Social Media Influencers Find Novel Uses For Machine Learning

Here at Smart Data Collective, we talk a lot about the benefits of artificial intelligence in the marketing profession. It is easy to see why this is such a core focus. There are hundreds of applications of big data for marketing.

 Most of our articles on artificial intelligence and big data in the marketing field pertain to the applications in SEO and marketing analytics. However, there are some more specific ways marketers are incorporating big data into their strategies. One of the newest approaches they are taking is leveraging machine learning in their influencer marketing tactics.

Kam Zulawski talked about this in a guest post on MarTech.

“Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) are revolutionizing the way brands conduct Influencer Marketing. AI-powered Influencer Marketing tech is helping brands in three key areas: identifying the right creators, suggesting impactful workflow actions, and creating more relevant content. With the digital universe continuing to expand, the true advantage of Influencer Marketing enhanced by Data Science lies in its ability to consider an inordinate amount of data in each recommendation,” Zulawski writes.

 How machine learning is transforming influencer marketing

The influencer marketing industry is booming. According to one analysis, the demand for influencer marketing services is expected to reach up to $10 billion by the end of this year.

However, many brands struggle to utilize influencer marketing effectively. Smaller companies, in particular, struggle to understand the core benefits of influencer marketing, even as they invest millions of dollars in it. They often focus on choosing influencers with the broadest reach, regardless of their relevance to their target customers.

Companies can realize higher ROIs by taking more data-centric approaches to their influencer campaigns. Marketers get more value from their influencers when they evaluate the right KPIs, which include:

• Customer engagement with various types of content. Experienced marketers understand the importance of monitoring engagement between their followers and their content. Unfortunately, they don’t always take a granular view of their content engagement. They will usually get better traction from their marketing efforts by segmenting different types of content and seeing how various demographics respond to them.
• Correlation between influencer styles and the satisfaction of marketing objectives. There are many different types of influencers on social media. The most successful marketers understand that influencers in similar niches outside their exact sphere of influence can be invaluable branding advocates. A company selling natural remedies might see results working with holistic health experts, low income consumers with no health insurance, survivalists and outdoorsman. They will try testing content with influencers in each of these niches and monitor engagement.
• Effectiveness of advertising campaigns on influencer content. Influencer marketing should be a supplement, rather than a substitute to other marketing strategies. Savvy marketers use ads to boost the feasibility of content shared by their most promising influencers. While this increases the impact of their campaigns, it also requires a larger budget. These marketers need to look at the data behind their advertising campaigns to optimize them for optimal effectiveness. This is an area where marketing filters can be useful.

Smart marketers have always studied these types of data points to get the most value of their campaigns. However, even the most analytically gifted people are bound to miss certain observations. Machine learning is helping marketers streamline these aspects of their campaigns.

The Utilization of Machine Learning in Influencer Marketing

Savaram Ravindra of Single Grain wrote a great article on the use of machine learning in modern influencer marketing. She shared some case studies of companies that have successfully integrated machine learning into their influencer strategies.

These companies are using sophisticated tools like Linqia, Combin and Influential to get more value from their influencer strategies. Combin is a cutting-edge tool for growing Instagram followers. The tool uses AI technology to improve audience targeting and boost engagement. Combin also uses big data to help users optimize their Instagram marketing strategies. Some of the benefits of using Combin include:

• Analyzing Instagram follower growth statistics
• Measuring the performance of Instagram marketing efforts
• Developing a deeper understanding of user engagement through machine learning
• Targeting users based on demographic factors such as geography and gender
• Automating repetitive actions

Combin has used machine learning to get the most value of Instagram marketing campaigns. This is one of the reasons it stands out from traditional Instagram marketing solutions.

These tools use state-of-the-art machine learning technology to find influencers and identify the best strategies to use them to engage customers. Influential uses the IBM Watson algorithm to look for synergy between influencers and target customers.

Some of the companies that have relied on these machine learning tools to get more value from influencer marketing include Hyundai and Nestle. Much smaller companies could also benefit from these machine learning platforms, but they need to understand the scope of their campaigns and utilize the AI features appropriately.