Stacey Hawes | March 21, 2019
High-performance marketing is rooted in strong data, but not all data is equal. Having quality data is critical to ensure you’re maximizing your marketing’s effectiveness, making every customer and prospect interaction count.
But what does having quality data mean? Let’s review key factors to consider when evaluating data:
- Coverage: Overall coverage looks at file size and element coverage is the percent of records on the file with that specific element. In today’s environment, coverage must go beyond name and address to also include multichannel coverage. This translates to higher and consistent identification rates across all consumer touch-points for more effective omnichannel marketing.
- Accuracy: Use a truth set file you have confidence in to compare and evaluate accuracy of data elements. Data accuracy ensures you’re reaching the right consumers with the optimal message and offer to maximize your marketing dollars. Tradeoffs between coverage and accuracy can happen, so the key to success is balancing and achieving high marks in both.
- Performance: Performance measures how well data elements are able to predict specific actions. Well-balanced models with data elements reflecting depth, breadth, variety and uniqueness drive the best performance.
- Privacy: Performance should never come at the cost of privacy. To succeed, you need data that’s transparent, verifiable and trustworthy. A privacy-first approach is fundamental to data-driven marketing.
The consequences of not using quality data
If data is not accurate or high quality, it doesn’t matter what statistical methods or advanced analytics are applied, nor how much experience is brought to the table.
Basing decisions on conclusions derived from flawed data can have costly implications for you and you business. Poor data costs the U.S. economy over $3 trillion each year and can cost businesses at least 30% of revenues. These costs go beyond the monetary impact of wasted marketing spend to include consequences like loss of reputation and higher-risk decision making.
Therefore, it’s imperative that brands seek data partners who are transparent about their sources and the steps they take to maintain data quality and respect consumer privacy.
The value of having the right partner
Let’s look at a real example of how improvements in data quality translate to improved business outcomes. Faraday faced two major challenges with their data: simplicity and quality. Their data supply chain was complicated and they were working with a large number of vendors. After piecing together data sources for many years, Faraday partnered with us on a solution.
We jointly selected several data sources, including a dynamic and robust install of our multi-sourced consumer marketing file TotalSource Plus®. Comparing our data to prior sources, Faraday found our data to have better quality, higher coverage and more attributes that suited their business.
The result? Faraday has seen a 50% increase in model validation accuracy, resulting in happier clients, better retention and an overall lift to their business.
Ongoing evaluation and commitment to quality
When was the last time you looked at the quality of your marketing data? Do you know if there are gaps? How often is the data refreshed? The time and effort you invest to ask the right questions, test (and retest!) data and find the right data partner will pay off many times over in smarter decision-making and better results.
Because of our commitment to high-quality data, we authorized a third-party audit of our consumer marketing file, TotalSource Plus, to evaluate its strength and ensure we continue to offer performance-driven data per marketing industry standards.