The Hitchhiker's Guide to the Web 3 Marketing - Part 1 of 2
Table of Contents
This content is provided for informational and educational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, references to any securities or digital assets are for illustrative purposes only and do not constitute an investment recommendation or offer to provide investment advisory services. Factomind cannot be responsible for your use of the information provided in this content. Factomind has established, maintained, and enforced strict internal policies and procedures designed to identify and effectively manage conflicts of interest related to its business activities. Factomind does not own any digital assets mentioned below, nor has it made any purchases or sales of the digital assets mentioned below. All materials in this research paper are sourced from publicly available information.
Some information has been censored in this paper due to possible conflicts of interest. If you require access to the full version, please contact us via e-mail.
Far out in the uncharted backwaters of the unfashionable end of the western spiral arm of the Galaxy lies a small unregarded group of Web 3 founders.
These founders had a common problem, which was this: They knew they wanted Web 3 communities, but they did not know how to have them. Many solutions were suggested for this problem, but most of these were largely superficial and abstractive, leaving these brave yet lost founders unattended.
The founders consider Web 3 marketing strategies as unorthodox and idiosyncratic, sometimes as beyond their fathomable universe. The word ‘Web 3’ itself is pronounced in an eccentric way, and industry-specific jargons: Airdrops, NFTs, ERC-20 tokens, are simply out of this world. However, if we examine carefully, the force that drives Web 3 communities is no different from ‘traditional’ communities. After all, we are born equal, and we are marketed equal.
We at Factomind Technology gathered Twitter data on successful projects and failed projects in their marketing strategies, and were able to witness a very clear separator in data patterns between two sides. We need to give a more thorough explanation on being ‘successful’ later, but to make a long story short, the key difference between the two was the timing, not the method, of user acquisition strategies (airdrops, giveaways, grants, etc.).
Failed projects, before anything else, arrive at Twitter and bombard airdrops right away. And the recipients of the airdrops, who feigned fervent advocators to be eligible, are no longer engaged with the projects as soon as the tokens are dropped into their wallets. Airdrops are not free; no project can consistently airdrop a significant amount of its tokens forever.
Successful projects play a totally different game. We analyzed Twitter activities of their respective official Twitter accounts, found out what clauses are written on their marketing rulebooks, and listed them out so that all you hitchhikers could see.
We hereby proudly present “The Hitchhiker’s Guide to Web 3 Marketing,” a comprehensive compilation of detailed, in no way vague or ambiguous, action items to be administered onto your official Twitter account.
2. Data Specification
2.1. Data Collection
For our data for Social Media volumes, we used data obtained from major data vendors and cross-checked with our internal database. No raw data has been used since we used derivative statistics instead. An example time series for our Social Media Volume data would look like below:
Figure 1 – Twitter Dominance vs Other SNS for a Random Ticker
For Twitter data, we looked through 70,629 Tweets from 55 projects, and scraped timestamps, external links, media links, full texts, and interactions from each Tweet. We define interactions as the following:
- interactions = a number of times users interacted with an official Twitter account
- average interactions = 7-day moving average of interactions
- organic interactions = average interactions excluding interval outliers
- core tweets = a local maximum Tweet out of 7-day interval
We calculated 7-day moving averages for each time series, and also 7-day moving averages excluding the local maximum (which we call organic interactions) as well, to account for outliers caused by bot activities. We will use organic interactions as our base numbers for analysis as an assessment on simple Twitter interactions time series is vulnerable to fake account (bot account) utilization.
Although other methods involving NLP offer a much higher degree of accuracy in terms of bot detection, we still used organic interactions as other methods are very computation-heavy, and organic interactions provide enough accuracy within our scope of study.
Note that the term ‘organic interaction’ strictly refers to our specified definition, and has no affiliation with Twitter API’s definition of non-public information.
2.1. Research Methodology
After we collected Twitter data, we categorized 55 tickers into three subgroups of ‘successful,’ ‘failed,’ and ‘in-between,’ based on quantitative and qualitative criteria including:
- – Market capitalization
- – Circulating supply to total supply
- – Sectoral representativeness
- – Community activity
Next, we filtered only successful projects and examined their Twitter properties.
We were able to split each successful project into four phases based on observed patterns of timestamps, textual analysis, and interaction metrics. After the phase classification, we double-checked whether the transition into each phase coincided with significant change in twitter activities or in price, and if a project did not fall into the category, we moved it out of sample. This process was to eliminate anomalies due to unfiltered bot activities.
