Micro-influencers can be a valuable part of the techniques used to reach a brand’s target audience, boost brand awareness, and increase conversions. However, it’s easy to waste time and money by choosing irrelevant micro-influencers that have little impact on your business or even harm it.
Micro-influencers can be a valuable part of the techniques used to reach a brand’s target audience, boost brand awareness, and increase conversions. However, it’s easy to waste time and money by choosing irrelevant micro-influencers that have little impact on your business or even harm it.
So, what’s the solution? Enter, machine learning models.
Being a branch of AI that uses data to make predictions and decisions, it’s no surprise that machine learning models can solve this problem. These models can be used to identify relevant micro-influencers across social media platforms, such as X (formerly Twitter), who can influence brand perception.
This machine learning identification process can be divided into several key steps: data collection, preprocessing, feature extraction, model building, and evaluation, which we’ll explain further in this article.
So how does one use machine learning models to identify micro-influencers? The first step is to establish criteria. Before identifying micro-influencers, it is essential to establish the criteria they must meet. This could include key characteristics like their engagement rate and location, making them more likely to engage with fans interested in a particular brand.
Clearly defining these criteria will narrow the machine learning model’s search by focusing on the right influencers to improve brand perception.
The next step should be to choose the social media platforms where the brand’s target audience and micro-influencers are most active. Application programming interfaces (APIs), such as Twitter API for X and web scraping tools like ScrapingBee, can be used to collect data and gain useful insights into audience behaviour.
Use these tools to gather posts, user profiles’ engagement metrics, such as shares and comments, and micro-influencer follower counts. The aim should be to collect data that mentions your brand or related keywords to filter relevant profiles.
We then move to data preprocessing and feature extraction. Tools like NLTK, SpaCy, Scikit-learn, Pandas, and NetworkX can then clean the collected text data by removing noise like special characters and stop words. These handy tools can filter out irrelevant users like spam accounts by checking specific criteria, including activity levels, engagement rates, and follower counts.
Apply sentiment analysis to the posts you have collected to assess brand perception. This should help you understand customer sentiment toward brand mentions. After extracting engagement metrics, extract specific features from user posts. These include text features like word embeddings, hashtags, and keywords. Social network graphs can also identify clusters of users with similar interests.
After collecting and extracting relevant features from the raw data the machine learning models have collected, it’s time to build a model of the micro-influencers. Clustering algorithms like K-Means will group users into clusters based on similar features, which can identify micro-influencers from their high engagement.
Additionally, anomaly detection algorithms like Isolation Forest can help build micro-influencer models by identifying users with higher-than-expected engagement relative to their follower count.
The clusters collected by the algorithms can then be evaluated using metrics like silhouette score or the Davies-Bouldin index. This will let you assess their quality and relevance to the brand you’re working with.
Manually review a sample of the cluster’s identified micro-influencers to ensure they align with your brand perception goals. Remember to remove influencers whose profiles’ have posts that could harm your brand perception. It’s integral to only pick those that are right for you.
You’ve made it to the final step. Now all you have to do after creating the model is to implement and monitor it. You can optimize your marketing strategy by tracking the activities and outcomes of micro-influencers, including engagement, conversions, and follower loyalty.
For example, you can run A/B tests with micro-influencers the machine learning models have identified to measure their impact on brand perception. You can use this machine learning to adjust the influencers’ initial assessment criteria, create more relevant content, and find trends in brand perception. Clever, right?
The range of testing and results can be monitored to continuously refine the model and improve the accuracy of future micro-influencer identification. Over time, irrelevant or fake influencers should appear less often.
And that’s a wrap! By following the steps above with the right machine learning tools, you can create a robust and easily adjustable system to identify micro-influencers who can positively impact your brand perception on social media platforms.
Automating the process with these models can drastically reduce the time you have to spend manually sifting through posts on social media sites. Not only can it help save costs overall, but it can also increase accuracy and reliability. Ultimately, these faster and smarter decisions can lead to better results when using influencers to promote your brand.
The writer is a machine learning enthusiast with a masters degree in business analytics from the University of Nottingham.
