We live in a world driven by data and although we probably don’t even understand half of it or don’t even bother to look into it as we should, decisions aren’t taken unless they can be fully rationalized. As Rishad Tobaccowala mentioned in a recent post:
“In marketing we worship the algorithm and its superiority to human decision making.”
He makes a good point. He continues with:
“In the world of media we are so fixated on the plumbing of finding the right person at the right place at the right time that we forget that the interaction we deliver will have to be absolutely right and brilliant not to piss of this superbly well located person at the exact right time. The better the “targeting”, the more important the tone, content and quality of the interaction. Lets think about the poetry versus just the plumbing.”
This reminded me of the conversation between Bill and Melinda Gates during this year’s TED event in Vancouver. Somewhere in this conversation – hosted by Chris Anderson – it’s clear that part of the magic between these 2 people in spending billions of dollars to charity is the mathematical approach of Bill Gates combined with the more tangible, human experience of Melinda with the people involved in the decision. Something they obviously recognize as a necessity in their decision making.
There’s a lot of talk about shareable content and what it means to go viral. This post is in no means a guide to make sure content does go viral, with this post I hope to help people understand why content spreads and if that is really what has happened in the first place. The reason I think this matters is because marketers are judging other brand’s content to understand what worked and what didn’t hoping to replicate, but they aren’t necessarily always looking at the right data. And since I get into conversations related to this topic constantly, I figured it would be well worth sharing this idea of share rate here as well.
Enough for the introduction, I will use online video (ads) as an example, but the theory works for all kinds of online content, although the data sources will be different of course. There are two key numbers that people tend to look at when judging the succes of online video: views/impressions and shares, although I presume that’s already a distant second measure.
Youtube for instance has its own way to rate popularity of video ads, that is explained by themselves rated based on “an algorythm that factors in paid views, organic views and audience retention” – the Youtube Leaderboard. It’s unclear what the weight of each element is, but it’s clear that it’s mostly based around views. It is also the only number mentioned in the rating. As an outsider it’s difficult to judge only on that number what made each video succesful, was it the idea or was it the mediaspent? Knowing what Youtube’s business is about, there’s no need to explain why this rating makes sense for them.
So it’s important to look further. If you as a marketer (or agency creative) want to figure out why something worked views aren’t the best number to look at on its own. Unruly Media created another way to measure video by looking at the amount of time something was shared on social media. The ads chart is sponsored by Mashable, but you can also look at popularity of other video content. If you want to understand why content was spread amongst people it’s probably a good idea to check if it actually spread in the first place.
Here’s where it becomes interesting. The ranking based on shares looks pretty different than the one based on views, if you look at Youtube’s n2 for August for instance, you will see it’s almost impossible to find in the Unruly ranking. So it’s clear, you wonder how content spread? Look at the shares. But that’s not all.
Let’s look at a classic video we all know for instance: Evian Babies. With over 3 million shares that puts it at number 7 in the Unruly Viral Ads Chart of all time. Very succesful, but does that proof it was spread across social and hence a big succes? Not quite. Paid views will also generate shares – paid and organic. So we need to look beyond that. Here’s where the share rate comes in. The best way to judge whether a video was viewed because people shared it across the web is to look at the ration between views and shares. There’s a few ways to look at it, we use [shares/views] as the ratio, some use percentages, the idea remains the same.
Truly viral content such as ‘Dumb Ways to Die‘ or our own ‘Push to add drama‘ will have a share rate in between 1/20 and 1/10 or even higher (meaning 1/9 or 1/8 but you won’t see those number appear much). Evian Babies – to come back to that same reference – has a ratio of 1/40 and the other example I mentioned (Foot Locker – Youtube’s August n2) has a ratio of 1/200. I would reckon that everything below 1/15 (probably) or 1/20 (definitely) received some kind of ad push. The lower the number the more views were generated through advertising (versus organic) obviously.
The share rate on its own doesn’t say much either of course, a high rate with little views isn’t much of a succes. But if you really want to know why a video was seen by so many people then this is the measure you need to look at. Does that mean those other videos weren’t successful? Of course not. Is it wrong the push videos online with media? Ofcourse not. But want to understand where success came from with the little data you can access? Find the video on the Unruly chart (they show both views and shares – makes it easy) and calculate the share rate.
