Dimitri Maex is managing director at OgilvyOne, author of Sexy Little Numbers and data whizz. Laura Swinton caught up with Dimitri to find out why numbers are big news right now.
LBB> Why did you decide to write Sexy Little Numbers and what has the feedback been like?
DM> It wasn't a conscious decision, it sort of happened. A couple of years ago I wrote a little internal paper on analytics for Ogilvy. Most people join advertising agencies to get as far away from data as possible, (well that used to be the case), so I wanted to write something very simple that would get people excited.
I then started writing a blog and it kind of rolled into a book. Most books are written for people who work with data on a day-to-day basis, and they get pretty technical very quickly. On the other hand books written by journalists can be easier to read but are very high level, so you don't really get an understanding of how it all works. I think analytics has become a lot more important; most people in marketing realise they need to understand it a little bit better, but the subject can be quite intimidating. So what I wanted to do was write a book that really demystified the general concepts of the subject.
LBB> As you say, data is big news right now and there are probably many people who would like to get their heads around it but are also quite daunted. If I were in that position, what advice would you give? Where would I start?
DM> I think a lot of people start in the wrong place. They tend to start with the data and technology. You should start by considering what it is that data can actually do for you. That’s why I wanted to write a book that didn't start with technical information, but with questions that could be answered. I guess that’s why the book is structured the way it is. So I think if you’re very clear about what you want data to do for you, you won’t get lost so easily in all the software and technology.
LBB> Traditionally there’s always been a bit of a divide between the research and creative elements of advertising and a perception that numbers and science are not ‘creative’. Do you think that’s still the case?
DM> It definitely used to be that way. Analytics was almost seen as a threat to creativity. But they were the attitudes of the older, more traditional creatives. I think the younger creatives, in my opinion, have become more open to the power of data and analytics. You’d be surprised - actually you shouldn't be - at how many of them crave information around what works and what doesn't. They like the fact that they can get real feedback on their work.
If you play it right being very rigorous with data, it can buy you creative freedom when you’re working with clients. In the digital world you can try things out really quickly and it doesn’t have to be expensive.
LBB> The book focuses on quantitative data, but is there still space for the more traditional, qualitative approach that many agencies still depend upon?
DM> I do think there is still a role for something like a focus group. If performed in the right way focus groups can be great for brainstorming. It’s not the best tool to determine someone’s opinion of a piece of work, but they can be very useful if you are trying to generate ideas. But I think you’re absolutely right, a lot of strategists and planners look mainly at qualitative data. It really depends on the person – I think the younger generation of planners will be a lot more open to quantitative data.
That’s changing now. In fact it’s changing quite dramatically, actually. The advertising community is becoming more technical because that’s where the real shortage is.
LBB> You mention this ‘talent crunch’, a shortage of people with the appropriate combination of numerical, social and creative skills. How do you think adland can address that? Are there different places that they could recruit from?
DM> We’re already looking for different places to recruit from, but we’re up against stiffer competition – we’re fishing in the same talent pool as IBM. I still think that being able to work in a place where data comes together with the creative process is a very interesting proposition, and that’s what we can offer.
I find it really interesting because back in the day, when I first started, nobody was really interested in data. It was a real uphill battle – at least that has changed.
LBB> You talk a lot about privacy in your. What do you think people’s attitudes are to data capture? Are these attitudes changing?
DM> It’s a huge issue. When you ask people about giving data, their first question is always about privacy. What we’ve seen from some initial research is that people definitely are concerned about it.
On the other hand they are open to sharing data if they are clear what they’re getting back from it. However that’s not always obvious. It’s important that companies really start demonstrating to consumers how data can actually create value and how the consumers can get something out of it as well. There are some obvious successes, such as the Nike Fuel Band. People don't mind giving data for because it’s very clear what they are getting back from it.
LBB> Perhaps that’s where brands like Facebook have become a bit unstuck. When the rules around data usage start changing and the truth comes out in dribs and drabs, people say ‘this isn’t what I signed up to’.
