Very often when you enter the analysis phase for the creation of a site or platform, you come across the concept of Buyer Persona.
In the web there are several valid definitions but personally the one I prefer is the following:
People are “invented” users used to represent needs and/or characteristics of a certain class of real users.
From this definition we understand that people are “ideal” users who are used to represent a category of users with similar characteristics and needs.
A practical example could be the following:
This Buyer Persona, as explained in the definition, is not a real person but an ideal person, i.e. a typical user representing a certain category of real users.
Specifically Lorenzo represents that male audience, aged between 26 and 34 years, boyfriend, who has no qualifications beyond the diploma and who has shown interest in sports and motor sports.
The question that therefore arises spontaneously is: “How do I create a relatively detailed buyer person like the one in the example?
Unfortunately, there is not a single infallible method to perform this operation, on the contrary, online there are several articles that explain how to identify these Buyer Person using the most diverse methods. The really problematic thing is that the proposed methods are almost all valid, the only difference is the time factor.
Often when we are in the analysis of the project the time we can dedicate to this phase is limited (because of the design and planning phases that usually take up the bulk of the time available) and often it is not easy to get good results with the little time available.
What I’m going to propose today is a “tendentially” quick method (after you’ll understand why I used this word) to get acceptable Buyer Persons to continue the project.
The method I’m going to explain to you uses the free tool called Facebook Audience Insights.
Essentially it is the tool used by Facebook to create sponsored campaigns, the advantage of this tool is that it is quickly accessible to everyone and requires nothing more than a simple registration on Facebook.
It’s not a universal method, much less the best, it’s simply one of the present methods I’ve experimented with recently and for certain projects it can be considered a more than valid method. So now enough theory and let’s move on to practice.
The objective of this phase is to go and analyse users interested in our sector and once analysed draw conclusions about the target that is actually of interest to us or that has shown interest in our sector. In order to continue, however, we need a specific case study so that we can analyse real data and draw conclusions about them.
Let’s take for example the case of a project for a website, in particular a blog, dedicated to the saga “The Iron Throne”.
Since this is a project for the realisation of a site we need to identify the BUYER person to analyse the needs of these users and then deduce a behaviour within the site, which will then lead to the realisation of a structure of the site based on this information.
Before starting the analysis we need to define the age groups and the country we are going to consider. In particular for this analysis we will consider only the Italian users interested in the series and the age groups analysed will be the following:
18 – 25 | 26 – 34 | 35 – 44 | 45 – 54 | 55+
So let’s start with our analysis on Facebook Audience Insights.
In the screenshot that will appear to us we will then have to consider the following parameters by setting them to these values:
We defined in the previous phase which are the age groups, the country and the topic considered now is the time to collect the data and organise them within a table, I suggest you to set it as follows:
|Country||Gender||Age||Interest||Tot. Interests||Tot. Facebook||% Tot|
Once set the document we will fill the various lines with the data present inside the instrument, in particular we are interested in the average between the two values present in the main screen under “New Public”. For each age group we will fill in a new line by entering all the required data in the table.
Collected all the data we will have as a result a table with 5 columns filled while the last 2 remain white. To fill these two columns we have to repeat the same procedure used to fill the first columns only in this case we will remove the interest. In this way the tool will show us the total number of active users on Facebook for that specific age group.
Once inserted this data we can finally find out of all Facebook users, of a certain age group, what is the percentage interested in the throne of swords.
You then get a table of this type:
|Country||Gender||Age||Interest||Tot. Interest||Tot. Facebook||% Tot|
|Italy||Male||18 – 24||GOT||175000||2750000||6,36%|
|Italy||Female||18 – 24||GOT||175000||2250000||7,78%|
|Italy||Male||25 – 34||GOT||275000||3250000||8,46%|
|Italy||Female||26 – 34||GOT||175000||2750000||6,36%|
|Italy||Male||35 – 44||GOT||125000||3250000||3,85%|
|Italy||Female||36 – 44||GOT||125000||3250000||3,85%|
|Italy||Male||44 – 54||GOT||17500||2750000||0,64%|
|Italy||Female||45 – 54||GOT||17500||2750000||0,64%|
At this point all that remains for us to do is to analyse the data obtained by looking within the table at which are the largest % and to which classes they belong.
As you can see from the table using only one country, one interest and 5 age groups we have come to a table with 10 rows. In some cases you can easily reach 100 rows if you consider more countries, different sentimental situations and narrower range age groups, so for this reason I defined the method ” tendentially” fast, because conceptually it is a fast process but it depends on the amount of data you want to analyse.
