Target audience. Target audience. Target audience. A buzzword that gets thrown around so often that almost everyone in the business of content creation and digital marketing, irrespective of their standing or level of expertise, loosely understands what target audience is. But do you really know the nuances of identifying your target audience? Have you invested in techniques like gender-based target audience segmentation?
If not, it’s probably the reason why you keep asking, “Why don’t I get good responses and traffic to my blogs and articles?” In my experience, gender is a key piece of the puzzle of developing a target audience persona, and by missing out on it, you compromise the potential of your content outreach.
Why Gender-Based Target Audience Segmentation Matters
I wrote a blog sometime back and posted it on social media groups but got a very lukewarm response. In general, my posts drive decent engagement — views, likes, shares, etc. But this time it was different. I didn’t get the response I was expecting.
Any guesses why?
Because I missed my target audience completely. When I did the gender analysis and prediction below, I found that the target audience for that blog was females in the 25–30 age (maybe because I had written about luck/fate), whereas I had assumed it would be more relevant to males in the 35–40 age group. And hence when I posted in social groups with a male-dominated target audience in mind, no one really liked the content.
In general, we write blogs or articles to attract target users and engage them. But if we miss connecting with our target audience through our writing, we won’t get the kind of response we want, no matter how good the blog or article is. That’s where accurate target audience analysis comes into play.
The good thing is that now with technology, we can solve this dilemma or at least get some cue on our target audience and either change our writing to reflect the user base we’re trying to connect with or target more relevant user groups right from the onset.
Role of AI in Gender-Based Target Audience Segmentation
So how do we achieve gender-based target audience segmentation?
We will use the Machine learning NLP model for predicting relevant gender for a particular piece of content, here is the basic flow:
Basic NLP Pipeline
For the purpose of this blog, I’m using the Sklearn library, but you can also explore TensorFlow or others as well. The input will be text-based content.
Let’s take a simple example of the sentence “The Secret To Women’s Empowerment Is Woman”. The sentence is clearly oriented toward females, and as such, we can expect a larger percentage of the target audience to be female, not male.
Here are the steps we follow:
1. Preprocess
In the preprocessing stage, we first clean up (normalize) our input text and dataset for any duplicate, unwanted characters, and convert them to lowercase for uniformity. Since we want to divide it into two categories (male and female), we also check that the dataset we have is uniformly distributed for male/female categorization. This ensures that we have a good dataset for training. Here’s how:
a. Normalize the data: Convert all characters to lower cases, clean up duplicates
b. Remove noise: Stop words, punctuation, and words that are too rare or too common. Stop words are words like “and”, “the”, and “him”, which are presumed to be uninformative in representing the content of a text, and which may be removed to avoid them being construed as a signal for prediction
c. Tokenize: Tokenize input documents (a token is a list of characters separated by a delimiter if the delimiter is space, a token is a word)
d. Data Integrity Validation: Ensure that the dataset we have is uniformly distributed between male and female for more relevant results
2. Vectorize
Vectorization means turning tokens into meaningful numbers. There are many ways to represent tokens in documents. Scikit-learn provides three commonly used transformers or vectors: CountVectorizer, HashVectorizer, and TfidfVectorizer. For more details on each, please go thru Sklearn
a. Associate category name with numerical index. e.g. male = 0, female = 1
b. Transform data into an array in vector form using TfidfVectorizer
c. Import TfidfVectorizer from Sklearn using this command
sklearn.feature_extraction.text import TfidfVectorizer3. Train
a. After cleaning and normalizing, the data set will look like this:
b. We can use any of these models: LogisticRegression, RandomForestClassifier, and MultinomialNB, for our prediction in SKlearn
c. We need to split our dataset to check the accuracy of the model and evaluate which is best. This can be done by splitting the dataset as {X_train, Y_train} which is used for training the model. And {X_test, Y_test} which is used for testing the model. The split can be 80% for training and 20% for testing to ensure maximum accuracy
Evaluate
Once training is successful, we can now evaluate the model using our example sentences to manually check the relevancy. Here are a couple of examples:
Example 1:
Text = The Secret To Women’s Empowerment Is Woman
Predicted_Output = Female 82% , Male 18%
Example 2:
Text = Practice makes men perfect
Predicted_Output = Male 86%, Female 24%
Here is the complete flow which depicts the process:
Here is the sample code for the normalizing, models, training, and dataset examples: https://github.com/callamits/gender_prediction
It’s always best to know the audience you will be targeting through your content, that way you can select the most relevant social media verticals and groups to ensure you get viewership and relevant users engage with your content.