DM> I think companies definitely need to be transparent. And you need to think about the data you create. It becomes tricky where that link between the data people are giving and the value they’re getting back is not obvious. In the digital media landscape, people are consuming tonnes of content for free online; the quality is getting better and better and they don't have to pay for it. The only reason that content is free is because it is being sponsored by advertising. The only reason advertisers invest money in digital is because the advertising works. The advertising works because it is fuelled by data. If you take away the advertising dollars online, then people will have to pay for content too. Very few people realise there is a connection between data that is being collected about them and the ability to consume free content. I think as an industry we need to explain it a lot more explicitly or we risk jeopardising the economic model of the Internet and all of the content we are consuming there.
Some companies are talking about using data rather than ‘pay walls’. If you go to the Wall Street Journal, for example, you get a first paragraph of an article and you have to pay to read the whole thing. In the future it may be the case that opting in to having your data collected will give you access to the full article.
Right now, a lot of the conversation is focused on fear and the negatives of data collection. I think it should become a much more balanced conversation that analyses not only the downsides but the upsides of data collection too. I think it’s going to change. I think what we’re seeing now is just a reaction to some of the abuse that has happened over the last couple of years. The technology was new, poorly regulated and a little bit wasteful. I think some regulations will come in and I hope the conversation will turn into a more positive one.
LBB> That seems to tie in with the growing pressure for brands to become more transparent and to live up to their promises. One might imagine that the more trustworthy a brand is, the more people will be inclined to give them information. So perhaps the pay-off to being a more transparent brand is that the quality of the data you can collect improves… What are your thoughts?
DM> I think in the future that will definitely be the case. I think we’re going to need to shut down the practice of collecting data without the consumer’s knowledge. People want to have control over what they disclose.
But I think you’re right, companies that are trusted will obviously become the companies that consumers trust with data.
LBB> Your section about text data was quite amusing – when you talk about companies clamouring to use text analysis algorithms when often common sense and just reading online comments can be far better. How long do you think it will be before text-parsing software is as effective and incisive as human readers?
DM> I don’t think you’re ever going to be able to create a computer that is able to interpret that text better than a human. But computers are generally quicker and can handle vast amounts of text in a way that we can’t, so there is a role for both.
LBB> What’s been the smartest example of data underpinning a really effective, creative campaign that you’ve seen recently?
DM> The interesting examples arise from using non-obvious data. This concept of ‘new data’ is fairly interesting, and is all about finding alternative data sources that are currently not really used to power campaigns.
In the future your competitive edge won’t come from the analytics but will depend on whether or not you have data that your rivals don’t.
As an example you can use search data in really smart, strategic ways. We did a project for an Infant Nutrition client which used search data to really understand the journey a mother goes through from the pre-natal stage all the way through to her child becoming a toddler. As you raise a child, hundreds of questions crop up. Each stage of development throws more curve balls, so mothers go online to search for answers. You can use search data to understand what they are looking for. We used that information to map out a typical journey, creating an entire timeline of mothers’ information needs.
LBB> Perhaps the flipside of the growing enthusiasm of data is that one can easily become blinded by science when faced by lots of impressive but impenetrable numbers. What tools can non-experts to read data reports critically and cleverly and to filter out useful information?
DM> I think what we’re going to see is analytics software with interfaces that non-technical people can use. We can use technology to pull out and highlight some interesting information for non-experts. I think that’s a really important trend with a lot of growth opportunity and you are going to see a lot more happening. We don't have the people with the statistical knowledge to be able to publish and interpret data so we will need software and automation to help people interact with data in a more intuitive way.
Google Analytics is a great example. It’s very intuitive, you can create certain rules to alert you to trends on your website and it’s really useful for non-technical users.
I think data visualisation can help, though I think at the moment we’re only scratching the surface with that. It’s a very hot topic but I still think a lot of the examples are fairly gimmicky and don’t have much utility. There’s a lot of room for improvement there.