With the increasing speed of data processing and the decreasing cost of data storage, the adtech industry is evolving to improve the efficiency and effectiveness of programmatic advertising for all data-driven advertisers. Real-time bidding has led more and more advertisers to leverage DSPs to manage campaigns and deliver media to the right audience right now—with great success—but what’s missing is the context to deliver the right media at the right time.
Imagine a photograph of a woman, between 25-34 years old, enjoying a picnic in Boston Common. We see green grass around her while she sits on a red and white picnic blanket. She’s wearing a blue plaid shirtdress from L.L. Bean, elastic-strap wedges from Talbots, Ray-Ban sunglasses and a necklace from BirchBox.
The photograph shows her with her two friends, and they’re enjoying a bottle of mid-range Cabernet Sauvignon from Seven Hills winery in Walla Walla, Washington, along with fine cheeses from Vermont Shepherd Cheese and Cyprus Grove, all of which she purchased a few hours earlier. Her iPhone is sitting next to her playing background music on Spotify.
Based on this photograph, we know quite a bit about this woman. We can start to tell a story about who she is as a consumer, which kinds of products she might prefer based on her recent purchases, and because we know a few things about her demographics, we can make broader statements about what she might or might not like.
This is the kind of information advertisers have in a demand-side platform, and it is incredibly valuable because it enables targeting in a way that was never previously accessible before RTB. This data unlocks the capability to target the right audience right now.
But behind every person in a photograph there is a much deeper story to be told, a story that can never be captured in a single, point-in-time photograph. The photograph grows stale over time as the woman continues doing more things, changes preferences and learns about new products she loves. Her full story takes up not just a photograph but an entire book, and provides the kind of next-level context that enables much sharper analytics, predictive modeling and more effective digital outreach.
This book is MediaMath’s Audience Platform (think DMP). Because of industry-leading event-level storage, we have more story to tell about an audience. The figurative “book” of MediaMath’s Audience Platform comes with hundreds of new-book-smell pages of behaviors and intentions, and begins with the tools needed to find what you’re looking for, a table of contents.
By searching the table of contents for long-term behaviors, we may learn that our erstwhile picnicker holds a deep appreciation for vintage furniture from Restoration Hardware and, in fact, that’s the only kind of furniture she’s ever purchased. As we continue reading the Audience Platform book, we learn that she uses AirBnB for a trip around Easter every year, that she recently purchased a wedding dress from Maggie Sottero and that she always responds to half-off Labor Day specials from J. Crew.
We may also learn that there are many people who are very similar to her, and many who are not, and we can choose which people to speak to with which message, on which device, at a time relative to her behaviors and intentions. With features like real-time dynamic segmentation, custom attributes, no-spend campaign performance forecasting and the ability to reach customers across devices and by location, advertisers are in control of their digital engagement now more than ever.
In short, MediaMath’s Audience Platform stores and provides access to event-level data that, in aggregate, informs statistical models that will make advertising more effective and more efficient. The DSP “photograph” provides the data to target the right audience right now, while MediaMath’s Audience Platform “book” provides the data to target the right audience at the right time. Advertisers who truly understand the connections between their data, their customers and their brand are already seeing positive outcomes.