“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Chris O’Hara, vice president of strategic accounts at Krux.
We’ve been hearing about big data driving marketing for a long time, and to be honest, most is purely aspirational.
Using third-party data to target an ad in real time does deploy some back-end big-data architecture for sure. But the real promise of data-driven marketing has always been that computers, which can crunch more data than people and do it in real time, could find the golden needle of insight in the proverbial haystack of information.
This long-heralded capability is finally moving beyond the early adopters and starting to “cross the chasm” into early majority use among major global marketers and publishers.
Leveraging Machine Learning For Segmentation
Now that huge global marketers are embracing data management technology, they are finally able to start activating their carefully built offline audience personas in today’s multichannel world.
The magic of data management, however, is not just onboarding offline identities to the addressable media space. Think about how those segments were created. Basically, an army of consultants and marketers took loads of panel-based market data and gut instincts and divided their audience into a few dozen broad segments.
There’s nothing wrong with that. Marketers were working with the most, and best, data available. Those concepts around segmentation were taken to market, where loads of media dollars were applied to find those audiences. Performance data was collected and segments refined over time, based on the results.
In the linear world, those segments are applied to demographics, where loose approximations are made based on television and radio audiences. It’s crude, but the awesome reach power of broadcast media and friendly CPMs somewhat obviate the need for precision.
In digital, those segments find closer approximation with third-party data, similar to Nielsen Prizm segments and the like. These approximations are sharper, but in the online world, precision means more data expense and less reach, so the habit has been to translate offline segments into broader demographic and buckets, such as “men who like sports.”
What if, instead of guessing which online attributes approximated the ideal audience and creating segments from a little bit of data and lot of gut instinct, marketers could look at all of the data at once to see what the important attributes were?
No human being can take the entirety of a website’s audience, which probably shares more than 100,000 granular data attributes, and decide what really matters. Does gender matter for the “Mom site?”Obviously. Having kids? Certainly. Those attributes are evident, and they’re probably shared widely across a great portion of the audience of Popular Mom Site.
But what really defines the special “momness” of the site that only an algorithm can see? Maybe there are key clusters of attributes among the most loyal readers that are the things really driving the engagement. Until you deploy a machine to analyze the entirety of the data and find out which specific attributes cluster together, you really can’t claim a full understanding of your audience.
It’s all about correlations. Of course, it’s pretty easy to find a correlation between only two distinct attributes, such as age and income. But think about having to do a multivariable correlation on hundreds of different attributes. Humans can’t do it. It takes a machine-learning algorithm to parse the data and find the unique clusters that form among a huge audience.
Welcome to machine-discovered segmentation.
Machines can quickly look across the entirety of a specific audience and figure out how many people share the same attributes. Any time folks cluster together around more than five or six specific data attributes, you arguably have struck gold.
Say I’m a carmaker that learned that some of my sedan buyers were men who love NASCAR. But I also discovered that those NASCAR dads loved fitness and gaming, and I found a cluster of single guys who just graduated college and work in finance. Now, instead of guessing who is buying my car, I can let an algorithm create segments from the top 20 clusters, and I can start finding people predisposed to buy right away.
This trend is just starting to happen in both publishing and marketing, and it has been made available thanks to the wider adoption of real big-data technologies, such as Hadoop, Map Reduce and Spark.
This also opens up a larger conversation about data. If I can look at all of my data for segmentation, is there really anything off the table?
Using New Kinds Of Data To Drive Addressable Marketing
That’s an interesting question. Take the company that’s manufacturing coffee machines for home use. Its loyal customer base buys a machine every five years or so and brews many pods every day.
The problem is that the manufacturer has no clue what the consumer is doing with the machine unless that machine is data-enabled. If a small chip enabled it to connect to the Internet and share data about what was brewed and when, the manufacturer would know everything their customers do with the machine.
Would it be helpful to know that a customer drank Folgers in the morning, Starbucks in the afternoon and Twinings Tea at night? I might want to send the family that brews 200 pods of coffee every month a brand-new machine after a few years for free and offer the lighter-category customers a discount on a new machine.
Moreover, now I can tell Folgers exactly who is brewing their coffee, who drinks how much and how often. I’m no longer blind to customers who buy pods at the supermarket – I actually have hugely valuable insights to share with manufacturers whose products create an ecosystem around my company. That’s possible with real big-data technology that collects and stores highly granular device data.
Marketers are embracing big-data technology, both for segmentation and to go beyond the cookie by using real-world data from the Internet of Things to build audiences.
It’s creating somewhat of a “cluster” for companies that are stuck in 2015.
Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter.
