Threads, an opportunity or another threat! (Part 2)

Intro

In this article, I explore the concept of Threads, the recently launched social media platform by Meta designed to rival Twitter. Additionally, I endeavor to address the questions posed at the conclusion of part 1(link below).

To begin with, gaining an understanding of the revenue model employed by social media platforms, particularly Twitter, would be beneficial. Over the course of social media platforms' existence, numerous revenue models have been explored, but the digital advertising model stands out as the most significant by a wide margin. While there are several other models to consider, other noteworthy ones are data licensing model, subscription model and partnership/licensing model by utilizing APIs. This article primarily concentrates on the advertising model, aiming to subsequently provide a more comprehensive perspective on the initial questions.

Digital Advertising

While digital advertising is a vast and expansive topic, I will refrain from delving into details beyond the scope of this article. Instead, let us focus directly on the advertising model employed by Twitter, which is also applicable to most other social networks. This model primarily relies on a blend of native advertising and programmatic advertising.

Native advertising

Native advertising is a type of paid advertising that matches the form, style, and function of the platform on which it appears. This means that native ads are designed to blend in with the surrounding content, making them less disruptive to users. Native ads can take many forms, including sponsored content, promoted listings, and in-feed ads. They are often more effective than traditional display ads because they are more likely to be clicked on and engaged with.

Programmatic advertising

Programmatic advertising is a type of digital advertising where ads are bought and sold through automated systems. This means that there is no human interaction involved in the buying and selling of ad space. Programmatic advertising is often used to target ads to specific audiences based on their demographics, interests, and online behavior. This makes it a very effective way to reach your target audience and drive results.

The Algorithm

Audience segmentation plays a crucial role in social networks. Users shape their virtual identity through their activities and connections on these platforms, which automatically places them into different audience segments. For instance, if you're a football fan who follows famous players, you become part of a larger circle of football fans. If you also support a specific team like Bayern, you belong to a narrower group consisting of both football fans and Bayern supporters. This segmentation continues to refine based on finer details.

Let's consider another example related to activities. Imagine you have an interest in vegetarianism or are inclined to become a vegetarian, but you haven't followed any vegetarian-focused accounts or directly connected with them. However, when you come across posts about new vegetarian recipes, you pause and spend a few seconds looking at them. As a result, the audience segmentation algorithm perceives your increased frequency of engaging with these random posts and infers your interest in vegetarianism, even though you haven't explicitly shown it. Consequently, if there were a hypothetical advertisement for a gathering of Bayern fans to watch a match at a vegetarian restaurant, you would likely see that post. It's important to note that this hypothetical advertisement appears in the regular post format, usually with a small indicator indicating that it is an advertisement located somewhere below the post.

The same principle applies not only to advertisements but also to the regular posts you come across on social media every day. In essence, this concept is similar to how SEO (Search Engine Optimization) and SEM (Search Engine Marketing) are distinguished in the search engine landscape. The posts that are specifically targeted based on user segmentation, similar to advertisements, can be likened to SEM. On the other hand, the posts (such as tweets, reels, stories, etc.) that are shown to users based on their segmentation rather than the accounts they follow can be compared to SEO.

Successful social networks have developed extensive audience segmentation and content segmentation over the years, which can be considered as their most valuable asset alongside their user base. For instance, Twitter boasts approximately 500 million users, while Instagram (the foundation of Threads) has over 2 billion users. When you multiply these user numbers by the average monthly posts per user (30 on Twitter and 2 on Instagram) and interlink them with tens of thousands of segments, it becomes clear that the algorithm behind it is highly sophisticated and complex. Effectively managing such a vast amount of data would be impossible without leveraging the power of machine learning and advanced big data analytics solutions.

Conclusion

It is still too early to say for sure what the future holds for Threads, but based on the available evidence, we can make some educated guesses. Here are the main questions that have been raised about Threads, and my thoughts on how they might be answered.

Q: What sets Threads apart from Twitter? How will it benefit consumers? And what does the future hold for text-first social networking?

A: At this stage, Threads lacks a distinct unique selling point (USP). It appears to be a clear imitation or clone of Twitter. Currently, Threads is missing many basic features present on Twitter, such as "Spaces" (a Clubhouse-like feature), direct messages, polls, voice messages, and other simple elements. However, since Meta (the parent company) has already developed these features in their other services, it would be relatively easy to incorporate them into Threads. Additionally, Meta could also integrate some appealing features from Instagram and Facebook to slightly differentiate Threads from Twitter.

Another significant difference between Threads and Twitter lies in their respective user bases. Meta boasts a substantially larger user base, approximately four times the size of Twitter's user base. Despite many Twitter users also being users of other Meta platforms, their loyalty and engagement levels differ (almost 10 times of the engagement rate of Instagram). Twitter users generally display a notably higher engagement rate compared to users of Instagram and Facebook. This difference could be attributed to the nature of engagement on each platform, with Twitter focusing more on text-based interactions from trusted sources, while Instagram revolves around entertainment and sharing personal daily activities. For instance, an imaginary company account on both platforms would use Twitter to announce updates, changes, and official views on trending topics, while on Instagram, the company would post its new campaign poster.

The other topic to consider is Meta's approach to monetizing the platform. According to Zuckerberg, they plan to wait until they reach a user base of 1 billion before implementing monetization methods. The key question is whether they will stick to programmatic/native advertising or explore the controversial subscription method advocated by Musk recently.

One advantage for Meta is that they have already developed an advanced algorithm based on user activities on platforms like Instagram. Since Threads uses the same user accounts for sign-ups, users' history seamlessly transfers to Threads. This enables Meta to create personalized posts and advertisements for users quickly. For instance, if you were an active Instagram user who frequently checked out pictures of girls in bikinis, you might soon start seeing Threads posts on your feed related to trips to coastal areas!

The coexistence of both platforms suggests that Threads will probably attract more light-topic engagement, while Twitter will retain its focus on more substantial, hard-topic engagement. Additionally, it's crucial to assess the conversion rate from Twitter to Threads; the higher this rate, the greater the likelihood of Twitter gradually losing its prominence.

Note: This article has undergone multiple enhancements and proofreading sessions with the assistance of ChatGPT. I intend to continue writing articles in my preferred topics within the tech sector, utilizing AI tools to generate images and enhance the quality of my written content.