Many organizations rely on a siloed approach to advertising, which is when a brand or agency secures various vendors to accomplish different roles. This might include a data-sourcing solution to track the purchase history of consumers, a data management platform to collect and analyze source data, a demand-side platform to identify and purchase advertising opportunities, and a measurement solution to evaluate success and gather insights for future initiatives.
It’s possible to use a different vendor for each step of an ad campaign, and you could even use multiple vendors within each step. There are even aggregation vendors that enable brands and agencies to combine datasets and create a proprietary “best in breed” engine suited to the brand or agency.
But it’s important to remember that not all vendors handle data the same way.
Your mobile ad provider might rely solely on device IDs, meaning all of the cookie data you’ve collected from users becomes useless. It’s also unrealistic to imagine that all of the data you get from providers will map perfectly to your DMP or DSP when it’s time to execute a campaign. These types of data leakage mean that your insights become less precise as you go down the funnel, and your audience might end up being much smaller than you intended. If each vendor brought into the fold means losing consumer targets and data, your ability to scale is threatened.
Although it’s theoretically possible to have zero data loss, you’ll still have to transmit data from one provider to the next — a step that could take days or weeks. If your data tells you your customer is in the market for a new product, he or she has already bought your competitor’s offering by the time your digital advertising machine is ready to react. If this scenario sounds all too familiar, it’s time to consider a closed-loop system.
With a closed-loop system, data signals feed into targeting analysis, which feeds into the ad placement engine and then into measurement analysis. Then, campaign insights are sent straight back into the data signals without data loss. With each part of the engine communicating fluidly, you can begin to tap into real-time analysis and unlock insights as they’re happening.
For example, if a customer is exhibiting signs that she’s in the market for a new SUV, you could be advertising test drives for her favorite models with all the features she considers the most important the day after the system picks up those signs. But if you have to wait for the data to move from vendor to vendor, it’s hard to act quickly on this information. So the opportunity to market to a customer who is ready to buy is lost.
A unified intent engine allows for holistic data analysis instead of a siloed approach. Each individual part of the intent engine, including DMP and DSP technology, is designed to work in conjunction with the rest. Because the intent engine serves as both DMP and DSP, it allows you to connect consumers with ad content in milliseconds.
The best intent engine should be powered by machine learning, unlocking immediate insights into your customers that constantly evolve as their behaviors change. With AI optimization, datasets and targeting improve not only with each new campaign, but also while the campaign is active. When campaigns can course-correct while they’re still in progress, you see improvements based on your goal, whether it’s online engagement, sales, or foot traffic.
Advertisers should also seek a provider with holistic data management and analysis. Too many marketers rely on a siloed approach because it’s all they know, but data leakage and a slow reaction time hamper their efforts no matter how impactful their campaigns might be.
With a unified intent engine, you gain access to powerful tools that were designed to work with the rest in a system. Thanks to AI optimization, you can make course corrections while campaigns are running. And at their conclusion, the insights are redeployed to inform future campaigns.
Learn more about how Valassis Digital can help you personalize messages through the Valassis Consumer Graph that provides predictive consumer intelligence.