Data quality is such a high priority for businesses because it’s critical for understanding consumers. Here are three ways to ensure your data is good.
Do you know why data quality has become such a priority for businesses? Because it’s critical for understanding consumers. The more detailed the information your business captures, the more effective your marketing strategies will be — enabling campaign messaging to resonate with your targeted audience segments.
A recent Forrester study found that 82% of marketing leaders say their organizations are making data quality a “top” priority. But many of these same organizations struggle to optimize data quality, which inevitably affects insights, marketing approaches, and execution.
As it relates to data health, accuracy is exactly what it sounds like — but with an added wrinkle: It’s not just essential that a piece of data is correct. Each figure needs to equal a specific value so that the insight it provides is unambiguous.
For example, let’s look at one situation that can cause mistargeting. John Smith is a relatively common name that could come up several times when culling marketing insights. If a children’s apparel company sent material to a “John Smith” in its system who did not have children, that’s inaccurate information. While part of the data might be correct, not knowing all the insights behind each data point can lead to mistakes in targeted marketing.
There are so many variables that make data points of information not only valuable but also correct. Bottom line: Data accuracy is impossible when inconsistent values impede the ability to aggregate and compare data accurately.
Like bread, data has a shelf life. Values and information become stale and outdated for a variety of reasons. Recency is key to data health because it illustrates the role that relevant and up-to-date information plays in creating actionable marketing insights.
Your marketing team may have generated targeted leads whom they have been reaching out to for years. But if that dataset isn’t regularly analyzed or updated, your marketing team might learn that some of these leads are no longer with their companies and that all these marketing efforts will have gone for naught. Attributes such as company, job title, and more all aid in personalizing messages — but they’re also fluid data points that must be kept current.
As an organization, you need to decide when a data set is considered recent. Is an update to all records necessary? Is this a single update or an ongoing process? Does an update to a single field associated with an individual count, or do all fields need to be updated?
Obviously, certain fields are more important than others based on business goals. As such, think of recency as more of a floating measurement. Consider your own data needs before drawing a line in the sand on what’s current and what isn’t.
The MarTech industry likes to talk about the elusive “360-degree customer profile”, which houses all your data in one place. But that data set can’t be shallow or skinny; it must be robust with insights on demographics, transactions, social behaviors, and even previous touchpoints with customers.
Your goal is to have a database full of robust data. The more attributes you can tie to customers, the more targeted, relevant, and — ultimately — effective your marketing outreach can be.
Data health and wellness is nothing to sneeze at (pun intended), and building a healthy database is critical to business success. If the information contained in a data set isn’t accurate, recent, or robust, your messaging becomes less relevant. It just won’t do what it could to catch people’s attention and bring them through your doors.
If you’d like to learn more about data health or data’s impact on digital marketing, visit our platform page. We have plenty of information available to answer your questions.