Data-driven targeting is only as good as the insights that fuel it. When choosing a data provider, it is important to understand the quality and accuracy of the data included within an audience segment, as well as the steps the vendor is taking to ensure compliance with consumer privacy regulations. Typically, the defined target and use case will help decide which audience makes the most sense. See below for the key questions to consider as part of the selection process.
How was the data collected?
Data collection methodologies often vary by data provider and can have a large impact on the overall quality and consistency of the data set. The IAB Tech Lab recognizes the following data inclusion methodologies as part of their Data Transparency Label, an initiative designed to standardize the way buyers evaluate audience data quality:
- Observed/ Known – directly observed
- Declared – self-reported by the audience members
- Derived – computed based on other known or declared fields on record
- Inferred – determined from business rules or logic
- Modeled – calculated using an algorithm, with a seed as the source
Once an advertiser understands how the data is sourced, they can begin to determine whether it is right for their targeting strategy. For example, while e-commerce data (observed) may help marketers to reach consumers based on the products they have already purchased, online survey data (self-declared) can provide insight into purchase intent, enabling brands to target shoppers after they have declared explicit interest in a particular product or service.
Do the data segments offer accuracy and/or scale?
Beyond helping advertisers understand where their data comes from, matching methodologies also speak to the accuracy of the data within a given segment. Based on the collection method, data segments may offer varying levels of precision.
- Deterministic data providers offer a more precise match, using a consistent identifier such as a phone number or email address to match a device cookie, MAID, or CTV ID to an individual or household.
- Probabilistic data providers match to various addressable IDs, drawing thousands of anonymous data points to identify statistically significant correlations between devices based on usage.
As expected, scale will sometimes come at the cost of accuracy. For example, if an advertiser is trying to reach a very small, niche audience, they might need to rely on a segment that is built probabilistically in order to achieve enough scale to run the campaign – though this audience will be less accurate than those matched deterministically. Ultimately, advertisers should prioritize scale versus accuracy depending on their targeting objectives.
How does the data provider address consumer privacy?
Data privacy is top of mind for advertisers as regulations such as GDPR and the California Consumer Privacy Act (CCPA) continue to change the digital marketing landscape. Compliance with the NAI Code of Conduct is also becoming increasingly important as new requirements around the collection of sensitive information, such as health data, emerge in 2020. According to eMarketer, 75% of US internet users are concerned about the ways tech and social media companies are using their data for commercial purposes, with only one in ten approving the use of their data for ad targeting.
In light of these mounting concerns, it is important for advertisers to demand greater accountability from their data provider, especially as it relates to privacy standards and frameworks. As a general rule, advertisers should seek out data providers that comply with the following tenets of CCPA:
- Provide consumers with transparency into what data is collected, who is it sold to or shared with, and why
- Offer consumers the right to deletion
- Offer consumers the right to opt-out of the sale of information
Ethically sourced data lends itself to better advertising experiences for consumers and measurable results for brands. Advertisers that can deliver targeted experiences while still demonstrating respect for consumer privacy will be most likely to gain customers’ loyalty and trust.
Access First-Party, Self-Declared Data from Fluent
As data-privacy regulations and unanswered sourcing questions muddy the reputation of third-party data, marketers are turning to second-party data to fill in the gaps. Comprised of self-declared consumer insights, Fluent’s audience segments enable marketers to reach pre-qualified audiences in a scalable, efficient, and privacy-safe way.
Data is volunteered by opted-in and anonymized users across Fluent’s proprietary network of websites and decoupled from media, helping marketers to reach audiences across mobile, display, and connected TV. Consisting of volunteered consumer insights, Fluent’s audience segments enable more precise targeting for marketers and better advertising experiences for consumers.
For more information on identifying and targeting your ideal customer with custom and syndicated audience segments, connect with us here.