Latest Entries »

How to choose your chart type

I recently came across a very good blog entitled ‘storytelling with data’ “ .
Kudos to you Cole Nussbaumer, it’s a splendid blog.

What I want to share with you is a great diagram published back in 2006 by Andrew Abela. .

Choosing a good chart

This is such an easy to follow approach to selecting the right chart for what you wish to portray using data, even if it does include pie charts.


Little Games Big Business

Great infographic about the rise of the mobile gaming industry courtesy of Business
Thanks to Jen Rhee for sharing.

Little Games Big Business
Created by:

E3 2011 report

Hello again

Here’s a little report that I knocked together for the Five by Five blog, on the E3 2011 expo in LA.

Hope you enjoy.


People have different opinions on social media monitoring in terms of its value. What I think it comes down to is whether you love the random and chaotic nature of the data or get frustrated by its inconsistencies, vagaries and lack of absolutes.

What I’ve done is identify a few of those aspects of the data that are perceived to be either positive or negative.

Positive Side

People’s authentic views: what you find on mining this data are people’s beliefs about things. It could be brands, customer service or how to make a cup of tea, but they are real.

Unfettered by constraint or control they’re not answering a researchers questions, they’re not responding to a list of product imagery. These people have been moved, for whatever reason, to issue an opinion.

Instantaneous reaction: comments, opinion, spleen-venting and joy are often generated immediately as a result of an experience – good or bad – without time taken to ponder the consequences.

People share: social media is about sharing and so not only can you see how far a story, piece of content or trending topic has spread but also its origin and drivers.

Relevant associations (challenging competitor sets): we always recommend setting results in the context of a competitor, particularly when we undertake large-scale retrospective social media monitoring pieces. It’s always interesting to see who the brand thinks their competitors are in comparison to how customers view the market.

Negative Side

Not geographic specific: because of the nature of the internet it is largely impossible to tell the country of origin of a post or mention. Data of that nature is often collected in places like Facebook and forums, but is not publicly available. Twitter users can include location in their bio, but it is not to be trusted.

Not easy to identify demographics: As with geographic location this sort of data is collected by Facebook and some forums, but is not publicly available. So trying to guess the sex, earning power or occupation of someone posting on a forum or writing a Tweet is nigh-on impossible.

(Some social media monitoring tools are now presenting data along these lines but the volume of data on which its based is so small as to render it unrepresentative, and the less said about its reliability, the better).

People don’t express themselves clearly: This could be due to many things. The scourges of natural language processing – irony and facetiousness – are obvious examples. It could also be haste, lack of grammar or use of slang or colloquialisms.

People gossip: As in offline life, people don’t always have something original to say and may merely be repeating what they’ve heard from someone else, or seen in a newspaper or on the television. Separating this repetition of mainstream media from the data in order to find consumer insight or naturally occurring trends is hard work.

Dirty data: No matter how well a social media monitoring tool claims to be able to present the user with clean set of data there is always work to be done. Whether it be excluding a site that has a linked story on every page or removing forum posts because a very active member has used a product name in their footer, there always has to be a certain lack of confidence in the volumes of data.


Social media didn’t evolve in order to provide marketers and brands with the hugely powerful (positive) sources of data listed above. It’s not an analytics form that’s driven by data and numbers in the way that email, web and dm analytics are. It’s much more about language and opinion and reflects the nature of social media, and more broadly, the internet itself.

We can use tools to focus on a specific area of the massive amorphous whole of the web, but these will only ever do half the job for us.

When you’re talking about undertaking social media monitoring for a popular brand there is always too much data to go through it all. You have to clean as much as you can and then read as much as you can in order to understand their true meaning and their value

Language, in this context, is both a conduit and a barrier and so social media monitoring sits on the borders between analytics and research.

Public speaking

Greetings and salutations.

I was on the speaking circuit earlier this week at the SocialPR 2011 event (#socialpr) on Monday 28th and at the TFM&A event at Earls Court 2 on Tuesday 1st.

The deck that I presented at both events was titled:  The importance of social media monitoring in the video games industry. A slightly unwieldy title I admit. (The alternative title was ‘What can data tell us about zombies, aliens and headshots’).

