Twitter Data Analysis: An Investor's Perspective
This is a guest post by Robert J. Moore, the CEO and co-founder of RJMetrics, a on-demand database analytics and business intelligence startup that helps online businesses measure, manage, and monetize better.
Context & Ripple Effects
Forbes framed Twitter as a metrics story back in February 2009 with its piece on the company's analytical business plan — a platform valued on engagement long before it disclosed revenue. This guest post extends that framing into public view: Robert J. Moore, CEO of analytics startup RJMetrics, runs his own numbers on Twitter's data and publishes them where investors read.
The significance is who is doing the measuring. A BI vendor using its own tooling to quantify a pre-revenue consumer platform turns investor diligence into content — and puts independent estimates on the table alongside whatever Twitter says about itself.
First-order effects
- Investors evaluating Twitter get a third-party, quantified read on its user data without waiting for company disclosure, complementing the February 2009 Forbes framing of the business plan.
- RJMetrics gains direct visibility for its on-demand analytics product among exactly the audience — online-business operators and their backers — that would buy it.
Second-order effects
- Other analytics vendors face a publishing bar: demonstrating capability through public analyses of high-profile datasets becomes a competitive marketing channel alongside paid acquisition.
- Twitter's own future statements about its metrics now get benchmarked against independent analyst estimates, raising the cost of any numbers that don't reconcile.
Third-order effects
- If independent analysts can compute engagement and retention from public data, valuation of pre-revenue consumer web companies shifts further onto measured usage curves rather than traffic narratives — a structural change in how diligence is done.
The trend: Investor evaluation of pre-revenue social platforms is shifting from founder narrative toward independently computed usage metrics.