This article was based on the interview with Chip Lewis, Director of Web Analytics at Shutterfly by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
In enterprise marketing, data is never in short supply. Insight, however, is a far scarcer commodity. We are swimming in dashboards, reports, and terabytes of user events, yet the fundamental story of the customer—their intent, their frustration, their multi-session, multi-device reality—often remains elusive. We track clicks, opens, and conversions, but we frequently miss the nuance of the journey itself, particularly when that journey is less of a sprint to the checkout and more of a marathon of creation, consideration, and emotional investment. The challenge is moving from a transactional view of the customer to a relational one, using technology not just to measure, but to truly understand and improve the experience.
This is precisely the landscape that Chip Lewis, Director of Web Analytics at Shutterfly, navigates daily. With a career spanning back to the “Omniture days,” as he puts it, Lewis has witnessed the evolution of analytics from a simple reporting function to the strategic engine of the enterprise. At Shutterfly, the stakes are high. Customers aren’t just buying a product; they are investing hours, sometimes dozens of hours, creating deeply personal keepsakes like photo books and calendars. A single abandoned project represents not just lost revenue, but a significant loss of a customer’s time and emotional energy. By dissecting Shutterfly’s approach to this complex, high-effort journey, we can uncover powerful lessons on aligning technology with human behavior, making massive data sets actionable, and applying AI in ways that genuinely serve the customer.
The Cross-Device Reality: A Journey, Not a Session
It’s no revelation that customers move between devices. We’ve been discussing the “mobile-first” and “omnichannel” world for the better part of a decade. Yet, many organizations still operate with a fractured view, where the mobile app team and the web team analyze their data in relative isolation. The customer, of course, experiences the brand as a single entity. They start a project on their phone during their commute, browse for more photos on their tablet in the evening, and finalize the details on their desktop over the weekend. For a business like Shutterfly, where the source of content (photos) lives on mobile but the creation canvas is often better suited to a larger screen, this fluidity isn’t an edge case; it’s the primary use case. Ignoring it isn’t just an oversight; it’s a fundamental misunderstanding of the customer.
Lewis and his team recognized that this back-and-forth wasn’t a problem to be solved, but a behavior to be embraced and facilitated. The key was identifying the friction points created by inconsistent experiences. When the user interface, feature set, or even simple terminology differs between platforms, the cognitive load on the user increases, leading to confusion and abandonment. The analytics, therefore, had to focus on identifying where these disconnects were happening.
“We’ve spent a lot of effort examining that and trying to align the mobile app experience with the site experience more so that as users move back and forth, they’re not confused. We have found people getting… stuck where the experience was different. So I think we’ve really done a good job of aligning those experiences better. And we’ve learned, we’ve done that based on analytics data.”
The lesson for leaders here is that cross-device analysis is not a technical report; it’s a strategic imperative for customer experience. The goal isn’t just to attribute a conversion to a specific device but to ensure the journey between devices is as seamless as possible. Are you tracking user states across platforms? Can a customer save their work on the app and pick it up flawlessly on the web? As Lewis’s experience shows, using analytics to pinpoint where users get “stuck” when they switch contexts provides a clear, data-driven roadmap for product and UX improvements that directly impact the bottom line.
From Events to Users: A Necessary Shift in Data Modeling
For many marketers, analytics is still rooted in an event-based model. A user lands on a page (event), clicks a button (event), and adds to cart (event). This model works reasonably well for simple, transactional e-commerce. It falls apart completely, however, when faced with a journey that spans 30 hours, multiple sessions, and several weeks. How do you measure progress on a photo book project with event-based tracking alone? You can’t. You might see a “Project Start” event, but the critical story unfolds in the long space between that start and a potential “Add to Cart” event weeks later.
This is where the strategic shift to a user-based data model becomes essential. Instead of viewing the customer as a series of disconnected sessions and events, this model stitches everything together into a continuous timeline for each individual user, regardless of device or time. This was a pivotal realization for Lewis and his team. The most important KPI isn’t just whether a user starts a project, but how they progress, where they stall, and what behaviors correlate with eventual completion over a long period.
“The data model itself is user-based rather than event-based… it allowed us to easily [track] across device types, which is what I just finished talking about, but also across sessions.”
This is a subtle but profound point for any marketing leader at a company with a considered purchase cycle. Are you measuring relationships or transactions? An event-based model is good at the latter. A user-based model is required for the former. It unlocks the ability to understand complex behaviors, calculate a more accurate customer lifetime value based on deep engagement, and build sophisticated models for churn prediction. When a customer has invested 15 hours into a project, they are no longer a casual visitor; they are a highly committed user on the brink of a high-value conversion. Recognizing and nurturing them requires a data architecture that sees them as a person with a goal, not just a source of clicks.
AI as a Practical Tool for Reducing Customer Effort
AI is, without a doubt, the dominant topic of conversation in every boardroom and marketing meeting. The risk, as with any major technology wave, is that its application becomes a solution in search of a problem—more of a marketing gimmick than a genuine enhancement to the customer experience. The most insightful leaders, however, are looking past the hype and asking a simple, powerful question: How can this technology remove friction and reduce the effort required for our customers to achieve their goals? For Shutterfly, the “job to be done” is creating a beautiful, personalized product, but the biggest barrier is the immense time and effort it takes.
Lewis’s perspective on AI is refreshingly practical and customer-centric. He doesn’t talk about chatbots or generative ad copy. He talks about the core product experience. The vision is to use AI as an intelligent assistant to streamline the most tedious and time-consuming parts of the creation process, turning a 30-hour labor of love into a more manageable and enjoyable 5-hour creative session.
“I would love to have an AI help me build a photo book… It’s really time consuming… I think AI just has a huge potential to make the experience much easier and instead of spending 20-30 hours on my photo book I can do it in 5 or 6 and still have a product that’s just as good.”
He goes on to give concrete examples: AI could intelligently group photos from an event, sort them chronologically even when timestamps are messy, and identify the best images of specific people. This isn’t AI for AI’s sake. It’s the strategic application of technology to solve the single biggest pain point in the customer journey. For marketing leaders, this is the blueprint for a winning AI strategy. Don’t start with the technology; start with the customer effort. Identify the most arduous, frustrating, or complicated part of their experience with your brand, and then explore how AI can fundamentally simplify it. That’s how you create real value, build loyalty, and drive growth.
From Observation to Action
The insights from Chip Lewis and the Shutterfly story serve as a potent reminder that the ultimate goal of marketing technology and analytics is not simply to produce more comprehensive reports. The goal is to build a more empathetic and effective customer experience. It requires a commitment to seeing the journey through the customer’s eyes—a journey that is fragmented across devices, extended over time, and often fraught with effort. It means moving beyond simple metrics to embrace a more holistic, user-centric view of data that reflects this complex reality.
As leaders, the onus is on us to ensure our teams and our technology stacks are oriented toward this goal. We must continually ask whether our analytics are merely observing friction or actively helping us eliminate it. The potential of new tools, particularly AI, is immense, but only if we ground their application in solving real customer problems. By focusing on the high-effort moments in our own customer journeys and leveraging data to make them simpler, faster, and more enjoyable, we can build the kind of enduring brand loyalty that no amount of marketing spend can replicate. After all, the most powerful strategies are born from a deep understanding of the human on the other side of the screen.
