Article author: SunnyZ
Source: Cube Labs
TL;DR
Background
As the person responsible for growth in a Web3 project, a common question I hear is how to cold start a project, which boils down to how to do GTM, i.e., how to make the project known to more people and head towards the market.
Compared to Web2, there is currently no systematic GTM methodology in Web3, still in a relatively blind growth stage. Furthermore, the method of managing community user expectations through Tokens and NFTs differs from Web2 growth hacking methods, making many Web2 market strategies difficult to implement or even incompatible.
However, the logic and process of the two are similar, both requiring customer acquisition, activation, retention, and referral, with customer acquisition having many universal strategies in Web3, such as AMA, Giveaway, Collab, etc.
Thus, based on these common scenarios, utilizing Web3 native growth platforms can significantly improve early customer acquisition efficiency and reduce acquisition costs. Additionally, well-coordinated community operations can relatively accurately retain loyal seed users. In a word: Absolutely!
AARRR Model
With the increase of various Web3 projects (it feels like there are more project teams than users in a bear market), many products have emerged in the Web3 Growth track, gradually refining into vertical fields. There are many DApps available across multiple dimensions, such as user acquisition, community management, information reminders, reward distribution, data analysis, etc. [See the figure below for details].
The highest relevance to project cold start is user acquisition. Recently, my LoopX project is undergoing this cold start process, so I reviewed how I used Web3 native growth methods to achieve the project's growth from 0 to 10k followers, hoping to provide some inspiration and help to other project teams in the market.
Growth path
In the past month and a half, I have deeply used 13 platforms in the order shown below, initiated multiple activities, and recorded data on Twitter follower growth. The overall trend shows a phase-wise steady increase, with rapid growth in September and October due to numerous activities. In November, due to my laid-back approach, I basically did not issue tasks, focusing mainly on collaborations and giveaways, so the growth rate slightly slowed down.
In the specific operation process, if you are unsure how to arrange the order of cooperation between platforms, you can directly refer to the figure below.
Although these 13 projects have different directions, their functional similarities are high, especially among All-in-one platforms. The differences are not significant, and it is evident that each is 'borrowing' trends. The main competition is in product iteration and BD speed, making for intense competition.
In order to better utilize each platform, understand advantages and disadvantages, and optimize activity details, I analyzed the data from these 13 platforms based on practical results (the activities of Aki and Clique have not yet ended, and the data will be updated after they conclude). I mainly compared the two modules of functionality and traffic among all factors, corresponding to the core pain points of solving needs and providing value (traffic).
Practical testing logic
Before presenting the data, let's clarify the logic. The main methods used are variable control + multiple tests, aiming to ensure comparability and relative accuracy of the data.
Task settings: The task settings on each platform are similar, involving social media follows and filling out a product feedback Google form (of course, some platforms do not support redirecting to form filling, so only social media tasks are available). The content of each platform's form is the same but the links are different.
Screening method: Manually filter real users based on the responses in the Google form backend and distribute rewards.
Data analysis: Data is analyzed on mintkit.ai based on user wallet addresses, categorizing users into 5 types: Bot, General, Diamond Hand, Blue Chip, and Whale based on indicators such as NFT holdings, categories, address balances, and wallet associations.
Multiple tests: If conditions permit, the same platform will test various functionalities multiple times, such as Galxe, Link3, Quest3, DeNet, etc., with each platform being tested no less than 2 times.
Segmented scenarios: For example, for AMA, use the same Space to measure data across multiple platforms; GA is similar.
(Note 1: All data in this article comes from the real growth data of the @loopx_web3 project. Single evaluation data may have certain errors and should be based on actual operations.)
(Note 2: For ease of identification, this article uses abbreviations for some platforms and common terms: TW for Twitter, DC for Discord, TG for Telegram, TS for TwitterScan, WL for Whitelist, GA for giveaway, Txn for Transaction.)
Traffic comparison
The traffic differences across platforms are significant, roughly divided into three tiers. Galxe stands alone, as its organic traffic can balance the second-tier platforms, namely Port3, Pyme, Quest3, Link3, TaskOn, and Trantor, where Quest3's Banner has a noticeable traffic advantage.
From the perspective of TS activity and dissemination, Port3, Quest3, Link3, Crew3, and TaskOn's Twitter operations are very proactive, resulting in a relatively broad scope of activity dissemination (by the way, why do so many projects have 'xx3' in their names? 👀). Galxe has shown signs of complacency after a long period of issuing tokens, as the foundational support is already substantial. Even if activity levels are not high, dissemination is not significantly affected.
Platforms with high traffic also tend to have many Bots, with almost all platforms having 15-30% Bots, and Quest3 at 36%, meaning nearly half are Bots. It seems that DID projects still have a long way to go. Among these platforms, Port3 and Clique support distributing rewards after filtering based on multiple data conditions. Notably, Clique's data monitoring depth is the most thorough, significantly reducing the number of Bots holding rewards.
I was quite surprised by Trantor. Despite having not many Twitter followers, it has a considerable number of activity participants, and the user data performance is also impressive. In subsequent communications, I found that Trantor often takes the initiative to launch joint marketing activities among multiple projects, which greatly helps increase B-end user stickiness.
Crew3 was affected by domain issues, making this data not very referenceable. Web3 projects are still quite fragile, and domain account issues can significantly impact project development.
User overlap comparison
All platforms have a high overlap with Galxe users, indicating that there is no significant platform preference among users seeking rewards, and cross-platform harvesting is the norm. This also suggests that Galxe has a large user base and a clear market first-mover advantage.
