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Influencer Marketing

What Is Influencer Marketing?

Influencer marketing is the practice of hiring influencers—prominent social media users with active engagement on their posts—to sell a product or service. Companies need accurate data on these personalities, and their audiences, in order to form successful partnerships with them.

Companies don’t need to pay high fees to people with huge follower lists, either: micro-influencers and even nano-influencers may provide a higher ROI than big names.

Why Is It Important to Have a Good Influencer Marketing Model?

People still trust product recommendations from friends and family more than any other kind of advertisement. Influencer marketing has more in common with friend recommendations than anything else. This is particularly true for micro-influencers (around 100,000 followers max) and nano-influencers (around 10,000 followers max), who have small but highly engaged audiences.

In addition, companies should consider that traditional advertising continues to decline in influence; they must embrace new opportunities to reach their target customers.

What Internal Data Should I Have for a Good Influencer Marketing Model?

To build a good influencer marketing model, start with internal data such as customer demographics and feedback. Quarterly transaction data and engagement on your company social media pages is also important.

What External Data Is Essential for a Good Model?

External data to use to build a useful influencer marketing model includes competitor analysis, market research data, and news data. Equally crucial, of course, is influencer research. As potential partners, you need to be sure these individuals have an audience that is receptive to your marketing and that the influencer’s reach is worth their price. To this end, there are many sources to determine an influencer’s reach, such as SocialBlade.

What External Data May Prove Useful for a Good Model?

Additional external data that can enrich your influencer marketing data model includes trend forecasting, social media, and market data.

What Are the Main Challenges of this Use Case?

Some of the challenges you will face in developing an influencer marketing AI model involve finding the right kind of influencer for your goals and your budget. However, one of the problems of this use case is the fact that many influencers—even regular social media users that do not have the number of followers to reach even micro-influencer status—have fake followers.

There are tools that identify the percentage of likely fake followers, such as Fake Check. You can also investigate influencers for fake followers using guides online.

Interesting Case Studies and Blogs to Look Into

Medium: Predicting Instagram Influencers Engagement with Machine Learning in Python
Mediakix: 2020’s Best Influencer Marketing Case Studies: 62 Campaigns from Top Brands, Influencers, & More

Tangible Examples of Impact

Ahead of posting, users could be prompted to confirm whether they have been incentivized to promote a product. Instagram has said it will deploy technology and algorithms to assess when users may have not disclosed their post was in fact sponsored. Labelled posts will not be treated differently than organic posts by Instagram’s news feed algorithm, according to the company.

That labeled posts are not treated differently is significant, said Oliver Lewis, founder and managing director at News UK influencer marketing unit The Fifth Group.

“Part of the reason labelling is overlooked is fear around engagement and deprioritizing” posts in the feed,” said Lewis.

Digiday: ‘One of many stages of maturity’: Ad industry welcomes Instagram influencer marketing labeling changes

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