Customer data strategy
Customer Data Strategy
Designing a data strategy for a Fortune 500 organization
- employees at the Fortune 500 org studied
- 420k+
- teams identified and interviewed in discovery
- 43
- of stakeholder interviews over one month
- 64 hrs
- north-star input metrics: Value, Agency, Trust
- 3
Designing a Customer data strategy
Customer data strategy begins with a deep understanding of three interconnected elements: The Business, The Customer, and The Ecosystem they both operate in. Each lens offers a distinct perspective on data and insights, and omitting any one of them leads to a limited view. The Customer reveals unmet needs, desires, and pain points, which provide The Business with value propositions to shape meaningful services. These services must align with business rules, industry best practices, and external regulations—factors governed by the broader Ecosystem in which the business exists. Only by considering all three can we create a strategy that fully integrates data, meets customer needs, and complies with the operational landscape.
First we diverge
The first step in designing anything meaningful is understanding the opportunity space. We explore this space by identifying the key factors that shape it—technology, people, processes, and physical environment. The organizational data strategy discussed here was developed for a company with over 420,000 employees and a place on the Fortune 500 list. Additionally, this organization boasts one of the most advanced targeting and personalization systems in the industry. In this section, I will detail how we assessed this intricate system, prioritized findings, and scaled solutions to guide the business and its customers toward an optimal future.
Many Many Many Teams
The intricate nature of personalization systems demands substantial effort to continuously maintain, enhance services, and ensure regulatory compliance. A critical step in the data strategy design process is identifying key stakeholders. By the conclusion of our discovery phase, we had identified and interviewed 43 teams. We thoroughly documented their data intake, processing workflows, and outcomes. Over the course of one month, approximately 64 hours of interviews were conducted, resulting in a comprehensive view of the organization’s structures, processes, team missions, data exchange points, current and future work, as well as the challenges these teams face in the current landscape. The cloud above reflects the names of each team involved in this discovery process.
Artifacts of Primary Research
Understanding an organization is a deeply involved process. Between teams, there are distinct data processes and practices, typically managed by different groups, with specialized knowledge often concentrated in individual members. These pockets of expertise contribute to the complexity, but they are also an inherent feature of any large, intricate system. It’s crucial to develop a research strategy that guides how data is gathered and formatted throughout the process.
Using a series of structured information collection templates serves two purposes. First, it allows interviewers to organize the data in real-time, ensuring consistency. Second, it reduces the need for extensive synthesis after the interviews. The template below was designed to capture and organize key data points during each interview, streamlining the process from start to finish.
This template was designed to capture and organize the information gathered during the interview process. The selected topics aim to provide a clear understanding of each team’s composition, responsibilities, and their overall value to both the business and the customer.
During interviews, data comes in various forms. While the Who, What, Why template is effective for capturing the core of a conversation, it lacks the capacity to document the images and workflows that are often shared during the discovery phase. To address this, we created containers to capture and organize these visual elements. These images and flows provide valuable insights and are actively leveraged by the team. Additionally, mapping out workflows helps pinpoint blockers in the process. The collection of these blockers offers the investigation team a prioritized list of opportunities that can complement the overall data strategy for the business.
Containers are used to capture distinct workflows or programs. They provide a flexible space that accommodates various types of data, such as images, PowerPoint slides, swim lane flow diagrams, and more.
A unique outcome of the discovery work is this two-by-two plot, which helps classify the data each team handles within their domain. Implicit data refers to information inferred from a customer’s behavior or other sources, while explicit data is directly collected from the customer or derived from their actions. Non-discrete vector data includes multiple values that require an additional key for retrieval, whereas discrete data consists of a single or fixed set of values with a clear label. By plotting these data types based on how teams receive and process them, we gain a quick and clear understanding of where each team sits within the value chain.
Putting Complexity on paper
The diagram below illustrates how data flows across the organization and how each team interacts with one another. The breadth of this diagram enables anyone in the organization to grasp what systems exist and how they are interconnected. One key insight was the realization that both Privacy and Fraud teams have system oversight, providing business rules for loss prevention and ensuring compliance. While assembling this diagram delivers significant value for the business on its own, it’s important to recognize that the perspectives of the customer and the broader ecosystem are still missing.
Is there value in understanding the Discourse.
In a previous discussion, we introduced 'The Ecosystem' as a critical lens for deriving insights that inform the formulation of a Customer Data Strategy. This section explores a specific use case, demonstrating how a nuanced understanding of societal dynamics—encompassing political forces, technological advancements, legal and regulatory frameworks, social movements, and prevailing ideologies within The Ecosystem—can generate predictive insights. These insights are invaluable for identifying potential threats to the organization's status quo and for developing medium- to long-term strategic responses.
Step One #1 understanding discourse: Find your key words
I often find that the initial search terms I use don’t fully capture what I’m actually seeking. This is quite common, and I suspect you may also refine your search terms multiple times throughout the process. This happens frequently due to the varied terminologies used across different disciplines. With my background in Architecture, the Arts, and Marketing, I’ve become familiar with this diverse linguistic landscape. There are three primary avenues to begin your research: scholarly literature, journalistic reports, and in-depth audiovisual materials. Contemporary qualitative software tools are invaluable for organizing and analyzing information from these varied sources. Another potential resource is market research, though I approach it with caution. While it can be attention-grabbing, I often find it lacks the critical depth and introspection that other sources provide.
Above are some channels to get you started on your own.
The objective is straightforward yet nuanced: to consume, organize, record, and identify connections in data, continuing until plausible causation emerges. Picture the Ouroboros, with yourself as the head, constantly ingesting information. When you start encountering recurring themes from the same sources, you've essentially reached the tail—signifying the boundary of the discourse. Each channel offers a unique perspective: scholarly literature provides deep dives, ranging from pragmatic analyses to philosophical reflections; journalistic reports reveal the public-facing aspects, highlighting controversies or emerging trends; and in-depth audiovisual content offers practical viewpoints. I recommend platforms like YouTube and podcasts as credible sources of data, based on the premise that an extensive interview with a subject matter expert can yield valuable insights. However, it’s crucial to balance these with other sources and diverse viewpoints.
There’s a dynamic interplay between these channels. Journalists and market research reports often reference academic studies, while practitioners respond to these reports in their work, and, when interviewed, reflect on major consultancy findings. Journalists, in turn, extract soundbites from these interviews. This intertextuality highlights the importance of critically evaluating and synthesizing information across these mediums.
Managing insights
The sheer breadth and depth of research data today can feel overwhelming, but there are tools that can help manage this endless sea of information. My personal preference is MAXQDA, a powerful software designed for qualitative and mixed-methods research. It’s widely used across disciplines like sociology, political science, psychology, public health, anthropology, and education, to name a few. No, I’m not sponsored by them—but I genuinely find it incredibly helpful.
Insight Lead research
It’s crucial to recognize the biases you bring into your work, as well as those that emerge as the work evolves. Gaining a holistic view requires openness to multiple perspectives. Reflexivity is key. Read, listen, and continue reading until the material begins to reference itself—that’s when you know you’ve captured a meaningful slice of the discourse. This foundation will help shape your opinion and guide you on where to explore next. You may find yourself diverging from the prevailing discourse, and that’s not a bad thing—but it requires solid evidence to support your different viewpoint. I find these moments exciting, as the primary evidence often doesn’t fit neatly into the established narrative. You now hold a personal, likely well-grounded perspective that adds fresh value to the conversation. Publish this view!
Bringing it home
Congratulations, you’ve now gained the knowledge to be considered a subject matter expert—at least for a while. So, what’s next? You could write a report that only a few will read, create a presentation that some may sit through and find inspiring, or distill everything into a call to action that you can repeat in meetings to guide next steps. I suggest doing all three. In this section, I’ll focus on the latter, which took the form of a North Star.
The North Star Framework is a concept widely used in product management and growth strategy. It emphasizes focusing on a single, critical metric—known as the "North Star Metric"—to guide a company toward its long-term goals. This framework helps teams align efforts across functions by concentrating on the activities that drive sustainable growth.
For customer data strategy, the key North Star concept is Customer Data Reciprocity: the practice of data exchange for mutual benefit between a customer and (insert company name). Yes, hundreds of hours of research, thought, and meetings distilled into one sentence. This is the message you drive home in every meeting and strategy call until people begin to internalize it. Defining the metrics that foster positive reciprocity is crucial, and these are areas where individual teams can contribute. For Customer Data Reciprocity, the input metric categories are Value, Agency, and Trust.
Where the fun begins
Once you’ve settled on a direction, the next step is to lead. Defining each metric and securing the buy-in needed to drive change usually follows this point. If you love research, be prepared—the fun shifts after this stage. Now, your goal is to steer the proverbial ship toward the agreed-upon direction, managing both up and down the organization. This is where I often find myself, and it brings a whole new kind of fun. Your research gets heard and tested. As different people engage with the work at a higher level, new directions begin to emerge—all thanks to your initial efforts.
Then we define the vision
Discovery told us where the organization stood. The next piece of work, done with a research partner, was a vision for where customer data strategy should go: what it is for, how it earns trust, and how a compliance-driven function can become a source of customer value.