Port3, Link3, and Galxe users have a high overlap, possibly because the previous version of Port3 did not support direct reward distribution, requiring collaboration with Galxe. Therefore, these users likely originate from Galxe. Moreover, Galxe was the first platform to introduce the AMA badge feature, while Link3 primarily promotes the AMA vertical market, making the high overlap in user sources quite normal.
Pyme and Trantor also have a high user overlap. Pyme has a clear user advantage in the Indian and Southeast Asian markets, while Trantor is incubated by StarryNift, indicating that users from GamFi primarily distribute in the aforementioned markets.
Among all platforms, TaskOn's data is quite different, with unique characteristics regarding both the number of Bots and overlap. TaskOn cooperates deeply with OntoWallet, and many users come from the DeFi track, suggesting that DeFi users are likely not the same group as NFT badge collectors.
Function comparison
When comparing horizontally among project teams, it is impossible to exhaust all functionalities. Here, I focus on the functionalities that project teams relatively need and frequently use, breaking them down into five modules: on-chain tasks, off-chain tasks, reward distribution, data analysis, and scenario coverage. Gray indicates that the platform has this function, and green indicates that this function is better than that of other platforms.
Functional comparisons between vertical platforms and All-in-one platforms are not on the same dimension, so I have also split them by platform type. It is recommended to view them separately for a more reasonable understanding.
The functional differences across platforms are minimal, primarily presenting off-chain verification as the main focus, supported by on-chain verification, with a multi-scenario coverage that also confirms the strong substitutability of the aforementioned DApp functionalities.
Data analysis features are very important, but there are not many projects with this capability, only Port3, Link3, BetaPlug, and Clique. After all, data analysis needs to be integrated with data monitoring. For platforms, this is a cost-ineffective feature, especially for retention data, which requires high standards for display. This also indicates that Web3 growth has not yet reached the data-driven stage.
The completeness of functionality and traffic is not necessarily proportional, as this relates to each project's market strategy. There are differences in whether to achieve To C through To B. For instance, purely tool platforms like CWallet, Genki, and Gleam are all To B growth and not To C, meaning they won't directly redirect benefits back to the platform, which also determines that this type of platform is not suitable for early projects to use for cold starts.
The specific differences in product forms mainly stem from the long-term missions of various platforms. For example, Galxe aims to create an on-chain credential system, so it focuses heavily on issuing badges. The initial processes can complete similar tasks as Gleam, which also enhances Galxe's scalability, but in terms of task releases, it appears quite average. In contrast, Crew3 aims to build a Web3 Discord, so it has many community activity templates, such as daily check-ins, content creation, and invitation tasks, which are more focused than on other platforms.
**Although All-in-one platforms have comprehensive functionalities, their depth of scenario validation is insufficient, and functionalities are not perfect.** Therefore, many specialized products have emerged from high-frequency scenarios like GA and AMA. In simple terms, the online earning market is vast, with many opportunities.
Here are screenshots from the backend of each project, allowing everyone to intuitively feel their differences and functional details ⬇️
Combining the two hard metrics of functionality and traffic, the general distribution of the above projects is as follows:
(Again, it should be stated that single evaluation data may have certain errors and should be based on actual operations.)
We can see that there is no very obvious leading product in this track; there is a flourishing competition, with new products continuously emerging, which is a very good market signal. Each DApp can find its own positioning, with enormous room for development.
Other factors comparison
In addition to the two key indicators, the following projects are quite good in design, development, and BD during product use and team interaction [ranking is not in any particular order]. Among them, the design of Quest3, the iteration of Link3, and the BD of Clique are outstanding 🆙
Timely BD responses: Clique, Port3, DeNet, Pyme, TaskOn, CWallet
Excellent product design: Quest3, Link3, Crew3, Aki Network
Rapid functional iteration: Link3, Quest3, Beta Plug, Clique
Many times, the differences in BD responses determine the speed of collaboration with project teams. These soft indicators often truly determine the future direction of a project, especially for DApp products, as product needs tend to be universal. The breadth and depth of information acquisition by frontline market personnel, as well as the speed of feedback to the product, directly impact product development and iteration. Therefore, when choosing to use a specific product long-term, it is advisable to look at the project's TW activity level and iteration speed before making a decision.
Usage suggestions
In the absence of in-depth cooperation with other platforms, it is recommended to use Galxe for the first activity, as its organic traffic is very effective, and it has a large user base, allowing for relatively quick acquisition of early users.
Try to reach a Co-PR and community promotion cooperation with the project team. If you can get a Banner or other recommended position, be sure to strive for it. The Quest3 Banner is quite good, and Galxe's Banner can be used when growth reaches a certain stage, especially during major product launches.
Do not focus exclusively on one platform for harvesting; use a distributed approach. After multiple uses of a single platform, there will be no new users, which is no different from only doing activities within your own community. In the long run, 2-3 All-in-one platforms are more appropriate, while vertical platforms should be broken down by scenario, with 1-2 per scenario being suitable.
Relatively mature project teams can focus more on activation and retention activities. However, I have not seen many project teams currently employing these activity strategies. Port3, Trantor, Crew3, and BetaPlug are still viable, but I have not discovered others for now. If you have any interesting platforms, feel free to recommend them~
Acknowledgments
A big thank you to all the friends who provided various information support for this article! Special thanks to MetaEra for their strong support. Finally, thanks to Grace from TwitterScan, Messy from Minkit for their help, and the project team who patiently answered all my questions during the communication process. I sincerely hope that everyone's projects continue to thrive, WAGMI!