When you have a new project in your hands, one of the most important processes to identify the content to create is the correct identification of buyers, or search, personas. For those who don’t know it, buyer personas are nothing more than a faithful portrait of the user we want to address. They represent, therefore, a potential client in case we should develop an e-commerce or, more generally, a hypothetical reader of our blog or news site.
The buyer personas do not indicate us in an approximate way if we are talking about a male or a female, if our consumer is a teenager or an adult – but they also describe the needs, aspirations, character of the recipient of our services. So let’s see, step by step, how to create them starting from the analysis of research related to our communication objective.
It often happens that we have to approach a new job without having a previous basic, if not very sketchy, knowledge of what we need to promote. So before any other activity, it can be helpful to start your work by searching for information as any other person would: writing what you are looking for on google.
Let’s suppose, for example, to have in our hands an online grocery store that wants to push its own section on sweets, but we just never liked jams and chocolates. However, we know that we are strange and that chocolate can be a great product to start with to create new content and intercept new possible customers. Let’s start to get an idea: what is proposed in the SERP, if I write “chocolate?” Obviously our competitors (but this is another story) and already some other interesting input: chocolate and health, chocolate salami, whole weeks dedicated to chocolate. In 2 minutes we have already understood what could be three macro areas for our new contents: properties, recipes and events.
Obviously, it’s just a first small step. At this point, the main keyword needs to be expanded, so that we can broaden our knowledge about what a user is looking for and how to satisfy them. After all, “query” means nothing more than “question”: our task is to give the best answer. So where to find the questions? Let’s start with the tools tool: AdWords Keywordplanner, or “Keyword Planning Tool”.
From here it will be possible to extract a first file with a list of related keywords, such as a type of chocolate “dark chocolate”, searches related to “chocolate recipes”, and some already more specific as “handmade chocolate”. At this point, the questions with the highest volume should be further expanded. Let’s start with the more generic one, “chocolate”, and let’s help ourselves with another instrument dear and known to all of us, Ubersuggest.
Each keyword entered can still be enlarged, like the “dark chocolate” you see in the image. Expanding this type of “question”, Ubersuggest already gives us back a first list from which we can begin to understand the recurring themes related to the keyword from which we started: the properties related to health (“dark chocolate and cholesterol”, “dark chocolate and diet”) but also new information such as research related to line maintenance (“dark chocolate calories”, “dark chocolate diet”) and a last one related to the percentages of cocoa present (“dark chocolate 100”). With those from which we started, we already know that we have these types of needs on the part of a user: to know more about the properties of chocolate, how much it affects the line, ideas for recipes, percentage of cocoa, events and events related to the product. Not bad for not having gone into detail yet!
At this point, the game is to copy the queries from Ubersugget and upload them, via .csv files, to Keywordplanner in order to extract the search volumes and again start to increase, expanding them, the number of keywords related to our main question and record everything in an excel sheet. A mechanism that can take us a long time, but that allows us to have a broad and detailed picture at the same time of what may be the needs of a user to answer. After this first step, however, it is necessary to put some order to this long list of keywords.
At this point you have collected (a lot of) information and expanded, until your eyes cross, different types of keywords, and you already have an idea of what could be the main clusters for these keywords. You can, at this point, organise your data in a mental map by deciding which sets to create: for example, as we have seen, all the searches related to properties, recipes, events.
Clearly such an approach will give you a very precise vision, and above all far from mistakes related to words that can be linked to several concepts, since no tool other than our brain will have the full mastery of the meaning of a keyword. However, to speed things up, two very interesting tools come to us. The first one is Wikimindmap.org which, exploiting the organisation of the contents on wikipedia for a type of query, returns you a first mental map:
The second one, unfortunately still not present in Italian at the moment, is Answerthepublic, which combines the query of your interest with all types of adverb questions (such as “what”, “how”, “why”) and with different types of prepositions (such as “to”, “with”, “like”). The result is here:
At this point, you have catalogued your questions according to clusters that are neither too general nor too narrow. However, in order to better understand the questions, and consequently better organise our answers, we can’t stop at keywords and their sets, but we will have to try to understand who is behind that search and what is the full question, in order to better understand how to create our buyer personas. First of all, forums or other types of communities come to our aid. For example, for the search “chocolate and cholesterol” a user asks if it is true that:
dark chocolate contains no cholesterol? Then, every once in a while, can I eat some or make sweets out of it?
From here we can already guess a certain confusion: cholesterol is not found in chocolate, but in the blood! For health-related research, therefore, we must not take anything for granted when we go to create our content. Our dear old Yahoo Answer also helps us, with questions related to different types of chocolate and their effect on health, and Quora, which although its main language is English, is the perfect source to find just the question you were looking for. The user here wonders how a food with such a high fat content can be good for health and even lower cholesterol.
Here we are at the final stage: creating the person behind these questions. The social networks in this case come to our aid, allowing us at no cost to collect statistics and more detailed ideas about the user for whom we want to create a content, or to whom we want to offer a service.
Facebook Audience Insight is a completely free tool that helps us collect valuable information about our target audience. The only thing you need is a Facebook account, so you don’t need to manage either a page or an advertising account. The first thing we are asked is if we want to analyse the entire Facebook audience or the one related to one of our pages. Choosing the first option, we then have this screen:
At this point we can add the interests of our hypothetical person, leaving everything else at least for the moment without specifications – so that Facebook gives us a first type of audience. In this case, following the example of chocolate linked to health topics such as cholesterol, we will then (in addition to the country of reference, Italy) include interests such as “Chocolate, “Nutrition”, and “Healty Life” as additional entries. In the meantime, we discover that most of them are women, between 25-34 years old, married, graduated, with a good job position. We can then scroll the tabs of Location, discovering that they live mainly in Rome or Naples, the Activity page that shows us how they mainly use mobile devices with Android.
There are already many elements, but we want to make our model much closer to a real person. How to do it? We are helped by the “Page Likes” tab that shows us which pages our target audience follows. We can simply take note of them, to recreate our user’s range of passions, or we can go into more detail. If we set our Facebook in English, it is possible to see which profiles like a certain page (the ones we find in the “Page Likes” tab), thanks to Facebook Graph Search. Clearly we could never use a particular profile for our buyer person for privacy issues, but we can better understand what the interests, the type of work, the way our target audience is and finally create a detailed model, which is the result of the union of several profiles.
Linkedin helps us in the same way as Facebook, especially to create B2B buyer personas. Also in this case with a bit of research it will be possible to reconstruct related interests of our profile.
In order to collect all the information collected up to here, we can create a template in which we can insert it or we can rely on who the buyer personas invented it, that is Hubspot. It is a fairly detailed template, which would perhaps require the use of broader market research or at least the use of surveys offered by Survey Monkey, for example, but it should certainly be taken into account.
In conclusion, we have seen that for one of the most important clusters regarding the “chocolate” query, i.e. the one related to health and cholesterol in particular, a hypothetical buyer person can be the following: