In marketing and advertising, the value of customer data can't be overstated. However, despite data being ubiquitous in digital marketing today, both brand marketers and agency buyers are skeptical about the transparency and effectiveness of the data their partners are providing.
According to a research project we recently conducted with Ad Age, more than 75% of survey respondents admitted they are not fully confident that the data they're utilizing is hitting consumers who are in-market to buy. Additionally, 65% of respondents claimed they do not fully understand the origin of their data sources.
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We've put together a checklist of straightforward questions that you should ask any advertising data provider prior to executing any campaign. This will ensure that you are, in fact, reaching target audiences in the most effective ways and getting the best return on your ad spend.
1. What are the sources of your data?
Every data provider will try to convince you that their data is superior to the alternatives, but by uncovering where their data is sourced, you'll be able to see past their sales pitches and truly understand what you are actually buying. Are they sourcing first-party data internally? If so, how? Challenge your data providers and don't let them gloss over this question. You should never buy any data if you don't fully understand where it comes from.
2. How far does your data reach?
Your data provider may have first-party data, which many agree is more effective than third-party data. But if the data isn't representative of a significant number of users, it will be difficult to reach the large-scale audience you want to target. Remember, large numbers of data storage terabytes (TB) do not necessarily equate to actual audience size. Find out how many real people your data providers can reach with an advertisement.
3. What percentage of your data is created from a look-alike model?
To continue from the previous point, some data providers may have strong first-party data signals, but relatively small scale and limited reach. Many companies are starting to use look-alike modeling to artificially inflate their reach to make their data look more appealing to buyers. Though look-alike modeling is becoming common practice, you should ask your data partners what percentage of their data is first-party vs. look-alike.
4. Which intent signals or behaviors place a user into an audience segment?
If a data provider is selling you customers who fall into a particular interest segment, you should ask what behaviors or actions qualified those specific users for that specific segment. Are your audience segments built from a single data signal? Or does it require multiple signals before adding a user to a segment? Generally, a higher number of signals in a given time period indicates a stronger level of user intent, but you should make sure those data signals are fresh and relevant.
5. How do you maintain your audience segments?
Consumers are fickle. Their purchase intent varies based on their mood, interests, what they want or need, and when they need it. If a user initially falls into a segment but does not continue to display interest and intent for that category, data providers should remove that person from that segment. As time passes with few or no actions or signs of purchase intent, those users are unlikely to be relevant targets for your campaigns. You must partner with a data provider with a solid strategy for keeping their audience segments engaged, fresh and current. If you use outdated data, you run the risk of wasting ad dollars and annoying your audience.
6. Can you explain the process behind how you define your audience segments—and the data that feeds into them?
Data providers often give audience segments attractive names like "home improvement enthusiasts" or "handy husbands." But the fact that someone read a DIY article or looked at a toolbox online once does not make them a home improvement enthusiast. However, a user who has looked at 5 toolboxes in the past 30 days, read multiple ratings and reviews, and then purchased a toolbox is much more likely to engage with and be influenced by timely home improvement product ads. This important distinction, along with properly labeled audiences, can result in much more effective targeting.
7. In which categories does your data best perform, and why?
Going back to the first question, you need to understand exactly how data is being sourced. If it comes mostly from sources associated with a particular category, such as health and beauty, ask your data provider to explain why their data performs best for beauty campaigns and what specific signals can point back to a customer's shopper journey for cosmetics.
8. For which metric(s) does your data best perform, and why?
Before running any campaign, it's critical to understand where your data provider's strengths lie in terms of metrics and performance to ensure that their strategy and recommended audience segments fully align with your business goals. If not, you risk wasting your advertising investment reaching users who are not helping drive your desired campaign outcomes. For example, if you want to increase brand consideration (share of voice), you should be able to understand how your data partner's data aligns to your brand strategy and what measures they are taking to drive toward your goals.
9. Can you reach the same user across their multiple devices?
A cross-device strategy is essential in today's digital world, where nearly everyone uses multiple devices and navigates between them seamlessly throughout their day. Reaching the same user on all of their devices allows you to have an ongoing conversation with them throughout their entire path to purchase. Ensure your data provider has a device graph (or trusted cross-device provider) that links user devices together for a comprehensive targeting strategy.
10. Does your data drive brand consideration and/or sales, and can you accurately attribute the performance lift directly to your campaign? If so, how?
The ultimate end goal of marketing and advertising is to drive sales and show a positive return on investment. A data provider should be able to demonstrate the value of their data with concrete examples of campaigns that drive directly attributable sales.