Another way to evaluate the effectiveness of bloggers
Hi, I'm Ivan, I've been working in Influencer Marketing for 6 years in different roles. I designed and implemented blogger marketplace architecture, built processes for Influencer Marketing Agency. I learned how to do 3,000 blogger marketing posts a month, communicate with hundreds of bloggers a day.
I have come across that there is a lot of "magic" in Influencer Marketing that my engineering mindset doesn't allow me to understand. Let's try to structure this "magic".
Advertising with bloggers brings good results, but in this text I will not consider when this advertising should be used and when not. We will analyze the situation when you are already working with bloggers or are ready to start.
How bloggers are usually chosen
You can find tons of articles on the topic of Influencer Marketing on the internet, where you will learn what you need to pay attention to when choosing a blogger:
Select the right blogger topic. Of course, the blog should match the interests of your audience.
Check the price. You should not choose the most expensive bloggers, and you can leave the celebrities for later.
Check the price per view. You can compare bloggers with each other, obviously the cheaper the view, the cheaper the lead. A cheap lead is what we need.
Check bloggers for fake subscribers. Of course, we only want real views, so we'll only hire bloggers without fake followers.
Make sure the blogger's audience is engaged, i.e. the site has a high ER. More engaged subscribers means more sales.
Match the blog with your target audience. Naturally, if your customers are women, then the audience of the blog should be female. The audience of the blog should match your target audience.
The usual algorithm of actions
Let's skip the crucial stage of setting the goals of an advertising campaign and drawing up a client portrait. We assume that you have already done all this and fully know what you want to get as a result. And, of course, you should not forget about this stage.
When you have determined the criteria and goals of the advertising campaign, let's try to make an algorithm of actions:
Choose a blogger who covers the topics we are interested in;
See that the price is not very high. Let's say we decide that one placement should not cost more than €1,000. We want to make several placements. So we will only take a blogger if she costs less than that.
Let's calculate CPV. Let's look at the last post from the blogger, or preferably several, divide the price by the number of views and realize how much a view will cost us, how it compares to market prices;
Let's check for fake subscribers in some service. We won't work with the blogger if the percentage of fake subscribers is high.
Let's calculate ER and take into work sites only with good, let's say, with ER above 3.5%;
Find out the statistics from the blogger or use any blogger analysis service. We will work with a blogger only if she has more than 50% of the audience of the gender we need.
Looks logical, but there seem to be a few problems.
A lot of work. Find a blogger, check ER, write to the blogger and find out the prices, calculate CPV, check the gender of the audience, check that the blog has a real audience.
Each blog has to be analyzed individually, although in fact half of the parameters could well be calculated automatically.
We take into account all the parameters separately, but in fact - they work together.
There is no way to compare two bloggers. A blogger is either suitable or not.
Example
Let's take, for example, a dating service that we need to promote to bloggers. Let's say we need a female audience of 18–35 years old from Brazil.
Interests... What are girls who are looking for new acquaintances interested in? We need to experiment. The goal is to increase the recognition of the service and get registrations of new users.
Let's pretend you've found the following bloggers to advertise with:
| empty | Blogger A. | Blogger B. |
|---|---|---|
| Price | €300 | €200 |
| Average Views | 30,000 | 36,000 |
| ER | 4% | 2% |
| Women | 51% | 80% |
| 18-35 | 30% | 60% |
| Fake subscribers | 15% | 40% |
Which blogger should you order advertising from? The first one seems to fit, but the second one not so much. The second one has a lot of fake subscribers and low ER, most likely he will not be recommended for placement by blogger analysis services.
But there is another view and how things really are?
How to choose differently, how to take everything into account?
Target Audience (TA)
Our task is to choose the blogger who will have the highest percentage of our target audience and at the same time the lowest price.
Let's try to calculate the percentage of the target audience. In our case, it can be calculated as follows:
%TA (percentage of target audience) = percentage of women * percentage of audience aged 18-35 * number of "live" subscribers.
