Customer Data Infrastructure's role in AI and Personalisation

In the rapidly evolving world of digital marketing and customer experience, two buzzwords have been dominating conversations: Generative AI and personalisation. It's clear that these technologies are at the bleeding edge of marketing transformation. However, there's a crucial component that often gets overlooked in this excitement: the quality and management of the underlying customer data.

The appeal of Generative AI is like a shiny new toy for marketers, yet its effectiveness hinges on the accuracy and quality of the input data. Without a robust customer data strategy, the promise of AI falls flat. Personalisation, as enticing as it sounds, is only as good as the data driving it. This brings us to an essential concept in modern digital marketing - customer data infrastructure.

Complexities of Data Unification and Maintenance

The challenge of unifying customer data is multi-faceted. It involves managing vast volumes of data, overcoming the fragmentation of data across multiple siloed systems, and navigating the lack of standardisation in data collection. Customer data is not static; it is dynamic and continuously evolves, reflecting changes in customer engagement, demographics, interests, and behaviors.

Establishing a unified customer view is only the beginning. Maintaining customer data infrastructure requires ongoing efforts:

  1. Creation: This includes data ingestion, identity resolution, data modeling, shaping, and workflow building.
  2. Maintenance: This involves protocols, data quality control, updates, workflow monitoring, compliance management, error resolution, and data governance.
  3. Change Management: Any introduction of new data sources or destinations necessitates a thorough and cautious process for integrating these changes without disrupting existing workflows.
Customer Identity and Changing Data Needs

Customer identity resolution and the unification of data into a usable format without the necessary infrastructure is complex, costly, and error-prone. Legacy systems and outdated approaches often fall short in accuracy and efficiency. The advent of privacy regulations and the phasing out of cookies further complicate this landscape.

Brands also face challenges in incorporating new data sources, maintaining data accuracy, and catering to varying user needs and permissions. The process of adding and utilising new data attributes is labor-intensive and fraught with risk of inaccuracies.

Navigating Change Management, Quality Assurance, and Customisation

Change management within customer data infrastructure is a high-stakes process. It involves coordinating across multiple tools and environments, often without a unified testing ground, making it both complex and risky.

Quality assurance is another area that demands significant attention. Standard approaches lack effective tools for mass quality checks, leaving businesses to juggle workflow coordination manually.

Customising tooling to fit specific business needs is also a formidable task. The integration of various tools often leads to data silos and inflexible working models.

Personalising Customer Experiences by combining AI and Data

The integration of advanced generative AI with customer data infrastructure stands out as a transformative approach and way forward. This enables brands to create highly personalised, customer-aware experiences at scale without costly content production requirements. The chemistry between advanced customer data platforms and powerful language models offers unmatched opportunities for personalised customer engagement.

Combining the strength of these large language models (LLMs) with customer data platforms (CDPs), enhancing every customer interaction, from marketing to customer service could offer brands the flexibility and control they need over their data, ensuring privacy and security while opening new avenues for personalisation.

Conclusion: Emphasising Robust Customer Data Infrastructure

The digital marketing and customer engagement landscape is fast moving towards AI-driven, personalised methods. The efficacy of these initiatives fundamentally depend on the management and quality of customer data. Businesses must recognise the pivotal role of customer data infrastructure in leveraging the full capabilities of generative AI and personalisation. By adopting comprehensive data strategies and embracing innovative AI integrations, companies can unlock new levels of customer engagement and business growth.

Written by: Adnan Khan Co-Founder, Stitch
Chairperson, MA Digital Special Interest Group
Email: adnan@stitchtech.co
Linkedin: https://www.linkedin.com/in/adnan2/