Written by Throtle founder and CEO Paul Chachko
Gone are the days when brands and marketers were satisfied with investing thousands of dollars on inflated, household level cookie inventory without the ability to target individuals within a data segment. Today’s marketers are extremely savvy and demand that their customer data is matched both individually and deterministically, so they can execute people-based marketing campaigns.
Just like the direct marketing business decades ago and the email business in the early 2000s, digital data advertising is being transformed, replacing probabilistic matching methodologies with deterministic matching. Deterministic matching uses customer data, such as hashed email addresses or log-in data to recognize individuals on various devices with 100% accuracy, whereas probabilistic matching uses IP addresses, browser type data, device type data and anonymous data signals to estimate connections. Although probabilistic matching can produce greater reach, brands have recognized the importance of individual targeting and the value of understanding each customer’s journey for better engagement, analysis, and ROI. Yet for most marketers this capability still seems too good to be true, with many still questioning how this process can be achieved in a non-PII way and still be reliable.
All of the data aggregated by data onboarders, device ID providers, and publisher networks must be collected and stored in a way that allows for individual connections to be made in a deterministic fashion. How are these connections made? Through the use of an accurate identity graph.
An identity graph is an offline consumer database that contains all the known identifiers associated with individual customers. This can include email addresses, postal addresses, mobile numbers, device IDs and IP addresses along with demographic, geographic and lifestyle information on each consumer. Using an accurate identity graph, data onboarders can effectively increase the match criteria for each individual by linking all cookies, device IDs and customer data.
Once the data is connected through the identity graph, a persistent ID is applied, which enables an onboarder or device ID provider to match all data and devices back to an individual. The final addition of the persistent ID is what transforms the identity graph into a consumer ID graph.
At scale, a consistent consumer ID graph empowers marketers with valuable customer insights across all devices. Without it, brands would be unable to accurately segment, target, or monitor attribution both online and in-store.
It’s for these reasons that a consumer ID graph is considered the current ‘holy grail’ of marketing. The providers of consumer ID graphs understand the power of individual level targeting and are investing significant resources to help brands tie online and offline data sets across multiple devices into a single view.
At the end of the day, whether you’re a marketer or a data onboarding provider, everyone will agree that identity and accuracy is the new name of the game, not probabilistic cookies by the pound. The digital marketing industry and the capabilities offered by data onboarding providers are maturing at lightning speeds. In order to enhance customer engagement and optimize marketing performance, brands need to demand individual matching, cross-device alignment, consistent IDs and the ability to do this at scale. With the use of an accurate identity graph and consumer ID graph, marketers will see exceptional and measurable results, without it, well… keep trying.
It’s all about the data.