How to make effective marketing dashboards and reports

Everyone could benefit from paying more attention. Five years, countless ciders and too many lectures in my career, I finally spent time on that.

Bad reports take up as much time as pointless meetings. Analysts spend hours creating reports that no one will read or creating dashboards that will never be seen. Bad reports mean that people focus on the wrong goals or choose the right goals, but they choose the wrong way to measure them. Either way, you end up in the same place.

Who is this data for?

This context will inform many of our decisions. We care about our audience, because everyone knows and cares about very different things.

A C-level executive doesn't care about keyword cannibalization, but you probably care about overall marketing performance. An SEO manager, on the other hand, you probably worry about the number of indexed pages and keyword cannibalization, but is less bothered by overall marketing performance.

If someone tells you that the report is for audiences with obviously different decision levels, then you will almost always end up creating something that will not meet the goals we mentioned above. Divide your reports into reports / individual panels for each audience, or will it be set aside and ignored.

Educate your audience

Asking them is particularly important, because you don't just need understand your audience, it must also educate them. To come back to myself, in fact, there are CEOs who will be concerned with specific keywords.

The problem is that they shouldn't. And if you can't convince them to stop worrying about that metric, your incentives will be wrong and search success will be more difficult. So ask. Persuading them to stop using the wrong metrics is, Of course, another article in itself.

To continue that point, Now is also the time to reach an initial agreement that these reporting dashboards will be the ones used to measure performance..

That way, when they send you an email for three months asking how you're doing with the keyword x, it is covered.

How to choose good panel metrics

This is the hard part. We are defining our goal by the metrics we choose to measure it.

A good metric is usually a direct measure of success. Ideally, you should not have warnings that are beyond your control.

No warnings? Ask yourself how you would explain if the number went down.. If you can immediately find excuses that can be answered for things that are out of your control, then you should try to refine this metric. (Do not worry, there is an example in the next section).

We must also make sure that will create incentives for people's behavior.

Unlike a report, to be used to help us make a decision, a board shows the objectives that interest us. It is a subtle distinction, but important. A report will help you make a single decision. A dashboard and the KPIs it displays will define the decisions and reports you create and the ideas people have. It will establish incentives and change the behavior of people who work outside it. Choose carefully. Avinash has my back here; go read his excellent article on how to choose KPI

You may need to compromise your metric depending on resources

What we just talked about is an ideal. Fit for industry, for example, it is usually quite difficult; you may have to settle for showing Google trends for some popular terms in a second chart, or show Hitwise industry data on another graph.

Be careful if you find yourself adding more than one or two additional metrics. When it reaches three or four, information becomes difficult to analyze at a glance.

What about incentives? The metric we established will incentivize our team to get more traffic, but it has no quality control.

Both metrics sound like a mouthful. That's because they have gone through a process similar to the one we mentioned earlier.. We could have started with income attributed to search before, then be more specific

The job of a board is to track a goal over time and tell if more research is needed or not.

How to create a good report

A report should be able to help us make a decision. Choosing the objective for a board is usually quite simple.. Choosing the decision that our report is helping us make is usually a little more difficult.. The most important, We have to decide.

If you don't have a decision in mind, if you're just creating a report to dig deeper, is wasting time. Don't make a report.

If the decision is to prioritize next month, then you might have a research report designed to help you prioritize. But the aim of the report is not to deepen, is to help you make a decision. This is mainly a state of mind, but I think it's crucial

Why is it important CLV?

create a specification for a report

Are we happy with this decision? In this case, it was not. Experience has taught me that SEO rarely runs week to week; One thing that our SEO split testing platform has taught us over and over again is that even the obvious improvements can take three to four weeks to generate significant traffic change..

now we are happy with our decision, so let's start listing the possible factors. For the sake of brevity, I'm only going to include three here:

  • Classification of individual keywords
  • Individual keyword clicks
  • Number of indexed pages

Report layout and design

Once again, our design must be suitable for the objective we are trying to achieve, which gives us a couple of principles to follow:

  • It's totally fine that the reports are great, as long as they're sorted by the odds that someone's opinion will be changed by the decision. Complexity is fine as long as it's accompanied by depth and you don't get it all at once.
  • At a similar point, you will often have to break down the metrics into multiple charts. Be sure to sort them by importance so someone can stop digging when they're happy.

Create an effective chart

Charts themselves are crucial elements of a report and dashboard. People have built entire careers by helping people visualize data in graphs. Instead of reinventing the wheel, The following resources have helped me avoid the worst when it comes to graphics..

Both the n. ° 1 like the n. ° 2 below don't focus on doing nice things, but in the objective of a graph: allow you to process data as quickly as possible.

  1. Do's and Don'ts for Effective Graphics
  2. Karl Broman on how to misrepresent the data
  3. Dark Horse Analytics: data looks better naked
  4. Additional geek feature: creating style charts 538 with matplotlib


Yes, we should include keyword rankings, but they must be grouped and, ideally, they must also be classified with and without Google functions. We also want to avoid range average, lose subtlety in how our keywords move with each other. This sample STAT chart illustrates this well:


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