Simplify Targeting And Overcome Low Match Rates
There is an explosive hype focused on leveraging an advertiser’s customer data to enable better digital media targeting. Several approaches have sprung up over the years that attempt to individually match customer level data to cookies for targeting ads. These processes include matching registered users through either a persistent cookie or an email address; some simply match a customer’s personal information (PII) with permission.
The match rates are not great! Some advertisers have reported match rates as low as 10% with only 50% accuracy in identifying gender. With low individual match rates exacerbated by inaccuracy, advertisers should start to rethink their data strategy. Is the cost and resources of individual matching really worth it -- or is there an alternative solution, one which is simple and more effective? Furthermore, for advertisers innately concerned with privacy, individual matching in the cloud opens up issues around data leakage and opens a door for competitive conquesting.
I will say there is an alternative solution, one that protects customer data, yet provides an accurate comprehensive approach to targeting across multiple digital targeting partners and platforms. Before I outline this solution, let me first recap the current data landscape.
Customer data is typically divided into three categories, each providing its own unique value in describing a customer’s attributes for smarter targeting.
1. First-party data most often sits behind an advertiser’s firewall and includes information such as visitor behavior on an advertiser’s website, registration with a mobile app and ultimately the CRM dataset.
2. Second-party data is derived through close advertising partnerships with big portals such as Yahoo and co-registration partners, etc. This data is usually provided only to the advertiser and includes a customer’s engagement with that partner along with relevant information that may enhance targeting with that partner and overall.
3. Third-party data is available to the general public through several primary data providers and the various data brokers who resell the same data at a premium. Third party data typically includes two types of information:
- Demographic data, generated through warranties cards, surveys and mostly Government Census data (Census data is free and available online for download)
- Behavioral classification data, modeled off a user’s digital browsing behavior. For example, when a user visits several 401K sites along with a sports blog, you can infer this user is an older male in-market for retirement products, etc.
The most accurate and meaningful data is first-party data. It allows an advertiser to focus investment and messaging to a more precisely defined target audience. Advertisers may also accumulate second- and third-party data as part of their CRM dataset. The alternative solution I recommend is the aggregate matching approach. This approach would leverage all the available data (first-, second- and third-party), protect data from leakage outside the firewall and still provide rich targeting instructions to any selected targeting partner.
Here is how it will work: the customer dataset sits within a protected environment and is usually easily accessible through a preferred analytics or business intelligence tool. Using the analytics tools, an advertiser, agency or nalytics vendor would evaluate the customer dataset to identify key attributes about the most desired segments and define targeting criteria (rules). Therefore, instead of matching individual cookies with a limited match rate, an advertiser would provide customized targeting rules. These rules would define the target audience as a whole and along with a plethora of additional relevant elements to further refine and enhance targeting criteria specific to each targeting partner (e.g. DSP, ad network, online video server, etc.)
In short, shifting to an aggregate matching strategy would secure your data, prevent data leakage, and ensure all the target audience is represented while likely reducing costs, time and resources.