Catch the next 100x Token with Data & Analytics

Finding the next moneymaker is difficult. Alex Becker, a pronounced crypto enthusiast has made millions off of finding the next moneymaker (he doesn’t do it anymore since he’s become a hundred millionaire from his personal business) - he uses community sentiment. I’m going to be teaching you a ton more ways to find the next moneymaker to the point where it’ll make community sentiment look infinitesimal(insignificant) in comparison. Also keep in mind, the reason I mentioned Alex Becker and how he doesn’t do it anymore, is to showcase how only intelligent people can successfully pick the next 100x. You’ll know if this isn’t for you once you’ve made 25 token prediction errors, don’t stop until you get there or else you’re giving up too early.

Right off the bat, we’ve got website statistics and data: use similarweb, a website that has visitation data on all the websites in the world - or anything that uses google analytics as it makes all the websites’ statistics public - google analytics also happens to simply be the best analytical tool out there which is why 99% of the websites under the sun use it.


Here’s a quick rundown of how to use this stat to find good communities and projects: look for an upwards trend. If they’re increasing above the industry average of let’s say 18%, and they’re performing at 40% - that’s a keeper. You’re keeping that project in your portfolio. This of course, assumes that the project you’re looking at exists on a website…projects like PEPE don’t really exist on a website - which is why the next statistic exists.


Social Media Trends

Same rules still apply, if they’re growing at a rate of 40%+ a month, that’s a keeper. It’s guaranteed, the project is 100% going to be going up in value, usually somewhere around 1/4th the value of their increases in statistics. PEPE is an anomaly here so we can’t really use it as an example. What you do next is find a way to track it - using socialblade or twitter trends, 2 very prominent data websites that show statistics for almost all social media websites you can think of - including YouTube and twitter. Twitter is a hub for crypto enthusiasts, which is why most projects you can think of tend to operate on there for publicity or marketing. It’s usually effective. Using twitter can never decrease the results, so you might as well use it if you have your own project.

Just a note, but bounce rates matter too. It tells you how long users stay on a website before they leave. This is a stat that is given alongside a webpages’ amount of visits.

Next up, we have discord growth tools. You usually can’t track a discord servers growth without there being a bot in the server that already tracks it, but what you can do is wait for 2-3 days and monitor it yourself. Select your server and list down it’s current member count, come back tomorrow, do the same, and then do the same on day 3. Then take the average amount of members gained and assuming the same amount of growth stays constant for the next 30 days, you should be able to predict the amount of members gained by multiplying the members gained across those 3 days’ average by 30. (Which means you’d have to also calculate the average, super easy stuff, just add them together and divide them by the amount of entries which is 3. The outcome is your average. Multiply that by 30 and then add that result onto the current member count. That’ll be their member count in 30 days from now. Pretty cool right? YES.)

That’s how you see if they’re going to have increases or decreases in token price. I did this with the Algorand discord server and actually noticed a decrease in members which means that they’ll be going down in token price and not up as they’re losing community members - I did this again recently and they were gaining members. Not enough to constitute as legitimate growth as they were only gaining ~100 members every 30 days. That’s a horrible amount of members for a project that big. To me it seems like most of their members are inactive which is also a metric you should look out for. If a server has a ratio of about 1 to 4 for active vs offline members, that’s a pretty damn good server. If it’s something like 1:10, they better be at the millions of members, because member-counts do matter a lot when looking at this statistic specifically.


I will continue this and create a part 2 as I feel it’d be a better use of your time to select whether you’d want to see part 2 or not. If this post receives ~15 likes+, I’d know it’s a keeper and I’ll make a part 2, but if it doesn’t…I’ll probably end the series. These sorts of post take a while to make amidst the research, especially when compared to the standard article. Anyways, I really do hope you guys have enjoyed.



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