Both calculations are correct, but I strongly recommend using the average engagement rate per fan. What is the reason for such an absolute statement? Here I'll tell you!

**What is the engagement rate per fan and how is it calculated?**

First, the definitions. We need mental clarification before we begin the overwhelming storm of calculations.

### ENGAGEMENT RATE (AVERAGE OR TOTAL) BY FANS

**Objective**: to know the performance of publications in that community.**Interpretation**: If it is 2% it means that 2% of the community has interacted with the content. If this metric increases, it means that your content is now more attractive to that community.**Comparisons**: it can be compared with the competition, justifying that even if an audience is small, it can be more faithful to the published content.

**Average engagement rate formula per fan:**

Total interactions/number of days in the time range/ followers * 100

**Total engagement rate formula per fan:**

Total interactions/followers * 100

**What is the difference between the average and total engagement rate per fan?**

Apart from the word “medium”, of course; that's how far we've all come.

Looking at the formulas, we found that the difference is that the average engagement rate per fan is divided by the number of days in the analyzed time range, while the total engagement rate per fan is not. If you're putting your hands to your head shouting “but that makes the final data smaller!” I recommend that you set aside the coffee, heat water, and prepare yourself a lime blossom. Ready? We continue.

Let's better exemplify it:

In the first columns you can see the total data and starting from column F, we see the data for the average engagement rate.

In this example, we have taken into account that 25,000 interactions are achieved every month and that followers are increasing by 1,000 each month until reaching 100,000 in the month of December. Both calculations use the same data.

In the case of **Average engagement rate per fan **we divide by the number of days in the time range. In this case, as you are viewing it by month, it is divided by the number of days in that month (column I). When we analyze a year, it is divided by the number of days in that year (row 16); if we want to analyze two years, we would have to divide by the number of total days in those two years.

However, in the calculation of the **Total engagement rate by fans**, we only divide the data of interactions by that of followers, in each of the months (without taking into account the number of days in the time range). In the case of the annual data (row 16), the interactions are summed up and divided by the final number of followers (100,000).

Is it too early or is your brain working a little slower than usual today and you haven't caught on? Don't worry, I have the solution. Open the , duplicate it in your unit and perform the calculations while you reread the section. With your own experience, you can understand it better, I assure you.

**Analysis of the total engagement rate by fans**

In the total engagement rate per fan in the example (which is not divided by the number of days in the time range), we observed that the monthly engagement rate is between 25% and 28%.

The figure is decreasing little by little because month by month you get the same number of interactions, but every month you are having more followers, which is not affecting greater interaction. In this way, the engagement rate is decreasing.

So far the analysis is correct. **The problem comes when we want to compare with other time ranges.**

By not dividing the data by the time range, we could only compare by months. For example, 26.6% in June and 26.3% in July can be compared because June and July have more or less the same number of days. It is true that it is not entirely accurate, since it is not being taken into account whether the interactions of that month were achieved in 30 or 31 days, but it could be analyzed. The same thing if it is done, for example, quarterly: it would give a larger figure but we could compare it with the next quarter.

But, and here's the key! (even if it's approximate). If that range is monthly, we always have to analyze the data monthly, and the same if it is annual, weekly or quarterly. Always with the same one.

**And why is this?**

You're about to discover the key to our analytical adventure. Concentration!

For several reasons:

**The data is not contextualized**

The first and most important is because the data is not contextualized. In other words, it is not being taken into account if the interactions have been achieved in several days, in just one or in a whole year. When it comes to comparing, it's unfair.

This is due to the nature of this engagement rate. The data of interactions is being related to that of followers, but the latter is not directly related to publications (when interactions are). That is why this particular engagement rate must be contextualized with another piece of information.

In the case of another type of engagement rate, for example the engagement rate per impressions, interactions are being related to impressions; and both metrics have to do with publications. In this way, it is not necessary to divide it by the time range, since they are already naturally contextualized.

**2. We have a “learned” engagement rate data and when comparing, it misfits.**

For example: we are looking at the whole of 2021. We have all the data per month in our heads (which are between 25% and 28% in the example). And, when we analyze annually for the report in January 2022, we see that the total engagement rate per fan for the year is 300%, since the total number of interactions for the entire year is taken (which will be much higher than the monthly one) but divided by the last known number of followers (December, in the example).

This figure cannot be compared with the rest of the months of the year, we would have to compare with the previous year (365 days). The main problem is that if the objective of our annual analysis is to see how the year went, this fact, apart from providing nothing, creates confusion.

The same thing happens if we want to analyze in detail. For example, in January we have 28%, and we want to know which week of the month has the highest engagement rate. It will be possible to find it, but the data will be completely different, it will not be possible to compare it with that 28%.

**Analysis of the average engagement rate by fans**

However, if the average engagement rate is found in the calculation, the data is already being contextualized in a number of days. **That's when we can compare with whatever time range we want.**

**In the example we see how the engagement rate is not decreasing month by month, but rather it changes according to the number of days of the engagement rate**. For example, in February, the average engagement rate is higher, since it is divided by a smaller number of days (28): in fewer days, the same number of interactions have been reached, so that month's engagement rate is higher.

When we analyze all of 2021, we will divide by the number of days in the year and find that the engagement rate is 0.82%. With that data, we can now compare with the rest of the months and see that **In January, February and April, the engagement rate exceeded 0.9%, being the months with the best results of the year**. In general, it has been decreasing because in January with 89,000 followers you get the same number of interactions as in December with 100,000.

If, for example, we are now interested in comparing with the last 5 years, we can do so. And so see if that 0.82% of 2021 is higher or lower than previous years. And the same thing on the contrary. **If we want to go deeper into February, which is the month with the best engagement rate of the year, we can see which week or even which day generated the highest engagement rate** and, most importantly, compare that figure with that of the total for the month.

**Conclusions**

Rather, a summary as brief and concise as necessary.

**1) The use of the average engagement rate by fans is always recommended for the following reasons:**

- To facilitate your engagement rate analysis on social networks.
- To be able to compare with different time ranges.
- To improve the accuracy of the data.

**2) You must always understand the data being analyzed:**

- It's important to review engagement rate formulas or other calculated metrics before drawing conclusions with that data.
- Forget the final value and prioritize the value of the metric for your analyses.

**Bonus track**

Are you left wanting more? I bring you a gem: the average engagement rate per fan calculated with the average number of interactions.

Apart from the engagement rate per followers, we could use the average engagement rate per followers. What does this engagement rate measure? Well, it's the same as the engagement rate per followers but, instead of using the interaction data, it uses the average number of interactions per post, further contextualizing the measurement and being even fairer for the comparison between competitors. If you want to know more details about this metric, you're in luck because “engagement rates are my passion”, and