The Power of Deep Tag: Precise Semantic (language) Analysis to Understand Followers’ Preferences

In the realm of influencer marketing, the trust and loyalty that followers have towards influencers are the driving forces behind their impact. Influencers build a dedicated following by showcasing their expertise and content, attracting followers who resonate with the influencer’s charm or thematic content. Many brands aim to tap into potential customer bases by leveraging the influence of influencers within their specific communities. When selecting influencers, their “content attributes” become a critical factor. 

By selecting and collaborating with influencers whose content aligns with the products or services, brands can effectively reach their target consumers, achieving marketing goals like brand exposure and conversion. However, influencers’ content is constantly evolving, making it a challenge for brands to stay up-to-date with the latest trends and follower preferences.

For example, seasoned influencer 蔡阿嘎 initially became famous for his “funny” videos, but after becoming a father, his videos gradually became more family-oriented. In the rapidly changing landscape of influencer content. How can brands keep up with trends and determine which types of content are favoured by followers (potential customer base)?

KOL Radar has introduced the “Deep Tag” feature to provide a more data-driven analysis of the content types produced by influencers, by combining AI crawling technology with Natural Language Processing (NLP) semantic analysis to automatically categorise content tags based on the textual content of influencers’ posts. This innovative tool assists brands to quickly and accurately understand the ever-changing landscape of the influencer community market.

With  Deep Tag, KOL Radar calculates the proportion of content types produced by influencers and their community engagement data, so that brands can swiftly and precisely grasp the preferences of the target market creating influencer marketing campaigns that resonate with the consumer. *Currently, Deep Tag is exclusively available to KOL Radar’s team in influencer marketing projects.

AI provides a semantic analysis of influencer content tags, enabling real-time and precise tracking of content types and their effectiveness

1. Precise Semantic Analysis for Automatic Tagging 

The all-new Deep Tag feature utilises AI-powered automated tagging technology to categorise the content of each influencer’s posts, encompassing a total of 27 content attributes. By leveraging lexicons and Word Embedding techniques in Natural Language Processing (NLP), it automatically identifies the types of content and assigns relevant tags. This not only reduces the time delay in understanding the market but also provides real-time insights into the latest trends in influencer content. KOL Radar will continuously update the social tags of influencers, ensuring their attribute categories are up to date with the most current trends.

2. Accurate Tracking of Influencer Content Types 

The Deep Tag’s radar chart presents the distribution of the top 5 content types managed by influencers. Additionally, it automatically calculates the “interaction rate” and “video view rate” of all posts in the top 5  content types in real-time. Brands can use the overview to understand the primary content influencers are currently focusing on within their communities. By combining this with performance data, brands can further determine which content types are most engaging, performing the best and are most favoured by the followers in the influencer community.

3. Evaluating KOL content effectiveness

Deep Tag allows KOL Radar to analyse the distribution of influencer content over approximately 3 or 6 months to observe the effectiveness of influencer posts across various social platforms and different content types. Brands can use this data to understand the recent trends in influencer content and identify influencers whose content aligns with their brand identity. Moreover, KOL Radar employs Deep Tag to identify content tags that are the most engaging within the chosen influencer’s audience, allowing brands to design campaigns tailored to follower preferences and seamlessly integrate product features into the content. This approach fosters collaboration that generates follower discussions and buzz.

KOL Radar’s Deep Tag offers services to a wide range of businesses and brands. For example, the next two cases where influencers achieved remarkable engagement results. Let’s take a closer look together!

Game streamer and financial advisor: 丁特

Former professional jungler (killing monsters to gain gold and experience) for Taiwan’s first eSports world champion team Taipei Assassins, 丁特, is also currently the CEO of the eSports professional team Beyond Gaming, and a well-known streamer. His professionalism in League of Legends and his humorous and straightforward communication style can engage his audience well. According to KOL Radar’s statistics, 丁特 has amassed a total of 1,509,578 followers over the past nine years, and most of his content has revolved around gaming, solidifying the public’s impression of him as primarily a “gaming” influencer.

However, utilising KOL Radar’s Deep Tag analysis, it has been revealed that on 丁特’s YouTube channel, apart from his original forte in “gaming” content, the “financial” content boasts an interaction rate as high as 0.55%, surpassing the engagement performance of gaming content (0.22%). Furthermore, the financial video view rate is 1.4 times that of gaming content. Based on this analytical data, it’s evident that 丁特’s audience doesn’t just enjoy watching him play games and chat; they also appreciate his insights into investments. This has given financial-related content a fresh angle for 丁特’s recent channel strategy, opening up possibilities for collaborations in the financial sector.

Gaming, unboxing collectables, and travel content: 安安邊緣子

The channel “安安邊緣子” is a team consisting of 阿民 and 阿憲. The channel’s style primarily revolves around gaming, unboxing collectables, and daily short videos. After 2019, they gained a substantial number of new followers through their “Fen Shou” (a series where they open various collectable items) series. They invested heavily to complete their collectable sets and provided authentic assessments of the strengths and weaknesses of each product from the perspective of a collector, becoming a reference for viewers before buying a product. As a result, 安安邊緣子 has become a significant opinion leader in the unboxing genre, associated with labels such as unboxing and gaming.

According to the content analysis data from KOL Radar Deep Tag, in the past few months, 安安邊緣子’s YouTube channel has started creating travel-related videos, and their performance in the social media community has been outstanding. Not only does their average engagement rate surpass their other videos, but their video view rate also reaches as high as 42.65%. This is around 70% higher than the view rate for gaming content. The unexpected appeal of the travel theme has captured the audience’s interest in 安安邊緣子, presenting a new opportunity for brand collaborations.

Conclusion

KOL Radar’s Deep Tag, powered by AI crawling technology and  NLP semantic analysis techniques offers a deeper understanding of influencer content and follower preferences. It empowers brands to plan highly engaging influencer marketing campaigns and strategies that align seamlessly with the target consumer preferences.

Interested in learning more about influencer marketing data strategies? Feel free to inquire for free consultations: https://www.kolradar.com/en/

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