Economics of Customer Data
Untapped Power of First-Party and Third-Party Data Synergy
Over the past decade, businesses have witnessed a transformative shift towards data-driven decision-making. The meteoric rise of data platforms like Snowflake and Databricks or our very own (CDPs - Customer Data Platform) – a category I tirelessly championed during my time as a CDP company founder a decade back – has empowered organizations to harness the power of first-party data (1PD) like never before.
However, as we stand on the precipice of a new AI era and lots of 1st party data collected in the last 10+ years, the true key to unlocking unparalleled customer intelligence lies in the strategic integration of third-party data (3PD) with these internal insights in a timely way.
The "Data is the New Oil" Analogy Revisited The adage "data is the new oil" has become a widespread mantra, drawing parallels between the value of data and the refinement process of crude oil. However, this analogy only holds true in a few limited aspects:
Value Through Refinement: Like oil, the value of data increases through refinement and analysis, unlocking actionable insights.
Fundamental Differences: Unlike the homogeneous nature of oil, data is an entirely heterogeneous good, with unlimited varieties and subjective value based on the buyer's intended use case.
The Economic Realities of Data
Marginal Cost: The marginal cost of selling the same data to another buyer is zero. The cost of producing data is highly variable (sequencing a genome is more costly than taking your temperature), but once it exists, that cost is sunk. The process of selling it to another buyer is the simple act of copying it which, for all practical purposes, is zero.
Establishing Value: It is hard to establish the true value of data without "consuming" it. A database of sales leads is only valuable if it results in actual sales. To make things worse, the value of the exact same dataset is highly dependent on the buyer (or its intended use). In this regard, data is actually closer to "experience goods" like vacations, entertainment or books.
The Divide: Bridging 1PD and 3PD
First-Party Data (1PD): Collected directly from websites, CRM systems, and customer interactions, 1PD has long been the foundation for understanding customer behavior and preferences within an organization's ecosystem.
Third-Party Data (3PD): Sourced from external providers, 3PD offers a comprehensive view of consumer demographics, interests, life events, and market trends – a goldmine of insights for enhancing customer analytics.
The Integration Imperative While the benefits of integrating 1PD and 3PD are evident, the path to successful implementation is not without its obstacles:
Data Quality: Ensuring accuracy and reliability across internal and external sources.
Privacy Concerns: Maintaining strict data privacy standards and fostering customer trust.
Complexity: Seamlessly blending disparate data sets (Interoperability) into a cohesive, actionable whole.
Success Story in Enterprise: Global Retail Leader's Triumph A global retail giant faced significant customer churn challenges. By integrating their 1PD (transaction history, product usage, campaign engagement) with 3PD sources (consumer life events, interests, financial indicators), they built a powerful predictive churn model. This integration enabled proactive retention strategies, resulting in a remarkable 25% reduction in churn and millions in revenue savings.
But such success stories are easy to imagine if you have lots of dollars, time and resources to make it happen, otherwise if you are an operator or sponsor of such projects knows how hard it is to go into production with above integration challenges.
The Third-Party Data Conundrum While third-party data (3PD) providers have proliferated, Look at the growth of 3rd party data vendors There are over 300k data providers in the US alone, and likely billions of datasets. Many of them could give you a competitive advantage in whatever you are trying to sell, predict or analyze.
But average businesses often rely on a handful of common vendors i.e Zoominfo, Experian leading to vendor lock-in, limited data diversity, same data is used by their competition and an inability to seamlessly combine first-party and third-party data streams in real-time. This fragmented approach hinders optimized customer experiences, personalization, and revenue growth.
The Disruption on the Horizon: Just like how composability is disrupting software and tools, its going in the same direction for 3rd party data vendors opening up to business demand for pay per use, per field, or per row, tailoring data acquisition to their specific needs and use cases, rather than relying on one-size-fits-all solutions.
GenAI is also opening so many valuable data fields which can be extracted & leveraged to further expand your first party data.
Conclusion: The subjective value of customer data is realized when first-party and third-party sources are seamlessly linked and integrated in a tailored, composable manner aligned with specific business objectives. Rather than relying on one-size-fits-all data solutions, organizations must adopt a flexible approach that combines internal and external data streams to enable data-driven strategies purpose-built for their intended goals. The subjective value lies in the ability to fuel highly customized and impactful use cases like predictive analytics, personalization, and actionable insights for acquiring new customers, retaining existing ones, or expanding revenue streams. By transcending data silos and bridging the divide between first- and third-party data, businesses can reap the full rewards of an integrated, composable customer intelligence strategy that unlocks the true subjective value of their customer data.
Thank you for reading.
This is a 3 Part series, in the next part i will write about how leveraging & combining external data is broken at many levels including the current data marketplaces.