Three Facebook Metrics You Didn’t Know You Had

23 Monday Jan 2012

Posted by in Social Media

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Lots of people have posted guides to Facebook‘s “new” insights. These are all generally pretty good explanations of the metrics that Facebook provides standard in their Insights tool (both online and in the Excel downloads). If you need an introduction to Facebook Insights, check them out. But if you’ve already been using the new Insights for a while, did you know that there are three key metrics that don’t come “out of the box” from Facebook? You can only get them with Excel (or a calculator, if you have free time). Here they are, how important they are, and how to create them:

  1. Frequency.This basic metric is the third part of the classic advertising trinity (Impressions, Reach, and Frequency). Frequency tells you how often the average Fan saw your content (posts, check-ins, ads, etc).Increases in average Frequency show that your audience sees your message more often. Continue reading »

Turning Tweets into Ratings Gets You…Nowhere

05 Thursday Jan 2012

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In his post “How to Turn Tweets Into Ratings Points“, Zach Rosenberg outlines a formula to convert tweets into some form of T.R.P. equivalent. It goes something like this:

Number of Tweets x Average # of Twitter Followers x % seeing the tweet x Frequency / Twitter Universe x Premium (due to self-selection) = T.R.P..

Zach’s “real-world example” uses the following: 200,000 Tweets about a TV show result in a T.R.P. of 65 points. There’s a catch with this, though – Most of Zach’s “formula” consists of assumptions: Continue reading »

Applying Crawl-Walk-Run to Owned Media

28 Tuesday Jun 2011

Posted by in Basic Principles, Web & Mobile

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Crawl-Walk-Run is a framework for digital analytics and performance optimization that I’m working to make approachable for people across experience and digital disciplines, and comprehensive enough to cover the spectrum of business goals involved in digital marketing and advertising.

At a high level, each stage differs in terms of:

  • Questions answered at that stage (what, why, how)
  • Time perspective (past, present, or future)
  • Level of optimization applied (reactive, tactical, or strategic)
  • Activities used (monitoring, testing, predicting)

Let’s see how this applies: Continue reading »

A Crawl-Walk-Run Model for Data Analysis

13 Monday Jun 2011

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I’ve seen a lot of discussions recently about analytics, competing on analytics, and how to approach analytics. Certainly, in the digital age we face a deluge of data, and need software and people to process it, structure it, analyze it, understand it, and act on it. For people like me (who live, eat, breath, and sleep data), it’s an incredible opportunity and amazing time.

For everyone else, it’s a little overwhelming.

Not only is there so much data and “analysis” out there that it’s overwhelming, a lot of it is contradictory. This makes everything even more overwhelming, as now everything has to be compared to everything else. Since every piece of data came from a different team with a different perspective and a different goal, it’s like comparing apples and oranges.

Maybe there’s a different lens we can use to look at this, one that puts everyone on similar playing fields and makes it easy to understand where we (and they) are in their analytics progression: Continue reading »

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