How Trove saves 3,000+ development hours a year with Contentsquare’s Product Analytics

Key insights

The company

Trove is a technology and operations platform that helps brands build and run their own resale and trade-in programs. From branded resale storefronts and trade-in initiatives to repairs and sustainable recycling, Trove gives its brand partners the tools and operational expertise to keep products in circulation longer—making circular commerce not just a sustainability statement, but a serious part of the business.

Rocco Giordano, Sr. Product Manager, and McCall James, Senior Data Analyst are responsible for delivering the behavioral insights and funnel reporting that 40+ brand partners depend on to run their programs.

The challenge

 As Trove's partner network grew and its product offering expanded, the limitations of manual event tracking became increasingly clear. 

A small data team was expected to deliver detailed, granular analytics across a diverse and growing portfolio that included in-store trade-in programs, online resale shops, repair services, and returns operations. Without a scalable way to capture behavioral data, this model simply couldn’t keep up. 

Key challenges included:

• Developers manually hardcoding event tracking into digital products, consuming approximately 252 developer hours annually across a six-person engineering team

• Data scientists spending time requesting and waiting on tagged events rather than analyzing data

• New partner onboarding taking up to two months—handled one partner at a time

• A fragmented data pipeline that made cross-partner and cross-platform comparisons unreliable

• Slow turnaround on analytics requests, limiting the team's ability to support data-driven decision-making across the business

The problem got bigger as Trove scaled. New partner types emerged—some taking on returns only, others running trade-in pilots, others running full programs. Each added demand to an already thinly stretched team.

The solution

Trove has been using Contentsquare's Product Analytics powered by Heap for seven years, making it a foundational part of how the data team operates. It provides the team with a single unified platform that automatically captures behavioral data across every Trove environment, so partners get full visibility into their programs. 

"Regardless of which trade-in platform a partner chooses for their implementation, we have one consistent set of events to build out funnels and reporting. The consistency of having Heap by Contentsquare as one platform tracking all of those events and funneling them into our database has been really helpful—otherwise those would all have had to be custom events hardcoded into our codebase, which creates a ton of engineering work and a lot of maintenance."

Rocco Giordano

Sr. Product Manager

How Contentsquare helped Trove

1. Eliminating manual event tagging with Smart Capture

With Smart Capture, Product Analytics automatically captures user interactions across Trove's digital products—no manual setup required. 

"Smart Capture has definitely helped where we haven't been able to keep up with new developments or new pushes to production. Because it retroactively captures the data, it's not like we're missing two months' worth of data."

McCall James

Senior Data Analyst

For a small data team, this has been transformative. When a new page, button, or feature goes live, the data is captured automatically and retroactively, meaning the team never misses behavioral data, even when they can't immediately respond to a new production push.

Contentsquare’s Smart Capture capability collects every interaction on your app or website from day one

When Trove launched a new repair plug-in for partners in Europe, Smart Capture allowed the data team to stick to their normal reporting timeline without scrambling to configure events before the pilot went live. All the data was there when they needed it.

2. Scaling partner onboarding from months to days

Before Product Analytics, onboarding a new brand partner to Trove's analytics environment was a lengthy, resource-intensive process—handled one partner at a time and taking up to two months to finish. With automatic event capture, setting up a new partner is as simple as creating a new project and switching it on.

The data team can now onboard multiple partners simultaneously—a shift that directly enabled Trove to hit its own internal KPI of dramatically reducing time-to-onboard. For a company focused on growing its partner portfolio, this was a significant advantage.

"It's as simple as creating a new environment within a project and just turning it on—all of those events flow through. There's no extra work from our side. It's made it a lot easier, and a lot fewer tasks fall through the cracks."

McCall James

Senior Data Analyst

3. Uncovering data insights at scale

One of the most strategically valuable applications of Product Analytics at Trove has been using Funnel Analysis to improve how consumers move through the trade-in experience. Unlike traditional ecommerce, there’s very little established industry research on what makes a trade-in flow convert well—meaning that brands running these programs are largely flying blind, and the teams that can answer those questions have a real edge. 

