TransUnion and Actable have released new findings that reinforce a central truth about artificial intelligence: its success depends less on algorithms than on the quality of data behind them.

The companies announced that a joint project integrating TransUnion’s TruAudience® Marketing Solutions data into Actable’s machine-learning models delivered a 10 percent improvement in predictive model fit for AI-driven marketing use cases. The results underscore the growing consensus among marketers that AI adoption without a strong data foundation limits real-world impact.

The collaboration focused on a challenging “win-back” scenario for a major retailer seeking to re-engage customers who had shifted purchases to competitors—a use case typically constrained by sparse and unreliable data. By filling critical data gaps with TruAudience insights, Actable reduced false positives by nearly 20 percent, enabling more precise targeting and improved efficiency for high-cost channels such as catalogs and paid media.

“AI isn’t magic; its output is only as good as the information it’s given,” said Brian Silver. “Garbage in, garbage out still applies. These results show that when you start with a strong data foundation, AI can deliver meaningful lift and real return on investment.”

Also Read: When Bad Data Breaks the Sales Pipeline

Why Data Quality Still Matters

As marketers accelerate AI adoption, poor data quality and fragmented identity resolution remain major obstacles to accurate prediction. TransUnion said its identity graph and enrichment capabilities provide the consistent, high-confidence input AI systems require, covering more than 98 percent of the U.S. population with over 700 demographic attributes and 15,000 behavioral signals.

“TruAudience data proved most valuable where knowledge gaps existed,” said Matt Greitzer, co-founder of Actable. “This partnership demonstrates how third-party intelligence can unlock better outcomes for marketers.”

Also Read: Human-in-the-Loop Isn’t a Crutch. It’s the Safety Net.

Business Impact and What Comes Next

The improved model allows marketers to allocate budgets more efficiently, particularly in expensive win-back campaigns where misfires are costly. Beyond churn reduction, the companies identified several additional use cases where enriched data could materially improve AI performance, including:

• Targeting site visitors with limited first-party data
• Prospecting for new customers
• Marketing luxury goods and long-consideration purchases, where behavioral signals are weak

As AI becomes embedded across marketing organizations, the results point to a clear conclusion: companies that invest in identity resolution, data connectivity, and enrichment will be best positioned to turn AI from experimentation into sustained business value.

In an era of rapid automation, the lesson remains old-fashioned but essential—better decisions still start with better data.

The post Why Better Data, Not Better AI, Drives Results appeared first on MartechView.