Many organizations still view customer and prospect data through separate systems and records. One profile captures professional activity. Another reflects consumer behavior. Additional records live elsewhere, often disconnected and duplicative. The result is an incomplete understanding of who people are and how they engage.
This fragmentation limits insight, weakens analytics, and reduces the effectiveness of marketing and sales strategies.
Industry research shows that more than 70% of marketers now use identity resolution tools to unify data across channels, and nearly 60% report measurable ROI improvements after implementing them. As organizations rely more heavily on data and AI, identity has become a prerequisite for performance rather than a supporting function.
Identity management was once treated as a technical necessity focused on hygiene and compliance. That view has shifted.
Today, identity functions as a strategic layer that connects data, analytics, and activation. When identity is resolved correctly, organizations can:
Without a strong identity foundation, advanced analytics and AI models are constrained by incomplete or inaccurate inputs.
A persistent challenge for many organizations is the separation of consumer and business data. People do not behave differently because a system labels them as a consumer or a business contact.
A buyer may research at home, engage with content through a work email, and purchase on behalf of their organization. When those interactions are stored separately, organizations lose visibility into the full journey.
ProfileFuse™ addresses this challenge by connecting consumer profiles to business contact records and business contacts back to their consumer identities. This creates a unified view of the individual and allows organizations to understand how personal and professional attributes intersect.
The value is not simply cleaner data. It is the ability to analyze behavior more accurately and act on it with confidence.
Achieving this level of identity resolution requires more than basic matching logic.
ProfileFuse is powered by Data Axle’s Match AI, which analyzes patterns and relationships across Data Axle’s business and consumer databases using more than 11,000 data sources and verification processes. This approach uncovers connections that deterministic or rule-based matching alone cannot identify.
Academic research indicates that identity-aware models can drive conversion rate lifts of up to 70% compared to models that rely on fragmented identifiers, particularly in datasets with limited or inconsistent signals.
With AI-driven identity resolution, organizations can:
AI does not simply accelerate matching. It enables more accurate analysis and decision-making.
When consumer and business identities are unified, new opportunities emerge that were previously hidden inside disconnected records. The following examples illustrate how organizations across major verticals are using ProfileFuse to unlock growth.
A telecom provider links a residential internet customer to ownership of a neighborhood retail business. Instead of treating these as separate accounts, the provider recognizes an existing relationship and trusted brand interaction. This allows the telecom team to offer business internet, point-of-sale connectivity, and security services to the retail location, using familiarity and continuity to shorten the sales cycle and expand share of wallet.
An insurance company identifies that a homeowner policyholder also owns a manufacturing business with 100 employees. Previously, this relationship existed only at the personal policy level. With a complete identity view, the insurer can proactively introduce commercial coverage options, workers’ compensation, and liability products. The result is a more comprehensive relationship that improves retention and increases lifetime value.
An automotive brand or dealer group recognizes that the owner of a Chevrolet Equinox also operates an HVAC business with 50 employees. This insight creates a direct path to fleet sales, service contracts, and commercial financing discussions. Instead of relying on broad outreach, the organization can prioritize high-propensity opportunities already within its customer base.
A financial institution connects a consumer banking customer to ownership of a growing professional services firm. With that unified view, the institution can offer commercial lending, treasury management, and business credit products alongside personal financial services. This enables relationship managers to move from transactional interactions to long-term advisory relationships.
In each case, the value comes from understanding the individual as a whole, not as separate records managed by separate teams.
When identity is resolved, analytics improve across the organization.
Unified profiles allow teams to move beyond basic reporting and toward advanced analysis. Organizations can identify shared attributes among high-value customers, uncover behavioral patterns, and better understand what drives engagement and conversion.
Industry benchmarks show that organizations with improved identity resolution experience 20 to 35% increases in campaign performance, along with 40 to 60% improvements in data accuracy and operational efficiency.
These gains influence more than marketing outcomes. They inform strategy, resource allocation, and long-term planning.
Many organizations know who their top customers are but struggle to identify similar prospects. Broad targeting and generic prospecting often result in low-quality leads and inefficient spend.
Data Axle’s data science modeling solutions address this challenge by applying predictive analytics to identity-enriched data.
Look-Alike Models use existing customer data to score and rank businesses or consumers based on their likelihood to exhibit similar behaviors to high-value customers. Models can be built using Data Axle’s Business, Consumer and blended databases, allowing organizations to align prospecting strategies with their specific goals.
Because the model can be refreshed on a recurring basis, it remains relevant as customer behavior and market conditions change. This makes it suitable not only for short-term campaigns but also for sustained prospecting and growth initiatives.
When identity resolution, analytics, and modeling are aligned, the impact is cumulative.
Unified data enables better insight. Better insight improves modeling accuracy. Stronger models lead to higher-quality engagement, improved conversion, and more efficient growth.
Industry analysis shows that personalization strategies built on accurate identity resolution can deliver five to eight times return on marketing investment and increase sales performance by 10% or more.
Identity is no longer a supporting function. It is a foundational capability that determines how effectively organizations can use data, apply AI, and drive growth.
Progress does not come from collecting more data. It comes from understanding people more completely.
One of the features clients consistently value most about ProfileFuse is the inclusion of confidence scores attached to each consumer-to-business linkage. These scores quantify how reliable each matched connection is, giving teams measurable assurance about the quality of the identity resolution being applied.
Confidence scores are generated by examining multiple matching signals across the consumer and business records. While the specific proprietary methodology reflects Data Axle’s unique data assets and AI, the underlying logic aligns with industry practice in identity matching. Algorithms evaluate components such as:
Each linkage receives a score that reflects the combined strength of these signals. A higher score means greater confidence that the personal profile and business contact truly belong to the same individual. This allows teams to prioritize high-confidence matches for activation and analysis while allocating appropriate review or filtering to lower confidence links.
For example, a sales team may choose to act immediately on linkages with the highest confidence scores when opening cross-sell or upsell opportunities. At the same time, analytics teams can tune look-alike models using linked identities above a certain score threshold to improve precision.
In many implementations, this confidence metric becomes as important as the linkage itself. It gives marketers and analysts a shared language to assess match quality, refine targeting rules, and justify decisions to internal stakeholders.
Want to learn more? Get in touch.
A longtime Data Axle Veteran, Doug Parsonage has been helping licensing clients exceed their business goals for the last 30 years. Doug is skilled in customizing solutions to best fit the needs of his clients, developing and managing client relationships, and growing key strategic partnerships. His passion is helping clients in the local search, navigation, local listings management, insurtech/fintech, and direct marketing industries. Doug holds a BA in English from Miami University, and in his free time you can find him cheering on the Nebraska football and volleyball teams, listening to the newest indie rock artist, and spending time with friends and family.