Trove's product team used Product Analytics event data to identify exactly where users drop off—and to test different implementations to find the right balance between collecting enough item information and keeping the experience frictionless.

This kind of iterative, data-driven product development wouldn't have been possible with manual tagging. Asking developers to build every new test scenario for every new flow is simply not scalable for a team of four.

"We used Heap by Contentsquare to test out different hypotheses, look at the data, and refine the product. The old manual tagging approach wouldn't have lent itself to that—you'd have had to ask your developers to manually build out all those different flows to track different test scenarios, and that's just not scalable."

Rocco Giordano

Sr. Product Manager

Another example that illustrates this well is a merchandising widget Trove built for their brand partner, a leading outdoor apparel retailer. The widget appeared as a tile on the partner's mainline shopping site, prompting customers to view the same item as a used, resale listing.

By using pre-existing Product Analytics events and simply splitting the data by UTM term (distinguishing users who arrived at the resale site via the widget from those who arrived organically), Trove was able to run a direct side-by-side comparison of session performance across both groups. No new tagging required. 

The team could see exactly how users who clicked through the widget behaved on the resale site compared to regular visitors. This gave the brand partner a clear, data-backed picture of how well the integration was driving resale engagement.

4. Empowering self-service analytics across the organization

The ease of use of Product Analytics has extended its value well beyond the data and product team. At Trove, senior partner managers and other non-data roles can access the interface directly—building quick reports, filtering by partner, and answering one-off questions—without having to submit a ticket. 

This self-service layer reduces pressure on the data team's bandwidth and lets them focus on higher-impact work, like building new reporting for product launches or refactoring data models.

McCall James came to Trove with no prior experience using the platform. Within weeks, she was fully operational—a reflection of how intuitive and user-friendly Product Analytics is.

"When I came to Trove, I had never used Heap by Contentsquare before. Being able to come in fully blind and have very minimal onboarding, I just found it very easy to get up to speed and actually be able to do anything I needed to do—with minimal experience."

McCall James

Senior Data Analyst

The results

The impact of Product Analytics on Trove's operations has been significant, improving engineering efficiency and reducing the data team’s workload:

• ~3,000 FTE hours saved annually by eliminating manual event tagging across six developers and three data scientists

• 4–12% year-on-year improvement in analytics request capacity, freeing the team to focus on strategic, higher-value work

• Partner onboarding reduced from months to days, enabling Trove to scale its partner portfolio without adding headcount

• A four-person data team now services 40+ brand partners with consistent, reliable, cross-platform reporting

For Trove, the value goes beyond saved hours. Product Analytics has changed what a small team can deliver—and given the business a genuine competitive advantage in an industry where behavioral data on resale and trade-in programs is still uncommon.

"Being able to have a reliable, stable platform that just works—that's the biggest win. It works well for what we're trying to deliver to our partners and doesn't require a ton of upkeep and maintenance on our side. That's huge when you're trying to grow your partner count with a fairly nimble team."

Rocco Giordano

Sr. Product Manager

What's next

As Trove expands its focus on returns management—one of the biggest challenges facing ecommerce brands today—the team is actively exploring deeper integrations between Product Analytics and Trove's operational software. 

The goal is to track associate-level interactions within warehouse and processing workflows, helping floor managers identify where staff are getting stuck and where efficiency gains are possible.

The team is also investigating Contentsquare’s Session Replay capability and working to expand adoption of Contentsquare across the organization. The roadmap reflects a simple truth: as Trove evolves, so does its use of the platform.

"Whatever a partner program looks like and whatever pieces of Trove they're using, we're able to support it because of Contentsquare. The future's always evolving—and Contentsquare gives us the ability to meet the needs of whatever we evolve to."

McCall James

Senior Data Analyst