Alliant originally created this guide in 2018 to help data buyers find transparent and trustworthy partners. The world has changed a lot since then, so we thought it was time for an update. Check out a preview of the latest ebook here.
Smart buyers know digital audience data can make a big impact on the performance of a campaign. When data is clean, fresh and relevant, it can give you a measurable lift in KPIs. But when it’s mediocre, it‘ll burn through your ad budget — or worse, your client relationship.
The good news is, there is a lot of great data out there — and recognizing it can be as easy as asking data providers some simple questions like the ones below. In just a few minutes you can get a solid read on the quality of a provider — and avoid burning a portion of your ad dollars on audiences that aren’t worth the investment.
1. Core Business
“What’s your primary business model? How is data a core component?”
What you’re really asking: How much do you really care about data quality?
When the provider’s data is critical to its business operations, hygiene will be as important to the them as it is to you — maybe even more. But if they buy data just to turn it for profit, scale will probably take priority over quality. The less important the data is in driving revenue, the more lax they’re likely to be in terms of their hygiene standards.
“Is all of your data — and that of your partners — properly sourced and permissioned for marketing use under relevant laws?”
What you’re really asking: Are you legit?
The only acceptable answer here is “Yes”. All prospect data used for digital targeting must be properly permissioned. Data that does not meet regulatory requirements is a deal-breaker. Using it could tarnish your business’ reputation, or worse, turn into a legal concern. Even though GDPR gets the headlines (or CASL in Canada), there are many U.S. laws that affect various types of data. Make sure your providers are aware of — and serious about — their regulatory and ethical obligations.
3. Industry Involvement
“Are you active members of any industry organizations?”
What you’re really asking: Do you have a stake in the health of the data ecosystem?
Thus far, best practices in data management and privacy in the U.S. are largely governed by self-regulatory guidelines created by and enforced through industry groups such as IAB, ANA/DMA or NAI. If you’re satisfied with its collection practices, a great follow-up is to find out whether your prospective provider is an active member of at least one of these all-important groups. Providers holding leadership positions on councils or boards of these organizations suggests they prioritize quality.
4. Source Channel
“Was the data sourced from online or offline channels?”
What you’re really asking: How well do you know the prospect?
There’s no right or wrong answer here – it’s just a matter of your objectives. The greatest benefit of online sourced data is recency. If you need the ability to act on something the second it happens, digital is your only option.
Offline data, on the other hand, may provide a more definitive view of a consumer. Simply because of the way its onboarded (the process of mapping offline PII data to digital IDs), offline data is inherently quality controlled, to an extent. The data provider must know the name and postal and/or email address of every individual they onboard – which provides greater verification of each record as a real person with known attributes.
5. Data Collection Level ≠ Onboarding Level
“At which level was the data collected — and is it onboarded at the same level?”
What you’re really asking: How strong is your signal?
Don’t assume the data collection level is the same as the level it’s targeting. Often they are different, which can have a big impact on campaign’s performance.
Consider an In-Market for Auto audience. If the signal represents an individual consumer and your goal is lead generation, targeting individuals will probably be a stronger bet. But if the data was collected at the individual level and onboarded at the zip+4 level, you’ll be targeting that consumer plus the rest of the neighborhood. If your goal is brand awareness this might be a great strategy — but otherwise, it could be a waste of ad impressions.
6. Data Type
“Are your audiences sourced from ‘known’ data signals?”
What you’re really asking: Did you model your data?
In a perfect world, everyone would have access to pure “known” data straight from the source. But in reality, that data is often unavailable.
An audience created by modeling doesn’t necessarily indicate anything about the quality of that audience. But understanding how and why it was modeled — and the specifics about the sources and strength of the signal data — can help you get a better read on whether a given audience is likely to deliver the kind of KPIs you need. And that leads to the next question.
7. Model Objective
“If you modeled your data, what was the objective?”
What you’re really asking: Whose interests do you really have in mind?
To be clear, modeling is the process of using algorithms, and multiple data sets, to make predictions. In the digital space, this typically means “How likely a consumer is to … [fill in the blank]”. Data providers group consumers with the highest propensity (or likelihood) to take an action into similar audience segments — and use that information to make targeting decisions. There are many different model types, each with a different purpose, but generally a data provider will turn to modeling with one of the following objectives:
To improve the quality of the audience.
e.g. The provider has a large universe of prospects and wants to narrow it to the best targets
To build the audience.
e.g. The provider uses multiple data sets to predict the best targets and create the audience
To increase the size of the audience.
e.g. The provider’s audience isn’t large enough to scale, so they find more prospects that look like the ideal target
“How do you verify the quality of your data?”
What you’re really asking: Can I trust you?
In a way, this is a trick question. While there are services that measure basic demographic data against industry benchmarks, they are not comprehensive analyses of data quality. If nothing else, you can always ask a provider for an independent evaluation by your own statisticians. Case studies can also be helpful but they may not always tell you the full story.
Also, on the horizon are emerging companies that evaluate audiences by conducting surveys to compare consumers’ self-reported information to provider data. Alliant has been testing some of these new methodologies and will have more to come on that soon.
In reality, if you’re a data geek like us you could ask questions for days — but these eight will give you the foundation you need to make an informed buying decision. And don’t worry about feeling like an interrogator. If the provider takes quality seriously — and they should — they’ll be more than happy to tell you all about their pain-staking processes for keeping their data “so fresh and so clean”. I know anyone at Alliant would.
Here’s a bonus tip: If you build a network of a few trusted data providers that have a help desk, you can lean on them for support with all of your campaign needs. Whether it’s an audience recommendation or a little flair for your client presentation – when you need help, we’ll have you covered.
Did you like this article? Let us know by dropping a comment below – or email the author, Matt Frattaroli, at email@example.com.
Submit a Comment