You will be able to test any metric by asking what you would do differently if it went up or down.
A junior marketer at a Singapore furniture shop sends the owner a report every month. It has follower counts for three platforms, total website visits, impressions for every ad, and an email open rate, and most of those numbers go up. The owner reads it, says thanks, and has no idea whether to spend more on Instagram or less, whether the new landing page helped, or why sales were flat while every chart climbed.
The report is not wrong. It is just full of numbers that do not connect to any decision. That is the most common problem in marketing analytics, and no tool fixes it, because it happens before anyone opens a tool.
Here is the test this course will keep coming back to: a useful metric is one where, if it moved, you would do something different. Ask it of each number in that report. If Instagram followers rose by 200, what would the owner change? Probably nothing. If enquiries from the website fell by a third, the owner would want to know why that week, so they would check the contact form and look at what changed on the site. Enquiries drive action, while followers mostly make people feel good.
Numbers that rise easily and change nothing are often called vanity metrics. Followers, impressions, page views and total email subscribers are the usual suspects. They are not useless in every case. Impressions matter if you are testing whether a new ad gets shown at all. Followers matter if you sell sponsored posts. The same number can be vanity in one business and essential in another, so the question to ask is always what decision it serves.
So start from the decision. The furniture shop owner has a few real ones each month. Where should the next S$2,000 of ad money go? Is the new sofa range worth promoting harder? Should they keep paying the freelancer who runs the newsletter? Each of those decisions points to a small set of numbers: sales or enquiries by channel, what each channel costs, and whether customers from that channel come back.
Write the decision first, then the number, then the threshold that would trigger action. For example: if the cost of each sofa sale from Meta ads rises above what one sale earns us in margin, we cut that budget and move it to search. Written that way, the number earns its place on the report because someone will act on it, rather than because the tool happens to show it by default.
Doing this also tells you what you need to track, which is the point of the next few modules. If the decision is about enquiries by channel, you need every link tagged so you know where visitors came from, and you need the enquiry form counted as a key event in GA4. If the decision is about whether customers come back, you need data from the shop's sales system as well as the website. Once the decisions are clear, choosing and setting up the tools gets much easier.
There is a cost to tracking too much. Every extra number on a report takes attention from the ones that matter, and a dashboard with forty charts usually gets opened once and ignored. Three questions answered well will get read every month, while a page that shows everything gets skimmed.
None of this means ignoring the rest of the data. GA4 will still collect it, and you can dig into it when a question comes up. It just means the numbers you report, review and set targets for are the few that connect to money and decisions.
By the end of this module you will have a one-page measurement plan with your goals, one main number for each, the actions on your website that count as success, and where each number comes from, and every later module builds on that plan.
Your task: list every number you currently look at or report, from any tool. Next to each one, write the decision it informs and what change would make you act. Cross out every number where you cannot fill in both.
List every number you currently report or look at, and next to each write the decision it informs, or cross it out if there is none.
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