In October, the article “Swipe Douyin, Play Kuaishou” demonstrated the core differences between Douyin and Kuaishou: single-column and double-column product designs bring about the difference in fault tolerance, and the difference in core optimization indicators of superimposed recommendation algorithms ultimately shaped Douyin Different from Kuaishou, a strong media type and a strong community type.
The difference in fault tolerance rate can be further explained from the difference in data caused by the difference in user behavior.
In the feed stream of Douyin, users can browse indefinitely and can choose to stay or not, and cannot choose whether to see a certain video. In the two-column feed stream of Kuaishou, users select the video they are interested in and click to watch. , After browsing/like/comment, exit to the dual-column feed to continue browsing. This makes the data that the two products can collect are basically the same: video completion rate, like rate, comment rate… But Kuaishou has one more link and a unique data indicator: from waterfall to click content viewing CTR.
Click on the cover to enter the details page. This behavior has a strong performance of subjective intention. This kind of strongly expressive data allows the algorithm model to accurately capture user intentions. In contrast, the user’s behavioral expressions such as the length of stay in a certain video and the frequency of refreshing are not necessarily clear, and longer calculation and recognition by the algorithm system are required. Users who use the two products of Douyin Kuaishou at the same time often feel that Kuaishou is iterating faster and can more accurately reflect users’ instant interest points. To some extent, it is the difference caused by the different dimensions of the input data.
Single column + user behavior is relatively weak ideographic vs. double column + user behavior is strong ideology, so that Kuaishou can tolerate more diverse content to users, making Douyin need to focus more on head content, showing a difference in fault tolerance. Combining the core optimization index differences of Kuaishou “submission rate> per capita VV> per capita attention” and Douyin “per capita VV> submission rate> per capita attention”, finally showing a series of differences between the two in the community atmosphere and content consumption efficiency .
There is one more link in the product funnel. Today, when advertising is content, this model can be further extrapolated to explore the platform’s advertising monetization potential.
Single column, Double column, Difference in Ads monetization
From the perspective of the user’s content consumption chain, the double column has an extra layer of click jump compared to the single column, so in the funnel model of advertising commercial products, the double column needs an additional layer of CTR.
For single-column products, the advertising revenue formula is well understood, that is, DAU (the number of users per day) multiplied by the per capita VV (the video volume per person per day) multiplied by the Ad Load (advertising load) multiplied by the CTR (Advertising click rate) multiplied by CPC (advertising click price), and finally the total product revenue scale can be obtained.
For dual-row products, the formula needs to be modified. The index of per capita VV must be further subdivided, and the total exposure is multiplied by the CTR from the cover to the inside. Per capita VV = exposure × CTR (according to Kuaishou “2019 Kuaishou Creators Report” data, Kuaishou’s CTR is about 20%). In the maturity period of the product, when the user duration is similar, the per capita VV of single-row and dual-row products are basically similar.
For example, suppose the two products are short video products with single row and double row. The average daily duration is 60 minutes, the average video duration is 30 seconds, and the product’s AdLoad is 15%, that is, the per capita VV of the two products is both 120. For a single column product, the number of ads displayed is equal to VV×Ad load=18 per capita.
The content exposure CTR0 of the dual-column product was 20%, 600 videos were exposed to users, and 120 VVs were harvested. The number of ads displayed = total exposure × Ad Load × CTR1, where CTR1 is the click-through rate of the advertisement. In an ideal situation, advertisements and content are exactly equivalent, and the click-through rate of advertisements and content is exactly 20%. In this case, the number of advertisements displayed by the double-column product is also 18, but in fact, users face advertisements and The click-through rate of the content cannot be the same, and the ad click-through rate CTR1 is less than the content CTR0.
According to the interviews with actual practitioners, the gap between CTR1 and CTR0 is larger than expected, even reaching a gap of 5-10 times. That is, the CTR of the content is 20%, then the CTR of the advertisement may only be 2%-5%, which means that the advertising inventory of dual-line products is much lower than that of single-line products. However, the good news is that active clicks mean a clear indication of user intent. In theory, dual columns can charge a higher price for each click, which is similar to search ads and their unit prices are much higher than display ads.