The deck was put together by me and Al Gray (@alasdairgray) and focuses on trying to understand how different content, released as part of the promotional activity for a video game, can affect the levels of interest and conversation around a title. I explained how different video games require different types of content to promote them based on the genre and format of the game. I also looked at the timing of content and the impact of DLC as part of the video games lifespan.

Big thanks goes to Seb (@sebhempstead) and Giles (@joodoo9) at Brandwatch for inviting me to share the stage with them, and then allowing me to take over, and also to Mike Phillips (@imjustmike) and Chris Buckley (@buckers) for their input on the deck.

The people at Influence People were also very friendly and supportive which was great because I’ll be honest with you, it was the first time that I’d done something like this and I was more than a little nervous. I needn’t have been, because I knew the subject matter back-to-front, but I’m a gabbler usually and have to concentrate hard on speaking slowly and clearly.

Once I’d got over my stage-fright (or The Chokes, for all you Boosh lovers) I think I did ok. People at the event on Monday said they enjoyed the presentation or that they found it useful. So you can judge for yourselves,  the results can be seen below:

There is also a link to a video, streamed live on the day, below (I appear halfway through the first video):

There was a live Twitter feed at the event, the reception was very positive, and I’ve got a few new followers as a result of my appearance.

Post-event I was approached by Redwood, who run the Royal Mail Media Centre, and they are going to be hosting the deck as part of a special piece they’re doing on social gaming.

You can’t treasure it if you don’t measure it

Whatever you’re doing campaign wise online, you must ride the learning cycle to success. You must be able to track and measure the right outcomes of your activity in order to understand whether you’ve been successful and have delivered the objectives that reflect the vision.

But before you can get on the cycle you must learn to ride it and this means taking care of the first four steps of the campaign cycle (see diagram below).


Ride the campaign cycle to success!

Once the activity has been planned, this is the time to sit down and agree the Key Performance Indicators (KPI). What does success look like for this campaign? How will I tell if my activity has been successful? Key Performance Indicators are derived from the objectives. For example, an objective for a social media campaign could be to foster conversation.

Then, for each objective, there will be at least one or more KPIs.  These are the tracking and measurement of one or more tangible performance metrics which enable us to tell whether the objective has been met.

For instance, the KPIs for fostering conversation would be

  • share of voice of the brand against its competitors
  • conversation reach
  • amount of conversation

We would measure conversation reach by the number of followers a brand has on Twitter, or the number of ‘Likes’ a Facebook page has. These are our metrics. Before the campaign begins all the tracking is put in place to ensure that metrics are tracked. During the campaign, activity can be tweaked and optimised to maximise performance and then once the campaign is finished it’s time to assess the results.

The learning cycle ensures that the results against the KPIs inform the next activity undertaken. By examining the results and looking for areas improvement we ensure a continual optimisation of our processes.

The ongoing monitoring cycle ensures that results are also used to inform the objectives and plans for the next campaign, again ensuring optimisation of processes.

The results can also be used as benchmarks for the level of success of future campaigns of a similar nature.

So that is the campaign cycle containing the ongoing monitoring cycle and the learning cycle. It is like riding a bike; once you’ve mastered it, you never forget.

What do you think? Are you currently employing learning cycles in your social media monitoring activities?

Getting started

Big moment. First post. I’ve done all sorts of different data analysis in the marketing world in the last 5 years (web analytics, dm, email, digital media, cost per vacancy) but the part of it that’s really got my drooling is social media analytics and that will be the focus of this blog. Hopefully you’ll find it interesting and enjoyable.

I think there are two reasons why I find it so fascinating:

  1. Its relatively new (particularly in comparison to direct marketing) so we can write the rulebook, we can define and drive development of the technology that enables the measurement.
  2. The data is alive. Its no just rows and rows of data about spending patterns (which I expect is actually quite awesome), its people’s words, their views, their opinions and feelings. For me that drives the challenge beyond data analysis and into the realm of sociology, of psychology and linguistics.

Oh, and there might be a few tanks and Star Wars references thrown in to, for good measure.