With Databox, Achieve moved from a manual process of tracking data to one that allows them to motivate their team, eliminate room for errors, and ultimately make improvements.
Case Study | Jul 23
Allison Frieden on November 20, 2020 • 5 minute read
Online entertainment is a hard niche to break into with YouTube, virtual gaming, social media, and more.
Before Covid-19 hit, the average daily time spent online was 12 hours and 24 minutes. Now that we’re all working from home, that number has increased to just over 16 hours a day.
One of the harder online entertainment areas to get insight into are memes. We use them every day in everything from Facebook posts to emails, Twitter, and Instagram.
While many companies today use memes throughout their internal and external communications, most use a third-party app to build them out.
The first of its kind, Neverthink.tv, was founded in 2016 and sought to develop a community specifically for generating and viewing memes.
Today, users of their platform can spend hours flipping through memes much like the television channel surfing filled days of millennials and previous generations’ youth.
To learn more about how Neverthink uses its data, we sat down with its VP of Growth, Aapo Kojo. As a semi-new employee, starting just a year ago at Neverthink.tv, Kojo sits at the intersection of product and data science.
Before using Databox, the Neverthink team was utilizing various analytics tools from external vendors. All of this, of course, was costly, time-consuming, and difficult to manage.
Here are the core challenges Neverthink faced:
“Curators needed a way to balance out quality and quantity with regards to the content put out,” said Kojo.
Bringing everything together to see the larger picture is always a challenge for any company. But when it comes to technology that touches nearly every part of our professional lives, it’s even more important to have a comprehensive system for better implementation.
“We had different analytics tools and were in the process of building out our own,” said Kojo. “Because of this, we knew we needed to find a way to streamline everything into one central view. Neverthink also needed a way to quickly create Databoards that visualize the data regardless of the tools we have.”
After demoing various products, Neverthink knew it had the right solution with Databox. Neverthink gave ten members of its team access and then connected its Google Sheets, Amazon Redshift, Instagram Business, and Facebook Pages.
In addition to these data sources, Kojo’s team also takes advantage of Databox’s Query Builder for Amazon Redshift and Data Calculations for targeted analysis. Neverthink also looped together its dashboards, including social media, high-level overview, Android App, and Instagram traffic.
For Neverthink, there are two main uses – product performance and content performance. On the product side, Kojo and his data scientists create dashboards based on the current features being built. For content, there’s a strong focus on how the Neverthink channels, creators, and marketing are performing.
Here’s how Databox solved Neverthink’s challenges:
“Databox allowed us to do a deeper dive into branding the content,” said Kojo. “We do this through creating social media pages for each of those accounts to help bring audiences to the Neverthink app.”
At the end of the day, Databox has significantly impacted the way Neverthink’s team operates at its core. The all-in-one system gets information quicker to its data scientists, opens up collaboration between departments, and improves the user experience.
Here are the results from Neverthink’s Databox use:
“Data scientists can now more easily work with other members of the Neverthink team who aren’t as tech-savvy,” said Kojo. “Databox just simplifies everything.”
“We can now follow global metrics of the company,” said Kojo. “Databox is giving us indications of our trends and showing us where we need to improve. We’re still a startup, so the direction of where our company goes could change in the next month or so. With better access to a straightforward system, I have no doubt we’ll be able to have great flexibility.”
“The biggest cost we have is the product development,” said Kojo. “So, whenever we can make the process faster, it’s an immediate cost saving. The more experimenting we can do throughout the year, the better our team performs. Databox has allowed us to do much more product experimenting.”
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