%TA(Blogger A) = 0.51 * 0.3 * (1 - 0.15) = 13%.
%TA(Blogger B) = 0.8 * 0.6 * (1 - 0.4) = 28.8%.
It seems that now blogger B is not so bad, he has more than twice the share of our target audience than blogger A.
And how much is that in absolute numbers?
TA = %TA * number of views;
TA(Blogger A) = 0.13 * 30,000 = 3,901.
TA(Blogger B) = 0.288 * 36,000 = 10,368.
The gap is crushing, up to this point, one would have thought that Blogger B is not worth choosing, but it turns out that he is 3 times better than Blogger A.
Cost per view of your target audience (CPV(TA))
The result is not bad, but we didn't take into account the cost of advertising. I suggest using the price per view per representative of your target audience. You need to keep that in mind as well. How to calculate it?
CPV(TA) = ad price / TA
CPV(TA) (Blogger A) = €300 / 3,901 = €0.076
CPV(TA) (Blogger B) = €200 / 10,368 = €0.019
The calculations clearly show that now the difference is 4 times, Blogger B has become even more effective.
Active target audience
We did not want to take blogger B also because he has a low ER. This indicator was not taken into account at all. So what should we do in this case? In fact, at least partially, we have already taken it into account when we counted only real subscribers.
But let's make more precise calculations with the ER index. We'll get a slightly synthetic index, which we don't need for anything except for comparing bloggers with each other, but it helps us to take everything into account at once and compare bloggers with each other more objectively.
Let's find out how many people from our target audience will rate a post or write a comment.
ATA (active target audience) = TA * ER
ATA (Blogger A) = 3,901 * 0.04 = 156
ATA (Blogger B) = 10,368 * 0.02 = 207
Blogger B wins, but the gap has narrowed.
Cost per view of your active target audience (CPV(ATA))
Let's multiply everything already and find out the general indicator that will help to choose the best blogger.
CPV(ATA) = CPV(TA) / ER = ad price / ATA
CPV(ATA) (Blogger A) = €300 / 156 = €1.92
CPV(ATA) (Blogger B) = €200 / 207 = €0.96
It turns out that blogger B is much more efficient. Of the two given options, it is better to choose him/her, despite the fake subscribers that may have been at the beginning of the blogger's career and the consequent low ER
Can it be done better?
In the end, we settled on choosing blogger B, which has a target audience share (%TA) = 28.8%
This is an indicator that can often be calculated without laborious communication with the blogger. But if it's a good result, how can you tell?
The smaller your target audience is - the smaller share you can expect from the blogger, obviously. Which means you can only understand if it's a good result by comparing %TA from different bloggers. You just need to choose the best one according to this indicator.
We constantly update blogger statistics with our analysis tools and social media data.
To select the best bloggers, we sort all of them by the share of the desired target audience and start working with the best ones.
Thus, the advantage of using specialized tools to compare the %TA parameter is that you can quickly get bloggers who will be more effective, and even our blogger B would not be in the top 100.
Why are there so many formulas?
It might be worth limiting ourselves to one metric and evaluating everything against it. For example CPV(ATA), but, unfortunately, we don't always have all the data.
At the first stage, %TA is a good way to cut off those bloggers who are not worth cooperating with. And proceed to the labor-intensive stage of communication only with those bloggers who fit the external analytical data.
Conclusion
I hope I was able to clearly show that a blogger with fake subscribers is not always a bad thing. It is not always effective to throw out subjectively “bad” bloggers without calculations. An analytical approach can greatly reduce labor costs and improve ROMI. A good blogger is the one who best suits your target audience. His indicators are relative qualities; you cannot evaluate a blogger without taking into account the tasks that you set for him and without comparison with other bloggers.
Selecting a blogger is only the first stage, but using this approach allows you to save a lot of money and time.
Thank you for attention.
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