Affinio

In 2013, Tim Burke and Stephen Hankinson saw an opportunity to use the Twitter platform as a way to seek out new customers and clients for their growing app business. “As we dug deeper, we began to realize that there was a massive amount of intel in a company’s follower graph on Twitter,” notes Burke. “In particular, there were naturally forming communities, and even smaller affinity groups within that set.”

Their observation led them to invest a year analyzing Twitter follow graph data (via the public APIs) and building out core technology to quickly identify and segment these groups. From this work, Affinio was born, and their first tools — Twitter user analysis and segmentation — found early success, placing them third in the Strata Conference Startup Showcase in 2013 and helping them win early customers including Universal McCann and BBC Worldwide. “Our early product had immediate uptake, helping our customers develop a far richer persona of their target audience than they ever had before,” says Hankinson.

Listening closely to their customers, Burke and Hankinson also realized that there were opportunities to make their product even more actionable by integrating the Twitter Ads platform. “Given our core competencies, it was a natural next step to help our customers broaden their reach across the Twitter platform by expanding their customer base and performing highly-targeted direct response campaigns. We witnessed the power of this strategy ourselves, growing our Affinio customer base using our own Twitter tailored audience targeting,” says Burke.

Burke and Hankinson leveraged their existing work to build a collection of content insights and targeting tools to meet the needs of their customers. This required leveraging a combination of the Twitter platform, including Twitter Enterprise APIs (formerly Gnip APIs), Tailored Audiences, and the Twitter Public and Ads APIs. In particular, they focused on:

• Lookalike group expansion. Once Affinio’s system discovers niche segments of a brand’s followers, it uses its proprietary algorithm leveraging Twitter’s API and the Twitter Enterprise Search API to match that profile across all of Twitter’s user base. This increases the user segment that a brand can then target in their Twitter advertising campaign.
• Targeting. The user list is then ingested into Twitter’s Tailored Audience list tool for running both initial campaigns as well as follow-on remarketing campaigns on the Twitter Ads platform. Affinio uses a custom optimization engine to continuously improve its targeting based on the statistical performance of the ad campaign.
• Content recommendation. When building a new advertising campaign, Affinio’s text analysis of Tweets via Twitter Enterprise APIs is able to offer common terms, hashtags, images and content used by that niche segment. These insights are then used to suggest copy that the user should consider to improve campaign performance, knowing that these will resonate with the target audience.
• Multivariate testing. Affinio uses the Twitter Ads API to create multiple simultaneous campaigns with combinations of data-driven creative and copy, and statistically tracks results in real time to perform auto-optimization on behalf of the customer.