On the whole, single-line products are more suitable for commercial realization of advertising, and double-line products have lower advertising ceilings than single-line products.
From the actual data, select the representatives of domestic and foreign single-listed products: Facebook, Twitter, Weibo, Today’s Toutiao, Douyin, and dual-listed product representatives: Kuaishou, Station B, Xiaohongshu, Pinterest, and the company’s liquidity ability according to The user’s advertising monetization ability for every hour of viewing is normalized.
The advertising monetization ability of dual-line products is weaker than that of single-line products. However, considering that several companies with dual-line characteristics are generally in the early stages of commercialization, there is still room for growth in the future.
There are a few more interesting figures that can be discussed.
From the internal comparison of the individual products. If Facebook is used as the monetization ceiling of a single product, we can see that the biggest difference in monetization efficiency of Toutiao today comes from exchange rate differences. After excluding exchange rate factors, the difference in monetization efficiency is about 20%, which is already at the same level. After excluding exchange rate factors, Weibo and Twitter’s realization efficiency gap is about 30%, and these two Weibo companies are considered to have weak liquidity capabilities at home and abroad. Compared with Facebook and Toutiao, the gap is indeed obvious.
TikTok has cut the duration to live broadcast this year in order to attack the fast hand, and its advertising monetization ability will be affected to a certain extent, otherwise it will be a bit higher than the current data.
The normalized liquidity of Xiaohongshu is almost similar to that of Douyin, and there is still room for growth in the future, reflecting the strong liquidity of the female vertical community. From the perspective of Xiaohongshu’s products and community atmosphere, the content of Xiaohongshu and the advertising content itself are highly unified, and users have a strong acceptance of advertisements. A large amount of software is distributed in the content, making the difference between the CTR0 of the content and the CTR1 of the advertisement. There is an exaggerated gap, but closer. The user’s precise click intention and clear click intention enable Xiaohongshu to charge higher prices for advertisements.
Xiaohongshu plans to double its advertising revenue next year. While focusing on user growth, it also strives to expand from beauty to fashion and beauty categories and expand the source of advertisers.
Pinterest’s unit monetization ability is higher than that of Twitter. The logic of the originator of this double-column waterfall is similar to that of Xiaohongshu. The user himself is browsing content for shopping, home furnishing or design inspiration, and is naturally highly compatible with advertising content. In terms of commercialization, it continued to acquire technology companies, strengthened its ability to accurately recommend, and launched a variety of advertising formats at the same time, and developed a variety of new commercial products such as shopping tags and e-commerce shopping guides.
The monetization ability of advertisements at station B is indeed too miserable. It is limited by the advertising inventory in the dual-column mode. The duration of small videos is longer than that of short videos. This makes the per capita VV much lower than short video products, and superimposes relatively shallow advertisements on station B. The main pool causes the same advertisements to appear repeatedly, ultimately resulting in extremely low advertising monetization capabilities. Judging from the financial reports of Station B in recent quarters, advertising growth is mainly driven by brand advertising, and user growth is still the core driving factor.
Currently Station B is testing a new and larger cross-waterfall advertising format, but it is still very cautious about the advertising form in the video. For Station B, maintaining a community atmosphere is still the first priority.
Kuaishou does not have the burden of station B. In order to break through the ad inventory restrictions brought by double columns, Kuaishou has launched in-video floating window ads and post-roll ads this year, and tried to open up new monetization models through various methods such as challenges. . In the near future, the fast-moving fast-moving fast version adopts a single product form, which may become a breakthrough in the realization of fast-hand advertising in the future.
From the conclusion, the funnel model for single-row products has a shorter path and is more suitable for advertising monetization. The ceiling for monetization of dual-row products will be lower than that of single-row products. To other monetization methods, such as live broadcast and delivery of fast hands, live broadcast and games at station B.