Effective identity resolution goes beyond separating consumer and professional personas. True personalization involves linking these identities to market to the 'whole person,' allowing for more contextually relevant messaging, such as targeting a professional with IT products during their personal hobby time (e.g., watching golf).
The traditional marketing focus on acquiring 'more data' for larger audiences is becoming obsolete. As AI increasingly drives content and offer generation, the cost of bad data skyrockets. Flawed inputs no longer just waste ad spend; they create poor experiences, making data quality, not quantity, the new imperative.
Many brands practice multi-channel marketing, addressing customers on various platforms, but fail at true omnichannel. The key distinction is context continuity, where each new interaction is informed by the previous one. Most brands still struggle with this, but combining predictive analytics with Gen AI is making seamless, contextual omnichannel experiences a reality.
Despite advancements in AI, achieving top-tier B2B data quality requires a hybrid approach. For example, Data Axel still makes 30-40 million phone calls a year to validate business information. This demonstrates that for high-stakes data, combining AI for curation with manual human verification remains essential for accuracy and reliability.