Category: Automation

In 2016 I created a game giveaway community called JustLoot.Me. It began from an idea to build up a gaming audience to promote my own game I had conceptualised a few days earlier (Spawn.world). I started a Steam group and found some giveaway sites which required steam signups to enter giveaways. I found that they display the steam username of everybody who enters these giveaways. Since I knew these users were interested in game giveaways, I came up with a strategy to start inviting all of them to my own steam group through my own automation I created. I ended up getting 200 users on the first day and consistently an additional 200 on each following day. I was giving away popular games (e.g Dark Souls) via a platform called Gleam which allows me to require joining my steam group in order to enter the giveaway. There was also a referral feature which gave them extra chances to win the more users they invited to enter. After implementing this, my growth was exponential and I was growing by 500 users each day instead of just the 200 from the automation. I reached around 20k users within a month. I still have the email list to this day (Gleam allowed me to collect their email) – planning to use it to promote my own game Spawn. I’ll be relaunching JustLoot.Me as a platform to create viral giveaways which other giveaway communities and streamers can utilise very soon.

In 2015, I created automation to grow my instagram account. I quickly gained 5k followers and reached 1500 likes per picture. It made me realise the power of social media and the ability to generate a lot of targeted traffic which can be utilised by brands. I researched influencer marketing and devised a strategy to match brands and influencers together based on niche. In order to do this, I had to collect a lot of data. I create software which extracted instagram profile data including the bio, tags published, follower count, average likes, average comments and even the tags used by the engagers of the profile to determine the interests of their actual audiences. Using this data, I was able to create SQL queries to get targeted lists of users within any niche. This was great, but not optimal – I want to go even further and started researching data analysis and machine learning. I scored the weight of tags used by the ratio of times they were mentioned within an individual’s profile – now for each profile I had a combination of tags and ratios. Using this data with algorithms such as KNN and ALS I was able to create an accurate ML model to infer the niche of any profile in real-time. I created an online ML solution using Apache Spark allowing high throughput and a constantly evolving model via a feedback loop. I ended up developing a SaaS solution to match brands and influencers based on their niche using this model, which was used for highly targeted influencer campaigns leading to better conversions/ROIs.