An interview with Kripa, Data Analyst at worked with twtData to create, refine and target their NFT target audience on Twitter. is a platform for art-lovers to buy, sell and discover new art. Powered by blockchain and backed by the British Journal of Photography, ART3 “presents a series of collections from both established photographic artists and hotly-tipped emerging talent”.

ART3 aims to financially empower artists by turning their art into NFTs and selling them; whilst providing a platform for art lovers to invest in art they love.

Industry: Art Publishing

Company Size: 11-50 employees

Location: London, UK

What is an NFT?: “NFTs – non-fungible tokens – are unique digital assets that live on blockchain technology. They can be music clips, videos, animations, digitized artwork, photos, or even a ticket to an event, such as a movie, that took place at a specific time. What's notable about NFTs is that they prove ownership.” - DPReview

nft-twitter.png Approached Us to Download Follower and Following Data of Hundreds of NFT-related Twitter Influencers

We spoke to Kripa, the data analyst at about why they downloaded large amounts of Twitter data with us and how they were planning on using it.

Key Takeaways

By using twtData, ART3 reached out to a refined audience of influencers and found the target audience they would use for their Twitter ad campaign.

We provided them with:

  • 12.3 million user data
  • Follower and following data of 100 target user accounts

Read below how they did it and why they needed twtData.

3 Ways Used twtData for Their Twitter Campaign

1. Found the Most Frequently Followed NFT Accounts

ART3 used the Twitter follower data we provided to analyse influential Twitter accounts in the NFT niche. They found their ‘Top 100’ NFT influencers and reached out to promote their private viewing.

How did ART3 analyse the data?

Initially, ART3 had received 300 different excel sheets, 12.3 million follower and following data. So Kripa said she used Jupyter Notebook to analyse and clean the data we provided.

Jupyter Notebook is an open-source app that allows you to create and share documents. It’s used for data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and more.

Kripa uploaded the user data we sent her on Jupyter Notebook. Using Jupyter, Kripa was able to visualise and compare the Twitter user data we provided. She looked at how frequently the accounts were being followed rather than the volume of followers they had. These people formed their ‘NFT Influencers’ list or ‘Top 100’ as Kripa calls them.

“Our team was sending out private invitations to people, but considering the people on the internet, we needed to cut that list down drastically,” she said.


2. Searched Twitter Bios for Keywords Related to NFTs and Crypto

ART3 also wanted to target Twitter users who followed the accounts on their ‘Top 100’ list. They aimed to find a target audience that would be interested in buying NFTs.

How did ART3 analyse the data?

ART3 extracted Twitter followers of their ‘Top 100’ list and analysed user bios. They established a list of keywords they wanted to target, such as; NFT, blockchain and crypto. Kripa uploaded the user data on Jupyter Notebook once again and scouted for the list of keywords.

3. Used Following Data of the NFT Accounts to Form Their Target Audience

Since ART3 had already found the main influencers in the NFT niche, they then wanted to target their following. So ART3 provided twtData with their ‘Top 100’ list and asked for the accounts’ following data (Following/Friend data).

How did ART3 analyse the data?

Thanks to twtData, ART3 was able to look into the “Friends” of the influential accounts on their list; the accounts that even the influencers were following. These users were influential for the NFT market. Using Jupyter Notebook, Kripa was able to eliminate duplicates as some of the following data overlapped.

“We determined the friends to be those who are influential to the big players we extracted. Those who our ‘Top 100’ were following are being influenced by them,” Kripa told us.

Why Did ART3 Choose twtData?

After seeing the quality of data we could provide, ART3 consulted us to help with accessing and downloading bulk user data. We extracted 12.3 million users’ data for them.

“If it weren’t for twtData we wouldn’t have been able to shortlist people to reach out to on Twitter because that would have been a painfully manual process. It’s painful enough that we were sending individual direct messages. This helped us shortlist our mavens, the most important people for us.” - Kripa, ART3.

Benefits of Running Ad Campaigns on Twitter

  • Direct reach to specific Twitter users: Upload your own audience by their Twitter handles
  • Pay for performance: When you promote tweets on Twitter, you only pay when you've achieved your marketing objective, whether it’s gaining a follower or increasing engagement
  • Keyword targeting: Target specific keywords Twitter users have tweeted about
  • Targeting your competitor’s followers: Use your competitor's followers to create a lookalike audience
  • Lower CPC rate: Twitter has less competition compared to Facebook, Youtube and Instagram so you’ll spend less

Useful Twitter Stats (via Omnicore Agency)

  • Twitter has 199 million daily active users
  • 63.7% of Twitter users are male, while only 36.3% are female
  • 28.9% of Twitter’s audience is 25 to 34 years old
  • 80% of Twitter users are affluent millennials
  • Twitter is the 6th-ranked mobile app

Challenges of Twitter

ART3 managed to identify the NFT influencers and reached out to them to get people interested in their first NFT drop. According to Kripa, it went really well. If it weren’t for Twitter’s policy on advertising NFTs, ART3 would have advertised to the valuable Twitter audience they extracted using twtData.