4 Reasons Why Marketers Should Care (More) About Data Quality
There are few things in today’s world that are as pervasive as the collection and utilization of data. As a consumer, the always-on lifestyle we’ve adopted has created an oasis of information-gathering opportunities for companies that want to learn more about us. Thanks to Apple’s weekly iOS screen-time alerts, I’m fully aware of my legendary data generating abilities every Sunday. Businesses large and small are using this data at every level and in every department; marketing, sales, HR, management...gone are the days where decisions are made by feelings and emotions (apologies to all the Don Draper fans out there). We’re not touching new ground here, most people acknowledge these realities. So much so that even glacially slow federal and local governments are getting in on the action with new laws surrounding data protection and privacy being introduced at a blistering pace. One thing, however, that is lagging woefully behind in the age of big data is a concerted focus on and attention to the quality of the data being used by marketers to make billion dollar decisions. There are a number of reasons why data quality hasn’t been in the spotlight for marketers - the incentives for many players in the data ecosystem are more closely aligned with quantity over quality. There’s so much data out there that finding the most accurate information may seem like trying to find a needle in a haystack. Most importantly, there has never been a universal set of tools available to data buyers and sellers to guide decision makers towards the most accurate results. Thanks to Truthset, those days are numbered. Here are the top 4 reasons why marketers should care more about data quality, and how Truthset can help:
1. Ads delivered to people outside your target audience are costing you money (and customer sentiment)! “All ads are annoying!” Au contraire, my friends. There’s a reason why 91.6 million people watched the Super Bowl this year and it probably had less to do with Tom Brady winning his 7th ring than you might think. Ads that aren’t relevant to the consumer are annoying. I don’t have kids, so having to sit through a 15 second non-skippable diaper ad before I watch a short YouTube clip is not going to endear me to your brand. An auto brand might want to pump their brakes on targeting someone that just bought a car last week. I could go on with examples like this for days, but every ad delivered outside of a brand’s target audience is not only money circling down the drain, but also has the potential to leave a lasting negative impression on a consumer that may one day be part of that target. If you’ve followed Truthset, you’ve probably heard reference to “21 cents of every media dollar being wasted due to poor quality data.” Media dollars are out there that can be better spent, all while ensuring that you’re building positive relationships with the right consumers for your products.
2. All consumer data is NOT created equally and labels are misleading At Truthset, we spend a lot of time in the Liveramp Data Marketplace, and one thing is pretty clear when perusing the different options available - there are a near infinite amount of ways to try to target similar audiences. At a glance, the Marketplace boasts over 150 data providers and tens of thousands of different segments; let’s just say it is not designed for the indecisive. Marketers trying to find a specific demographic audience may also be dismayed to discover that naming conventions or data labels can vary widely between providers and audience types. If a marketer is interested in targeting Hispanic consumers, they first need to figure out whether they should select from Hispanic consumer segments, Latin music fans, Cinco de Mayo shoppers, or other more indirect signals. After they’ve narrowed their search, there are still over 40 different audiences for purchase to reach that Hispanic consumer, and no way of determining which segment is going to perform the best. Luckily, Truthset allows data buyers to view these audiences in a way never before seen - based on how accurate each segment is at correctly identifying whether or not they are Hispanic. Analyzing each of the 40+ datasets available shows a wide range of quality with some segments having a composition of Hispanics of over 75% and others as low as 14%!
3. Current measurement solutions aren’t giving you the full picture Marketers that want to evaluate their on-target percentage have some tools at their disposal, namely Nielsen’s Digital Ad Ratings (DAR) and comScore’s Validated Campaign Essentials (vCE). These services exist to provide advertisers with benchmarks to gauge the effectiveness of reaching their target audiences. While products like DAR and vCE offer the ability to understand a campaign’s delivery in some context, there are a number of features that marketers are starving for. Here’s a few;
On-target measurement beyond age and gender - some brands that offer consumer staples can get away with simplifying KPIs to reach the right age and gender, but the reality is that this misses the mark for many advertisers. Even brands with massive reach want to target different consumers with different messages. Defining campaign success against these limited demographics can seem downright antiquated when you compare them against the abundance of granular targeting criteria available to today’s marketers. At Truthset, we offer the ability to measure on-target delivery across a wide range of attributes that go beyond age and gender to key segments including household income, presence of children, race and ethnicity, state and region, life stages, and more.
The ability to guide impressions to the right audience before buying the ad spot - there are ways to track and optimize campaign delivery using current tools, but it would be easier to simply not buy an impression before validating that there’s a good chance it is going to the correct audience. Truthset gives marketers the ability to set accuracy thresholds within the data segments you target that can more effectively balance scale and accuracy before you make a buy and waste media spend.
4. Further error is created when expanding audiences with lookalike modeling Audience expansion is crucial for ensuring that a campaign reaches the most prospective customers. 1st party customer data is a great resource for brands that have a direct relationship with their consumers, but they'll need to look to audience expansion if they want to reach new, similar prospects with advertising. The use cases for audience expansion are endless, so there is a reliance on lookalike modeling that isn’t going away anytime soon. Platforms conducting this audience expansion will take a set of IDs associated with a particular audience (i.e. the seed file) and compare underlying data to identify a set of criteria or differentiating characteristics that is common across the file. This process makes the accuracy of the seed file extremely important. If there is inaccuracy in the seed file, that error gets expanded exponentially by the lookalike model which is assuming that all the data from the original file is 100% accurate. At Truthset, we work with brands and agencies to ensure that seed files comprise the most accurate audiences prior to being expanded out - even a 10% reduction in error can have a massive impact on the success of audience expansion. If you’ve made it this far, thank you for taking the time to learn more about the importance of data quality and the steps Truthset and our data partners are taking to bring accuracy and transparency to the forefront. If you have any questions on anything data quality-related or want to optimize your next data decision, drop us a line at email@example.com.