The daily data deluge can cause fits of analysis paralysis. Thankfully, Dimitri Maex is here to prevent that deer-in-the-headlights feeling that comes from information overload. His Sexy Little Numbers, out this week, explains how to focus in on the numbers you need to find--and better use the stats you already have.
Fast Company talked with Maex, who is managing director of OgilvyOne New York, Ogilvy & Mather’s Direct and Digital operations, as well as head of the company's Global Data Practice about why--with a little cleverness--easily accessible data is more valuable than statistical gymnastics, why you're going to hire a data planner, and how all these numbers will restructure your company for the better.
FAST COMPANY: How can you better build data into strategy?
DIMITRI MAEX: If you start from how you want to use the data rather than the data itself, that’s probably the most important thing marketers can do.
So how should data be used?
Let’s start with determining who you should talk to and where you can find them--the targeting question. It’s been proven that targeted communications work a lot better than non-targeted communications.
In the old days, you’d do it by media, by geography, or if they’re little more sophisticated, using demographics. Or even lifestyle, supplement the data with lifestyle data, life stage and lifestyle segments that can be purchased and used for targeting.
With digital, other targeting dimensions have become important. The first is time: Recency is probably the most important thing you can know today. If you are in the market for a hammer today, it’s important for me to know and act on that straightaway.
Second is place, especially with mobile. It’s very important to know where someone is and when they are there, so you can put some context around where that person is.
There are companies that will take it to a whole new level. Last week I was talking with somebody about an Israeli company that now has the ability to target people within a shop, within an aisle, by triangulating the phone signal. We’re also working with another company that I can’t disclose the name of, but to add elevation to targeting. Not only can you know which building they’re in but also which floor they’re on--which in a city like New York is very important. You’re going to get more and more granular, you’re going to find out exactly where people are and tailor your message based on that.
But what if you don’t have those resources?
A lot of data is out there for free. I’ll give you an example--this was for Caesars Hotels. We did an analysis with them for their Paris Hotel on the strip in Vegas. We wanted to know why people were coming to the hotel and what they actually liked about it.
What we did was very simple. We went to TripAdvisor, we had a team run through all the four- and five-star reviews for the Paris Hotel and categorize what people were actually liking about it. What we found is the thing they liked the most were the views of the strip.
We changed the advertising copy to have it include “fountain views” and “views of the strip,” and we also changed the landing page of the Paris Hotel to feature those views. Doing those very small changes increased the return by 20%.
That’s a great example of the wealth of information that is out there. You don’t need a complicated statistical techniques to analyze it, you just need to use it in a smart way.
A term you use in the book is a math marketer--what is that?
A great model for what a math marketer actually is what they in the U.K. call “data planners.” They’re not necessarily mathematicians, because those would be analysts, but they’re people who like data and know how to use it. Funny enough, that function doesn’t exist in the U.S., but we’re going to need more of those people.
We’re going to need the statisticians, that’s for sure, but a lot of that is going to get automated, it has to, there’s not enough people to crunch the numbers. What we need more of are the people that can take the insight from the data and then use that in their everyday decision-making. That’s the closest to what a math marketer of the future will be.
If you were an entrepreneur, how would you improve the way you work with numbers?
You need to build a level of data literacy in your company. Whether that means you’re going to analyze the data yourself or outsource depends on the kind of business you are.
There’s a continuum from the data itself to the insight that drives the decisions. We start with the latter. I would start with people that know how to make decisions based on data, and then move back to people that know how to extract insights out of data, and then go all the way back to the analysts and the number crunching and the data capture. I would start with hiring talent that is as close to the decision as possible and then work my way back that way.
The biggest challenge has always been the way you use data on a day-to-day basis to make decisions. I think that a lot of businesses are struggling to be agile, they’re still stuck in a linear planning process that starts in the third quarter, leads up to a plan for the financial year, and gets revised, if you’re lucky, four times a year. That doesn’t really work in a world where you are constantly drinking from a firehose when it comes to information of why people are buying (and) what they’re buying.
So how do you build a company that can move with the data?
What we’ve found with clients is that you end up putting together smaller interdisciplinary teams where the folks that crunch the data sit together with the people making the media decision or the creative decision. And they sit together in small teams that are collocated, because informal communication is a lot more important, since you don’t always have time to set up meetings.
What data does is it gives you a lot more inputs--in real time. You just need to change the way you work so you can react to it in real time. You can’t stick to a quarterly plan anymore if you’re getting feedback on day two that the quarterly plan is not the right plan anymore. You’ve got to course correct and you’ve got to continually do so. And that’s often actually the hardest thing.
How do you better work with data? Holler at us in the comments.
[Image: Flickr user Jolantis]