show cohorts (by month of acquisition) and how they spend over time. A heatmap or simple table works — you want to see whether cohorts are improving or degrading.
Experiment log
Track active tests, their hypothesis in a sentence, and the decision owners made from results. This keeps experiments practical and accountable.
Tools like Looker Studio, Metabase, or even Google Sheets work here. The point is clarity, not complexity.
Cohorts explained in plain language
A cohort is just a group of customers who started in the same time window. They help you see patterns that averages hide.
How to think about cohorts
- Make cohorts by the month someone first bought.
- Watch how much each cohort spends after 7, 30, 60, and 90 days.
- Compare cohorts to see whether new campaigns are bringing better customers or worse ones.
Why cohorts matter
If one month’s customers come back more often than another month’s, you didn’t get lucky — something in messaging, targeting, or product changed. Cohorts tell the story.
Experiment design — the simple, practical approach
Experiments are how you turn questions into knowledge. You don’t need fancy math to run useful tests — you need clarity and control.
How to choose a test
- Pick one clear question. Example: “If we change the headline on the landing page, will more people add to cart?”
- Make that question measurable with one primary metric (e.g., add-to-cart rate). Secondary metrics can be things like time-on-page or bounce rate.
How to keep tests clean
- Change only one thing at a time when possible. If you change headline and hero image together, you won’t know which one moved the needle.
- Run the test long enough to see a pattern — not hours, a couple of weeks is common depending on traffic.
- Decide in advance what “good” looks like so you can act quickly (for example, a 5% relative lift, or a clear cost-per-acquisition improvement).
What to do with results
- If the test wins, roll it out and consider follow-up tests to scale the idea.
- If it loses, learn why. Sometimes a loss teaches more than a win. Document the learning and move on.
Experiments are a learning rhythm — small bets, fast feedback, repeated over time.
The 90-day testing rhythm — explained, not templated
Instead of a rigid template, think of a 90-day rhythm as three linked cycles: discover, validate, and scale.
Month 1 — discover
Run hypothesis-driven tests that probe the biggest friction points: homepage clarity, headline, or checkout field. These tests are exploratory.
Month 2 — validate
Take what worked in month 1 and validate it in different channels or audiences. A homepage winner should be tested in paid social ads, for example, to see if CPR holds.
Month 3 — scale & lock
Scale the validated creative and operational changes. At the same time, run a retention-focused test that aims to turn a slight increase into a structural improvement (welcome flow timing, early reorder incentives).
Across the 90 days, keep an experiment log, note resource needs, and prioritize the ideas that improve both conversion and retention.
Plain language glossary
- Awareness lift — more people noticing your brand; can be seen in search volume, direct traffic, or simple brand-lift questions.
- Activation — the moment someone does the first meaningful thing (signup, add-to-cart).
- Cohort LTV — how much a group of customers brought in over time, starting from the month they first bought.
- CPR — cost per result; define “result” clearly for each test (click, add-to-cart, purchase).
- CAC — customer acquisition cost; total channel spend divided by new customers in the period.
Add one-line definitions like these to your dashboard for shared language.
Platform notes — choosing tools without overcomplicating
- GA4 — good for tracking events across web and app; use it for funnel events if you don’t have a product analytics tool.
- Looker Studio — quick to connect and share visual dashboards for stakeholders.
- Mixpanel — strong for product and cohort analytics if you track in-event behavior.
- Metabase — a simple SQL-powered BI for teams that want to run ad-hoc analysis.
- Optimizely / VWO — specialized A/B testing platforms with traffic control and statistical support.
Pick a small stack that serves your core needs: a tracking source, a place to store data, and a visualization layer. Automate daily refreshes so the monthly meeting is about decisions, not data assembly.
Small-team practical checklist (explanatory steps)
- Build a single executive snapshot that shows one metric from awareness, activation, and retention.
- Create month-of-acquisition cohorts and inspect their 30- and 90-day revenue behavior.
- Pick one experiment per month with a clear hypothesis and one primary metric.
- Record every experiment and the decision taken so learnings accumulate.
- Run the monthly ritual: review snapshot, diagnose a problem, decide one test, and assign an owner.
This is about a steady habit rather than heroic analytics.
Final thought and CTA
Measuring brand health is less about being perfect and more about being consistent. The monthly ritual, cohorts, and small experiments create a learning engine. Over time, you’ll trade guesswork for repeatable improvement.
If you want help building your dashboard or running a hands-on brand metrics workshop, join the LiLA community for conversations and templates, or book LiLA Studios for a facilitated brand metrics session. We’ll help set up a simple dashboard in Looker Studio or Metabase, explain cohort queries, and coach your team through the first three experiments so the practice sticks.
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