For the record, Youtube also has a kind of share ratio they use in their presentations but it’s not meaning the same thing. They will look at the ratio between paid views and organic views. Again, thinking of their business selling video ads, I makes for them to correlate paid versus organic views, rather than views versus shares as I suggested.
As mentioned in the beginning I used video to explain but this idea of share ratio counts for all types of content and thus should help you analyze success (or not) of others in good way.
(Please note that I only use examples to illustrate a point, it’s no judgement at all about the videos themselves).
Big data is the whole grail of marketing. And yet not many is actually making lots of progress. There’s a good good on that captures well what the state of big data is if you ask me:
“Big Data is like teenage sex. Everybody talks about it. Few do it and they do it in the dark”
As always also here there are the exceptions that prove the rule. Companies like Starbucks or Taco Bells have showcased that they are actually using data to the extreme to help improve their business activities and communications. But in general only little data is being used, it’s something we encounter only occasionally during the marketing activities and also as a consumer I don’t see much return on the fact that people seem to know quite a bit about me.
And I wonder if that isn’t going to become a problem sometime soon. As a tech savvy consumer I know that companies have data on my consumption behaviour, I am very aware whenever I need to give someone personal data and I know that a lot of my online behaviour is public for everyone to see. Because I know, I also expect something in return. There’s a very one on one relationship between filling in a form before being able to proceed to a next stage and in that case you can immediately judge whether handing over that bit of data was worth the return. But that counts also on general consumption, on the data that companies can gather by tracking your behaviour. I know they do and also there, even if less one on one, I expect a return.
I’m ok with my data being used, I want my data being used or even more, I demand my data being used. I think consumers will get ever more aware of the fact that their data is being collected and as a result become more demanding on how they are treated. No more useless questions, seemingly random suggestions, repetitive data collection, … And that makes in my opinion the need for companies to start really using their ‘big data’ even more important. Not just because they indeed can improve their business and communications if they use it right, but because they have to as consumers will start demanding that. So big data is great, but it’s also a big promise!
So if you’re a business owner or a marketing specialist I think you really need to start figuring out which data you currently gather and how you can connect that to make consumers lives better. All in all I believe there are 3 kinds of data we have to think of that are key to improving your marketing & sales efforts:
Form fill: data that you’ve asked consumers to fill in, can be at various types of touchpoints, all data people hand over to you to get something in return
Usage: usage/consumption data from both sales as marketing activities, everything you collect through the customer journey
Public domain: everything people share in public online & offline that relates direct or indirect to your business
So think about it. The companies that are using it are outperforming you and taking a lead and your customers will move to those companies because they are living up to the data promise making the gap even better.
Infographics are the new black. Well maybe not, bet they sure are popular these days. And that’s too bad. It’s too bad because today it seems that every JPG including some data and pictures is an infographic. Every day at least one of those pops up in my reader and I don’t know what the name for those JPGs should be but we sure shouldn’t refer to them as infographics.
“The purpose of visualization is insight, not pictures” Ben Shneiderman (1999)
So in case of infographics, if the visualization doesn’t enable you to have a clearer view on the information/data below, it’s pretty much worthless. Because if it doesn’t do that, it’s just another summation of data. Adding ‘clipart’ to it doesn’t change that fact. Manuel Lima did an interesting exercise to create an Information Visualization Manifesto which is worth reading.
“Form Follows Function. Form doesn’t follow data. Data is incongruent by nature. Form follows a purpose, and in the case of Information Visualization, Form follows Revelation. Take the simplest analogy of a wooden chair. Data represents all the different wooden components (seat, back, legs) that are then assembled according to an ultimate goal: to seat in the case of the chair, or to reveal and disclose in the case of Visualization. Form in both cases arises from the conjunction of the different building blocks, but it never conforms to them. It is only from the problem domain that we can ascertain if a layout may be better suited and easier to understand than others. Independently of the subject, the purpose should always be centered on explanation and unveiling, which in turn leads to discovery and insight.”
Keep on sharing those infographics, but stick to the real ones please. The ones that you can find on sites like +Datavisualization.ch. Everything else is a waste of time.
This is how the Urban Dictionary defines this: “(adj) something that is so baffling only goggles could understand”. I suppose that is how you got to think of Google Goggles, a mobile tool that allows you to take a picture of something to get instant search results based on the content of the picture. Sounds cool, check this out.