The Analytics Gap Most eCom Teams Don’t Know They Have | Raul Parquet, Dir. eCom (Princess Cruises)
Director of E-Commerce Raul Parquet explains how Princess Cruises is turning one of travel's most complex buying experiences into a seamless digital journey by building a strong analytics foundation.
When you think about e-commerce, booking a cruise is about as complex as it gets. Our guest today has spent 20 years building analytics setups across the top companies in the industry, with the goal of making these transactions dead simple to understand.
Raul Parquet is the Director of e-commerce at Princess Cruises, where he’s helping to lead them into a more digital future where visa requirements, multi-destination itineraries, and endless customization options are something customers can actually complete online.
In this episode, Raul shares:
The unglamorous but vital elements of a complete e-commerce analytics stack, and the table-stakes things teams often skip
Why an Analytics team embedded inside product is a requirement, and the deployment discipline that comes with it
And how Princess Cruises is using AI behind the scenes to help their team work smarter — and why, when it comes to customers, simplicity will always matter more than technology
1. Why embedding analytics inside your product team changes everything (6:00)
When analytics lives outside the e-commerce team, data loses consistency and collaboration breaks down across UX, product, and merchandising.
Raul’s fix?
Embed analytics directly in the digital team and put them in every planning meeting — from strategy to development.
“When those analytics team members are within e-commerce and within marketing, everything flows together.”
The product takeaway: Structure before tools. Get the team right first, and the data gets better automatically.
2. The #1 deployment mistake product teams make (8:00)
Most teams instrument analytics after a feature ships, but Raul says that’s already too late.
Every migration, every feature, every release needs tagging built in before it reaches customers. Combined with a throttled rollout and A/B testing, this lets you catch problems early (when you can still iterate) rather than after a full launch.
“We don’t normally just launch anything. We test everything.”
The takeaway: Analytics isn’t a QA step. It’s part of the build.
3. How Princess Cruises is using AI right now (and where it’s still unproven) (17:23)
There are two sides to AI for any product team:
What you use internally to move faster, and
What you deliver to customers
Internally, Princess runs on Microsoft Copilot — automating reporting, surfacing insights, and building executive presentations. But every AI output still gets a human review before it drives a decision.
On the customer side, service automation is the low-hanging fruit. Questions like visa requirements can be answered on-site by an AI agent before they ever reach the call center.
But conversion inside the booking funnel? That’s still an unsolved problem.
The takeaway: Deploy AI where it’s proven, and be honest about where it isn’t.
4. The analytics gaps hiding in plain sight (18:00)
Raul’s most common diagnosis when he looks at an e-commerce analytics setup: teams that track A to B and C to D, but accidentally skip B to C — and unknowingly lose visibility into a large part of their funnel!
His advice for every product leader?
Understand analytics fundamentals yourself, and bring analytics SMEs into every new project at the start — before UX is finalized, and before development begins.
The takeaway: Completeness of coverage matters as much as depth of reporting.
Links
Chapters
00:00 Simplicity Wins
01:44 Raul’s product background
03:50: Why cruises are one of the hardest ecommerce problems to solve
06:32 Embedding analytics teams into product
08:00 The #1 deployment mistake product teams make
13:29 Table Sstakes: What every ecommerce team should be monitoring
17:23 How Princess Cruises uses AI internally
18:00 The analytics gaps most teams don't know they have
19:47 The three-tool analytics stack for ecommerce
21:35 Simplifying complex bookings: The Tesla analogy
28:01 Where AI actually fits in the customer journey
32:35 Conclusion
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