Going back to our case we will go to analyse the data obtained by observing inside the table which are the major % and which classes they belong to. In particular, we will identify the largest percentage for men and the largest percentage for women. At this stage, if you have more data available (such as data from other countries) you can go to make a comparison of the same age group in different countries. For simplicity I will go to consider only the higher percentage for each gender in order to get 2 buyer person. In our case the higher percentages are:
Man > 25 – 34 with a score of 8.46%.
Woman > 18 – 24 with a score of 7.78%.
We have therefore defined for each gender what age group we are interested in, now we have to go and structure the Buyer real persona.
The objective of this phase will therefore be to go into more detail about the most interested age groups that have emerged and create “ideal” users based on this information. The process that we are going to apply is the same whether it is 1 Buyer Persona or 100, so I will show you only for one of the two cases.
Female audience, age 18 – 24
Let’s reopen Facebook Audience Insights and choose an age range in the emerged age group. Let’s take for example the average value of the range, 21 years old.
At this point we set the values again in the instrument and as an age we put 21 years old.
The first thing that jumps out at us is that by focusing on just one age value we have gone from an average of 175000 users to an average of 27500, this means that 15% of all women in that age group are 21, but we keep looking.
In order to speed up this process it’s better to choose from the beginning which data we want to analyse, in order to aim directly at the data omitting the rest, the Buyer Person scheme we saw at the beginning is useful in this phase (in this phase we can use a more or less detailed scheme depending on the project).
So let us fill in the various fields, leaving aside the name and demographic data that we already know, let us go and analyse the profession and the level of education. Going down slightly inside the screen of the tool we find a section called “Education level”, as you can see in this case the highest percentage is occupied by female university students, so we will take this data.
To go now to locate the profession you must first click on the middle column of the graph “education level”, in this way of the 27500 users that emerged we will select only those university students (which then represent the person we are creating). As you will notice once clicked, the number of total users will increase to 17500, or 63% of total users, women and 21 years old.
Going down again we will notice the section “Professional Title”. As you can see there are many very different options and choosing one based on the percentage could be one of the methods to select a profession. There is, however, a better method to skim them, going back slightly above in the screenshot we will see the “Sentimental Situation” graph. As you can see from the graph, the largest percentage is occupied by “single” users who occupy 53% of the total. If we click on the column representing the acronyms, the tool will then select all those female users, 21 years old, interested in the throne of swords and singles. If we go back now to the “Professional Title” section, we notice that only one item is left, namely “Art, Entertainment, Sports and Media”. Knowing that these are university students, we can consider a degree in communication sciences.
We have filled our first fields, but now we must move on to analyse the interests. On the main screen we go to the “Like on page” tab. In this section we will show the most popular pages, as you can see we have a choice. In this phase we have to analyse the interests that in our opinion are quite characteristic of the selected age group (useful in this phase to get help from someone who actually belongs to the selected age group), for example we take into consideration:
To analyse the technological profile we move to the “Activities” tab and go to analyse the graph in the “Device User” section. You can immediately notice that the largest percentage is occupied by the column concerning both desktop and mobile. You can also see a clear separation between apple mobile devices and android mobile devices.
At this point we can say that we have collected enough material, all we have to do is to fill in our chart.
As you can see from Facebook Audience Insights real users similar to Linda (ideal user) represent 23% of female users, 21 years old, who show interest in the throne of swords.
The percentage we have obtained, apparently low, can actually be defined as relevant, because as we have noticed the variation revolves around the sentimental situation, which therefore almost never affects the interests. As seen previously going to eliminate the sentimental situation and leaving instead unchanged the other data we obtain a value of 63%, definitely relevant.
As mentioned at the beginning of the explanation this is not the defined method but it is one of the methods present. The data collected are obviously partial since they are only users connected on Facebook who have included these characteristics among their data. The result obtained is therefore not a precise estimate but can be a basis for considerations. A very important factor is also the sector for which the analysis is being carried out. If we know that the target audience is a user who on average does not use this social network, the estimate has no value as it would only represent a slice of the total audience and would therefore not be characterising. For the case that we have instead examined, since this is an age group that spends most of its time on social networks, we can consider it a fairly representative estimate of the total audience.
Anno 1993. Dopo il diploma presso l’Istituto Tecnico Aeronautico mi iscrivo alla laurea Triennale di Scienze Forestali. Durante il periodo degli studi inizio a coltivare la mia passione per la grafica e dal 2010 inizio a lavorare come Graphic Designer. Nel 2014 ho fondato ByTek Marketing, insieme ad alcuni studenti della facoltà di Ingegneria, dove svolgo il ruolo di responsabile dell’area UX/UI. Nel 2017 ho partecipato ad un Master in UI Designer presso Tag Innovation School. Appassionato di sport, natura, musica, scoutismo e vita all’aria aperta. La cosa che odio di più in assoluto è il Natale.
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