Mat Morrison's data-driven social media insights and opinion
For most of the world (China and Russia excluded), Facebook should be seen as having the same role in our marketing communications planning as Google. Together, Facebook and Google are the twin engines that drive discovery for the average web user. Everything else is fresh and interesting, but sometimes we have to prioritise.
A version of this post was first published on Emerging Spaces on 02/08/2013 LinkedIn recently published a strong Quarter 3 Earnings Report. Taking these in association with previous financial announcements, we can plot the following chart. It’s notable that over the past three years, they’ve experienced 9x growth in what they call “Talent Solutions” (services […]
I’ve just spent a happy lunch hour playing with Google Correlate. It lets you enter real world time series data (weekly sales, temperature, or footfall for example) and then tries to correlate it with search trends. This could be extremely useful for planning content or paid search, or just for winkling out that all-important ‘insight’ […]
I’m cold on engagement. Sure, I used to have a planning chart that I rolled out from time to time that said, more or less, “The secret to social media success is to listen, respond, influence and engage,” but when I became a man I put away childish things. In this particular case, it involved […]
I’m probably missing something really obvious here, but I’ve just been looking at the clicks on bit.ly shortlinks embedded in posts on two Facebook Pages, Asda and TOMS. It seems that each can be plotted on really nice-looking curves: Is that so obvious so as not to merit mentioning? Why should it be like this?
In an earlier post I started looking at how I might use R to forecast Google search volume. Now I find the useful `decompose` function, which decomposes a time series into seasonal, trend and irregular components using moving averages. Which produces the following:
This is by way of being a bit of an experiment. I’ve been reading John Foreman’s excellent and fascinating Analytics Made Skeezy blog and came across the Projecting Meth Demand using Exponential Smoothing, in which the protagonist helps a drug lord forecast monthly demand. I was trying to follow along with the spreadsheet, but fell […]
One of our media partners kindly shared a couple of slide decks today with lots of information about some broad audience segments. There were a couple of graphs that looked interesting, and I wanted to grab the raw data. As so often happens, though, the charts had been copied and pasted from Excel, so those […]
Three videos on Facebook Stories from Stamen Design visualise how photos posted by George Takei were shared and re-shared over time. Stamen’s work is always wonderful — this is remarkable. There’s no rubric, but I’m assuming that this is a form of network graph — where each node may itself become the source of new […]
R The R Project (software and manuals) John M Quick’s R Tutorial Series Computing for Data Analysis at Coursera. the R-Podcast Cookbook for R Quick R Perl and R Machine Learning for Hackers (O’Reilly) (££) L Francis: Text Mining Handbook – Casualty Actuarial Society ggplot2 (for pretty charts) Hadley Wickham’s ggplot2 General Tony Hirst’s Ouseful.info
