How Prioritizing Good Data Leads to Better Targeting

If you don’t focus on data quality in your marketing, it will never reach the right people. 

Bad marketing data can lead to wasted time and money, not to mention missing out on meaningful connections with the right audiences. And in the world of digital marketing, growth and customer loyalty are both driven by your ability to make those connections.

With all the data out there to sift through, it’s an ongoing challenge to ensure data is accurate and timely. Modern consumers want instant, personalized messaging, and that can only happen when you get current, reliable data. 

Let’s take a look at the following considerations for more effective targeting:

  • How is good data connected to targeting?
  • What does effective targeting look like?
  • How can you prioritize good data?
  • Finding the right data source

How is good data connected to targeting?

While certain marketing channels take a broad approach to spark customer interest (for example, a billboard), digital marketing gives brands the opportunity to target advertising, so it only reaches people with the best chance to become buyers. But this requires you to make decisions based on consumer data. There’s too much to filter through online to have a clue who you’re talking to without information about them where to find people, what they care about, their behaviors and motivations. 

Take digital marketing a step further with personalization, which more consumers care about in our modern climate. Research shows that 74% of consumers are frustrated when content isn’t relevant to them, and 56% would reward personalization with a purchase. 

Consumers want to feel like brands care about solving their problems, not just getting them to buy something. The personalized marketing experiences that create these connections are a major reason for connecting data to targeting.

But it’s important to understand the harm that bad data can cause it’s more than just ineffectiveness. In addition to failing to connect you with the right person at the right time, bad data actually leads to false assumptions about your audiences that get you further and further away from fact-based decision-making. 

Once a bad data point is in your data system, it leads you to create profiles about demographics or groups that just aren’t true, and then the dominoes fall from there. That one piece of bad data negatively impacts all of your targeting efforts.

What does effective targeting look like?

Effective targeting, on the other hand, means using high-quality data (data that is complete, accurate, timely, consistent, and accessible). Only then can you make accurate consumer assumptions and meaningful connections. 

Targeting requires that you define your audience based on what your brand provides and what it means to that audience. Who are you most suited to help? Why should this population care about your brand? What benefits do they get from working with you? 

You then define what drives these individuals based on purchasing behavior and where they go for information. You want to excite good prospects with messages that are relevant to what they’re looking for in that moment. This personal level of targeting isn’t possible unless you have accurate data.

In addition, you need to be able to segment audiences so your messages are more relevant and personalized. And effective segmentation cannot occur unless the information is correct and up to date. 

How can you prioritize good data?

Data quality management (DQM) is the practice of maintaining high-quality data. DQM teams put processes and steps in place from the data acquisition stage to distribution to ensure that data is clean, complete, and accurate. This requires a mix of components that work together to monitor quality, including the people involved, thoroughly reviewed data, and data reporting.

The DQM team must be on the same page about what defines data quality. There should be processes in place to remove old or bad data and identify data that has been compromised.  

Data storage is crucial for data quality. Information needs to be stored in one place so there’s only one source of truth and you avoid redundant data. Data solutions should also employ automation techniques that perform functions like removing data that is out of date. It’s also important that the team can see date stamps that track editing history.

Finding the right data source

Another way to prioritize quality data is to be vigilant about choosing a data source. Many marketers are using (and reusing) the same data or depending on less-than-trustworthy sources, whether to save money or to get a fast answer. But it’s always imperative that you get data from the most reliable sources, where you know data has been vetted and verified. 

BDEX is a data source you can trust. Our BDEX Data Exchange Platform (DXP) gathers and delivers more user data than any other platform, providing more opportunities to connect with the right prospects for your brand. We take data quality seriously and have implemented tools like the BDEX ID Check, which filters out bad data from the platform, giving you the highest quality ID graph in the data market.
Contact BDEX to learn how our data solutions will help you improve your targeting efforts using high-quality data in real-time.