The Problem with Comparing Match Rates and How to Approach It
Discover how to elevate your marketing strategies beyond traditional match rates.
Jessica Ruppert, principal product manager at Bombora, questions match rates’ efficacy in identifying high-value accounts. She advocates for resolving anonymous business activity to specific accounts, including data quality, source diversity, and resolution accuracy.
For many B2B marketers, the bottom line depends on how well they can identify the right high-value accounts to target. Find engaged accounts, and they may gain a competitive edge; they may spend too much time on the wrong accounts, and they may miss sales and revenue goals.
It’s common for folks to measure their ability to identify high-value accounts through a “Match Rate.” However, a high match rate is often a red herring and does not translate into accurate target account identification. Rather than chasing the highest match rates, B2B marketers should be asking how the data will help them drive the best business results.
Making a Match
“Match Rate” is a fairly common term in B2B marketing that refers to how well a data provider can map a customer’s accounts to accounts within their datasets. This is often confused with another common term, “resolution rate,” which involves resolving anonymous business activity in an account.
This is an important distinction to make because simply matching account lists to generate a high match rate doesn’t always align with a B2B marketer’s goal and move the needle for a company’s bottom line. If your goal is to capitalize on your anonymous business activity, your focus should shift from seeking high match rates to achieving high-quality resolutions.
Defining High-quality Resolutions
Discovering high-value accounts involves taking millions if not billions, of individual events and aggregating them to determine business activity. Before this, the individual visitors must first be de-anonymized by a company. Data providers who fail to resolve business activity to an account carefully can tout high match rates, and these higher rates are often accompanied by poor data quality in the form of inaccurate domain identification.
Similarly, if the resolution process doesn’t exclude non-business activity, such as Bot traffic or consumer activity, the aggregated account information will not properly represent the buying propensity of the organization.
See More: Data Privacy & Data Quality Driving Success in Direct Marketing
Industry Use Cases
As a B2B marketer, staying informed of these practices is critical and your organization should strive to resolve at least 25% of your site traffic back to a business domain.
To illustrate the practical benefits of prioritizing high-quality resolutions, consider how this strategy has been applied effectively across different sectors:
- Retail and E-commerce: A B2B provider of e-commerce platform solutions can use high-quality resolutions to understand the digital behavior of retailers better online.
- Education: If an educational technology business offers remote learning, they can use high-quality resolutions to distinguish between the general interest of an e-learning demand versus specific needs, like institutions seeking platforms for grades K-12.
- Technology: A tech or software company can improve its account-based marketing campaigns by shifting focus to high-quality resolutions, which may lead to a rise in conversion rates by aligning marketing efforts to audiences with genuine interest.
- Manufacturing: To differentiate manufacturers from a saturated market and focus on sustainability-focused buyers, leverage high-quality resolutions to pinpoint accounts with a demonstrated interest in sustainable practices. You will then be able to tailor messaging to highlight relevant products and initiatives.
Each industry and business case is unique. To determine the true business impact, B2B marketers should dig deeper and zero in on three critical factors: business activity identification, data source diversity, and data quality.
Diving into Resolution Rate Factors
How can B2B marketers identify the amount of business activity that any single account will generate? The industry is getting better at answering that question with the help of artificial intelligence (AI). Advanced AI can analyze vast amounts of data and behavioral patterns to identify B2B activity — a crucial component that can then be mapped to accounts in order to predict those most likely to buy.
The more diverse the sources of data, the richer the data set. For a comprehensive view of B2B buying behavior, marketers should consider the diversity and scale of data sources used to seed intent insights. To achieve meaningful data-source diversity, data providers should create a network that captures first-party business research from a wide range of publishers, platforms, and brands. That data can then be aggregated to accounts in a privacy-compliant way before it is surfaced as intent data. Data providers can also join forces through strategic partnerships to share data sets, which can help them validate B2B account graphs, strengthen their identity capabilities, and improve their understanding of business identities.
In addition to diversity and volume, a substantial collection of data sources also affects data quality. Because IPs can’t capture the buyer’s full research journey, one needs a significant number of alternative identifiers and partners to capture and resolve all business activity accurately. Tapping into an extensive partner network enables the mapping of millions of accounts around the world, which can be updated frequently and matched to new business activity.
Securing the Future of Match Rates
Taking this kind of advanced approach to identity resolution is key to B2B marketers’ success, but the approach must evolve as the identity landscape changes. To stay ahead of the curve, data providers must cast a wider and more accurate net by growing their data networks and partnerships.
At the same time, progress in data science and technology — such as better behavioral modeling techniques, unique identifiers, and probabilistic identity resolution methods — can protect B2B marketers who use intent data from future privacy-related risks and the ever-changing identity landscape.
When B2B marketers shift focus from constantly seeking high match rates to targeting accounts that engage in meaningful business activity, they gain a competitive advantage and achieve successful business outcomes.
As a B2B marketer, how are you maximizing data utilization to enhance your account targeting goals? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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