Big data is big business. As more and more data providers strive to feed the growing demand for deep audience insights, the quality of the data being offered can drop off dramatically. It is no secret that selling data is a volume-based business. The higher the volume, the more money the data provider earns. Data providers that are only after high volume and lack quality control standards should be avoided. Once more, even good-quality data can lead you astray if it is misinterpreted or used inappropriately.
Here’s a look at 5 considerations when shopping for data so you can make the best decisions for your brand.
It’s difficult for many data providers to accept, but all data decays over time.
People move to new homes, switch email addresses, dynamic IP addresses rotate, and their interests change which complicates the integrity of linkages used to obtain device IDs as well as the marketing signals used within a campaign.
This truism means that refresh cycles should be explained by the data supplier. You should specifically inquire about 3 update schedules: How often is PII being corroborated and updated, how often are attributes being phased in and out, and how often is a full replacement of both the PII and the attributes sent to on boarders and DMPs. All too often, buyers focus on only one of the updates schedules or ignore the value of full replacement over delta changes, which leave campaigns exposed to stale links and signals.
Deterministic vs. Probabilistic
Some providers compile deterministic data, which is based on self-declared attributes, behaviors, and desires reported directly from the individual. Others offer probabilistic data, which uses modeling based on assumptions or inferences to create audiences. While probabilistic data receives a lot of press about providing extending audience scale, there is no substitute for deterministic data.
It is important that you understand the nature of the data being provided, and if pre-modeled data will work for your campaigns. In some instances, using pre-modeled segments from data providers will reap great rewards, but in many cases the modeling is loosely done because the data provider needs to service a diverse set of clientele, ultimately sacrificing accuracy and individual insights for reach.
Combating Fraudsters and Robots
What is the data provider doing to combat fraudulent and robotically generated records? This is the most overlooked question we’ve seen from marketers, brands, and agencies. Data breaches, hacks, and scams are so commonplace, you need to know what specific actions your data provider is taking to purge harmful data from their ecosystem. After all, it is counter-intuitive for data providers to intentionally cut their own supply. Their answer will be telling as to whether volume or quality takes priority.
Does Search Traffic Equal Intent
Search traffic is a great source particularly for certain hard to reach audiences. But like all data, search traffic needs to be questioned to determine if the assumptions being made by the data provider are logical to indicate intent. For example, if an individual search for “Porsche 911” is it reasonable to assume they are in the market for a vehicle? Or is it more logical that a significant portion of “Porsche 911” searches on the given site are simply enthusiasts, fans, or do-it-yourselfers wanting to check out the latest car pictures and specs? In fact, research has shown that only a third of automotive search traffic accurately conveys intent to buy. This doesn’t mean you should abandon search traffic data providers, but you should understand how and why assigning the coveted intent signal to a search makes logical sense.
Consider the Source and Scale
The data world has a ton of players—the data collector, data aggregator, the data-management platform, the demand-side platform, analytics, and more. Be cognizant that what goes in one end of the supply chain may be unrecognizable at the destination on the other end. The closer the data provider can bring you to the individual, the better for your campaign.
When exploring the segments offered by a data provider, consider if the scale makes sense. Ask yourself, for example, are there really 300 million females in the U.S.? Inflated estimates of audience density will lead to poor campaign ROAS (Return on Ads Served) later on. A little common sense will go a long way.
While the amount of data and data providers grows each day, you can still ensure your campaign is using the best data possible by asking strategic questions and discussing the topics we’ve mentioned to compare data providers.
Webbula offers only self-reported, deterministic data that is linked at an individual level. We keep brands safe each and every day from fraud, robots, and scammers by removing those hazardous records from our audiences. Contact us today to see how you can drive campaign ROAS (Return on Ads Served) with the highest quality data in the industry.