Twitter's Analytical Business Plan
The popular messaging service plans to make money from selling analytics and other services. — BURLINGAME, Calif. - Sitting inside a conference room at Twitter, BlackBerry in hand, Kevin Thau is all business. — In his first interview since taking charge …
Context & Ripple Effects
By early 2009 Twitter had proven commercial value without earning any of its own: Dell had already attributed $1 million in revenue to the service, yet Twitter itself had no business model. Kevin Thau's first interview since taking charge of the company's business side was the first concrete answer — sell analytics and other services to the businesses already getting value.
The plan moved quickly from talk to product: within months Twitter committed to rolling out commercial accounts that same year, and third-party analysts were already framing Twitter's firehose data as an investable asset. That analytics-first foundation is the seed of the revenue model the company would spend the next decade building out.
First-order effects
- Twitter shifts from a free consumer utility to a company with paying customers: businesses become the first buyers, purchasing analytics about how their tweets perform rather than ads or subscriptions.
- Kevin Thau's team owns a new mandate — turning the data exhaust of public conversation into sellable products, the first time Twitter articulates who pays and why.
Second-order effects
- An entire ecosystem of third-party Twitter analytics and measurement vendors springs up around the same data, forcing Twitter to decide whether it competes with or licenses to the tools built on its platform.
- Commercial accounts establish that businesses will pay for access and insight, giving Twitter pricing precedent and customer relationships it can later extend into advertising and subscription products.
Third-order effects
- The pattern — monetizing a platform's proprietary activity data as a product line in its own right — becomes a structural template for social platforms, where the audience is the product but the data about the audience is the second business.
- It also foreshadows Twitter's long-running struggle to diversify: a dozen years later the company was still building non-ad revenue through subscriptions including paid TweetDeck and tipping to ease dependence on ads, suggesting analytics alone never fully carried the model.
The trend: Platforms increasingly treat their own behavioral data as a standalone revenue stream alongside advertising — one Twitter helped originate and has spent over a decade trying to scale into durable, diversified income.