Marketers and the Future of DMP Insights

By: Hannah Chapple
Advertisers, agencies, and publishers are swimming in data.
They have so many data points, from a variety of sources, that they are simply
overwhelmed by it all. Website (cookie data), social data, CRM data, you name
it, and they’ve likely got it. Sorting all of this data from various (often
siloed) sources, in a timely and efficient manner is a near impossible human
We all know that the role of a marketer is to reach the
right consumer, at the right time, with the right message. But to do this
effectively, marketers are challenged with interpreting their mass amounts
of data and uncovering actionable insight, at speed and scale.
Interpreting mass
amounts of data is no easy feat.
As the demand for digital marketing and
programmatic/real-time ad buying rises, marketers face more pressure than ever
to target audiences faster, and with laser-precise, data-driven insights. We
know that consumers will only respond to the messages that speak to their
interests, passions, wants, and needs. And in the world of real-time bidding,
technologies only have milliseconds to get that messaging right. And guess
what? These messages cannot be crafted with broad categorization methods like
demographics alone. Demographics as a stand alone are limiting and tell you
nothing about what an individual is interested in, passionate about, or value.
To fill this gap, we have seen marketers seek more and more
data resources. That’s why we see marketers not only trying to make sense of
their first-party data but also second party data (from partners) and purchased
third-party data. Can you understand why marketers are swimming in data? It’s
a vicious cycle. So again, we arrive at our original problem: how can
marketers turn mass amounts of data into actionable insight, at speed and scale?
Are DMP’s the magic
solution in the advertising ecosystem?
To better target potential consumers, many advertisers rely
on Data Management Platforms (DMP’s) to collect their mass amounts of disparate
audience data (including the first, second, and third-party data we spoke
about) and interpret it. In short, DMP’s are cloud-based warehouses used to
generate an audience segment(s) based on patterns and trends set within defined
parameters. The goal, of course, is to deliver high-quality, accurate audience
segments to marketers, and all other players in the advertising ecosystem, like
DSP’s. When placed into action, these audience segments (generated by the DMP)
should result in smarter optimized ads, efficient media spend, and less ad
waste. But is this actually the case?
Marketers are sitting on a wealth of data, with a goldmine
of potential insights to derive from that data. That’s why more and more
companies are investing in DMP’s for their business and are hiring
highly-qualified, expensive professionals to manage them. However, while DMP’s
are used to extract insights, there is still a lot of wasted potential in these
Here’s a quick DMP lesson: DMP’s operate on a ‘hypothesis’
basis. DMP users must set conditions or a query to break down the data sources
and form a specific audience segment they want. For a DMP to work properly
(with speed and accuracy) and know what data to segment or pair, a DMP user
must understand many factors including media, marketing, analytics and of
course data. The DMP will then do its best to match data and form an actionable
audience segment for the marketer to leverage.
For example, a marketer could leverage behavioural cookie
data to build an audience of males in Nova Scotia, over 30 who browsed a car
website on their mobile device. This audience can then be used for ad-buying,
media placement, etc. 
But marketers don’t
know, what they don’t know.
But what does this marketer really know about this audience?
What are their interests and passions, outside of cars, and how can they be
determined? This is why, despite the integration of DMP’s, marketers still
aren’t getting it right. While automated, there is still a human error in how
DMP’s select which data to process and interpret.
Don’t get me wrong; there is incredible value in DMP’s but
there is also an incredible opportunity present. Ultimately, the goal of
leveraging a DMP is to provide a personalized consumer experience by relating
to their interests and behaviours. But marketers are only grasping at the data
that they are currently able to understand. Like I said, DMP’s operate on a
hypothesis basis, contingent on the user’s understanding of the data.
We, as marketers, haven’t even scraped the surface of what
is possible with DMP data. Marketers need a solution that looks beyond
predetermined hypothesis and attributes. Instead, we need a solution that
interprets unsupervised data and can discover the hidden relations and insights
within audiences that marketers don’t yet know.
How do you foresee 2017 shaping up? How will DMP’s evolve?
Share what you think down below: [Read
more on the Affinio blog]

About the Author: Hannah
Chapple is the Marketing & Content Coordinator at Affinio, the marketing
intelligence platform. Hannah holds a Bachelor of Business Administration with
a major in Marketing from the F.C. Manning School of Business at Acadia