To put simply, if there was no significant Tweet but there was a very high spike in organic interactions enough to trigger a phase transition, we checked whether there was any spike in token price; if there was no change in price as well, we put a ‘unfiltered bot (or used bots twice in a 7-day interval)’ label on that ticker.
The final dataset would look like Figure 2:
figure2 – A Time Series Twitter Interaction & Text Data for Gaming Ticker A
3. Before We Lift
3.1. Why Twitter?
Twitter is big in Web 3. Really big. You just won’t believe how vastly, hugely, mindbogglingly big it is.
Twitter is the largest, indisputably dominant media for communication in Web 3, and all major exchanges of opinions and information take place on Twitter. Thus, it is essential to operate your Twitter account properly to make a business, and we will help you to run it effectively.
First of all, you might be questioning our emphasis on the dominance of Twitter in Web 3 space, so we brought a handy plot here.
Figure 3 – Twitter Dominance vs Other SNS
Twitter dominance in Web 3 social volume compared to Reddit, Telegram, and other less conventional SNS, grew extensively during the bull market. Currently, we can daresay all established projects use Twitter as their main announcement channels.
Aside from boring quantitative proofs, all the fun events in Web 3 also were ignited in Twitter:
- – An argument between two KOLs evolving into a multi-million sized speculative bet on price of a token
- – A whitehacker warning a DeFi protocol of a possible exploit and thus saving all the retail money
Basically, you should not expect to gain sizable customer attention if you do not run a Twitter account.
So now we know that Twitter is important, we gathered and analyzed available data on Twitter: Timestamps, media links, Tweet contents, interactions, etc., and we found out that all successful projects have something in common when they run their Twitters, and all failed projects also do so. Wonder what they are? Please read on.
3.2. 3.2. Successful Projects vs Failed Projects
Twitter data from successful projects possesses very different characteristics from Twitter from failed projects.
We defined a project as ‘successful’ based on:
- – Market capitalization and trading volume within its sector
- – Twitter buzz
We defined a project as ‘failed’ if it falls under the criteria of:
- – A white dwarf, which has a lot of buzz but has a very low market capitalization and a very low trading volume
- – A dark matter, which has absolutely no buzz
Now then, what is this ‘difference’ we have been talking about since the preface? Take a look at Figure 4 and Figure 5.
Successful projects have steady interactions for all Tweets, whereas failed projects have intense interactions with their continuous, early-stage airdrops or IGOs, but nobody pays attention to other news—including those projects’ very products!
By merely looking at interaction plots, we can clearly make a distinction between two projects. Please keep in mind that we classified each project ‘prior to’ drawing the above plots, and we found out the plots exhibited above properties when we drew them ex post.
The same patterns were observed in all successful and failed projects apart from these two examples, thus we naturally examined deeply into the constituents of the patterns—namely, Twitter texts.
Figure 4 – A Time Series Twitter Interaction Data for Gaming Ticker A (Successful)
Figure 5 – A Time Series Twitter Interaction Data for Gaming Ticker B (Failed)
3.3. Four Phases of Web 3 Marketing
We discovered that successful projects show a distinctive time series pattern from failed ones, so we selected timelines of successful projects and identified what events happened during their respective time frames.
Surprisingly (or maybe unsurprisingly, as if there was no commonality, we would not be publishing this), they shared common traits throughout the passage of time. Moreover, we could divide each project into four phases of community marketing based on these common traits.
Figure 6 – A Time Series Data Divided into Four Phases
– Phase 1, Genesis : The development phase of a project’s legitimacy. Phase 1 is the stage for bootstrapping the initial core community through broadcasting legitimacy.
– Phase 2, Pre Big Bang : The community expansion and hype production phase of a project’s product. Phase 2 is the stage to scale a community in a broader term, or to go viral for the product’s launch.
– Phase 3, Big Bang : The core achievement of a project’s roadmap. This would be the decisive moment for a project’s prosperity, as Phase 3 is when a project actually achieves mainstream exposure.
– Phase 4, Post Big Bang : This phase refers to all time frames beyond Big Bang, and thus is dissimilar for every project.
We could also distinguish each phase based on stylized facts as in the table below.
Table 1 – Stylized Facts for Four Phases of Web 3 Marketing
Most numbers are self-explanatory, but we want to point out two things. One, although we filled out 7 days for the Big Bang phase span, a Big Bang event is actually a one-time event and we only configured its time span to be 7 days artificially, as we were averaging interaction counts. Two, the Post BigBang phase is pretty much empty since all projects differ radically in this phase, so it is impossible to generalize completely different features into common principles.