When first-party data is clean, comprehensive, and correctly labeled, it enables AI models to learn patterns, make predictions, and generate reliable insights. This leads to better marketing thanks to more relevant customer experiences. And because data is so foundational to AI-powered decisions, companies that use fresh, deterministic data can safeguard against biased, unethical outcomes caused by stale or inaccurate data, undermining trust with customers and partners.
When a brand’s AI model has been trained on inaccurate customer data, it can lead to targeted ads that are irrelevant or even offensive, resulting in alienating customers instead of engaging them. An uk rcs data engagement as simple as offering a mortgage to someone who just lost their job can damage brand equity. Business outcomes can also be impacted immediately – what if a brand’s AI-generated creative contains an offer that isn’t profitable to the business based on an expired promotion or an outdated customer attribute? That kind of mistake can be costly. Prioritizing data quality is critical to avoiding these pitfalls.
Create Checks and Balances with AI-Human Collaboration
AI offers a powerful way to centralize data and activate it cohesively across channels, including cross-departmental data. Organizations should implement automated processes to refresh data in real time and verify its accuracy to maintain high-quality inputs.