The story of building a continuously improving product feed management system is one of several chapters. Like building a house, the first chapter begins with solid foundations, as we covered in Best Practice Product Feed Management: A Growth Story in Action. With the right foundations in place, online retailers move out of a reactive mode and begin renovating their product discovery strategy. The decisions and materials you then add to that sturdy slab of data determine whether future growth is shaky or sustainable.
Consider the tale of the Three Little Pigs. A house of straw is akin to a product feed strategy built on poor data and manual guesswork. It looks functional until it’s stress-tested. A house of sticks works better, with cleaner data and fewer errors. However, there are still creaks and cracks that widen when the winds of a competitive market blow. A house of bricks is built with optimized data, automated processes, and continuous testing. It doesn’t just survive; it remains resilient under all conditions. For digital performance leaders, the goal is to build that house of bricks. The trick is knowing how to build it.
Your product data feed is your media strategy
In the early days of Google Shopping, product feeds were predominantly an operational concern. They were often owned by technical or catalog teams, with campaign strategy sitting separately in the ad platform.
The shift toward treating product feeds as a strategic lever happened gradually. Some larger retailers were already treating feed quality as a performance lever well before the rise of Performance Max in 2021 and Meta’s Advantage+ Shopping Campaigns in 2022, which further accelerated the change.
Fast forward to today, and platforms like Google and Meta have evolved into massive AI-driven product discovery engines that no longer require humans to micromanage individual bids. These platforms are extremely efficient at helping shoppers discover products and brands, but only when they’re given the right data to work with. The algorithm is only as smart as the information it’s fed.
David Jones understood this shift and used it to fuel an aggressive growth phase. By focusing on improving product data quality for better ad performance through the Athos Commerce solution, the team didn’t just fix errors. They fundamentally changed what the platform could do with their budget. Working alongside their agency, Hearts & Science, the product data feed became the load-bearing architecture that allowed their media investment to reach even greater heights. Alex Carter, Head of Digital Experience and Operations at David Jones, described how this enabled much more precise targeting:
Alex Carter
The result speaks for itself. David Jones increased its digital investment by 97%, driving a 110% uplift in revenue. When your revenue growth outpaces spend, you’ve moved beyond simple scaling into structural engineering.
Continuous experimentation is the new competitive edge
Strong foundations and a feed-first approach set the platform for growth. But staying ahead of competitors requires something else: a willingness to keep testing, even when things are working.
Consumer behaviors shift, and seasonal intent spikes and disappears. What earned strong results last month may underperform during a peak period. The retailers outperforming their peers aren’t always the ones with the biggest budgets. Instead, they’re often running the most tests and rapidly implementing their learnings.
David Jones built this into their operating model using Athos Commerce Experiments. In April, the team ran a 30-day experiment on a specific product segment, testing optimized titles and attributes against a control group. The result was a 66% performance uplift. They applied the same approach to their Mother’s Day campaigns, testing keyword-rich titles to capture seasonal intent. Revenue for that segment increased by 62%.
Alex Carter
Individually, each test is a small refinement. Applied consistently across tens of thousands of SKUs, those refinements become a significant commercial advantage.
Beyond maintenance: turning automation into revenue
Automation doesn’t need to replace the human element of a digital team. The David Jones story shows that automation is what gives the human element room to operate at its best.
Before their transformation, the team spent a large amount of time on reactive maintenance. They were fixing disapprovals, chasing vendor updates, and manually patching feed errors. Progress was stunted because the foundations demanded constant attention.
By implementing automated rules and proactive error management through the Athos Commerce platform, real wins quickly appeared. Over 60% of previously inactive or disapproved SKUs were reactivated. This didn’t happen through a new budget or new product ranges. It happened by ensuring that existing product data was clean, correctly formatted, and visible to the algorithm.
That newly found bandwidth opened further doors. The team activated Merchant Sponsored Shopping campaigns and achieved a 23x ROAS.
Alex described the shift it created within the team itself:
When the operation stops being reactive, online retailers benefit. Automation becomes the mortar that enables the team to build a stronger product data management system, brick by brick.
Building your commercial reality
The results David Jones achieved weren’t gained from a single campaign or a one-off fix. Those results are proof of what happens when technology, data, and human strategy are aligned around a shared goal.
Treating the feed as the core of media strategy gave the team the confidence to nearly double digital spend while improving efficiency. A culture of experimentation uncovered growth where others might have seen stagnation. Automation handled the heavy lifting, freeing the David Jones team to make decisions that actually moved the business forward.
A 97% increase in digital investment. A 6% improvement in overall ROAS. A 23x return on newly activated ad formats. The house of bricks doesn’t just stand the test of time—it becomes a scalable architecture.
If your current ecommerce strategy feels like it’s built on straw, the most powerful place to start isn’t the ad platform. It’s the data that fuels it.
The Athos Commerce team exists to power peak performance for online retailers. We’re here to help you build the house of bricks through:
- Auditing and enriching product data to fuel smarter algorithms
- Aligning feed strategy with media investment for greater returns
- Automating feed management to free your team for high-value work
- Running continuous experiments to find growth across your catalog
- Connecting performance data back to feed decisions at every stage
If you’re ready to stop patching straw and start building a resilient architecture for product discovery, request your complimentary demo of the Athos Commerce Data Feed Management platform.
FAQs
What is product feed optimization and why is it important for ecommerce performance?
Product feed optimization is the process of improving the quality, structure, and completeness of product data used across platforms like Google Shopping and marketplaces. It is important because this data determines whether your products appear in search results and how relevant they appear to shoppers. In the David Jones example, improving feed quality increased visibility, reduced disapprovals, and supported stronger engagement, helping retailers earn the first click and build trust from the outset.
How does product data impact ad performance on platforms like Google and Meta?
Product data directly influences how platforms like Google and Meta deliver ads. When attributes, titles, and taxonomy are incomplete or inconsistent, campaigns struggle to achieve strong click-through rates and conversions. In the David Jones case, poor data limited visibility and required manual fixes, while improved data quality supported stronger performance and relevance. In other words, better product data creates stronger signals, and stronger signals improve ad efficiency and outcomes.
Why is onsite search critical for ecommerce conversion?
Onsite search represents one of the highest-intent moments in the customer journey. Shoppers using the search bar already know what they want and expect fast, accurate results. When search returns irrelevant listings or zero-results pages, trust breaks down quickly. When search aligns with shopper intent, it reinforces confidence and keeps the shopper moving forward. As a result, strong onsite search directly supports higher conversion rates and a smoother path to checkout.
What is the role of merchandising in product discovery?
Merchandising shapes how shoppers explore products when they are not using search. It highlights new arrivals, bestsellers, and relevant categories to guide browsing behavior and inspire purchase decisions. Throughout this article, you will see that effective merchandising acts as a series of trust signals, helping shoppers feel confident in their choices. When executed well, it reduces friction and improves engagement. When executed poorly, it increases effort and lowers the likelihood of conversion.
Where should retailers start when improving product discovery performance?
Retailers should start with strong product data foundations. What does this look like? It’s about ensuring attributes are complete, titles and descriptions are structured, and taxonomy is consistent across channels. Once this foundation is established, retailers can optimize onsite search and merchandising to improve the shopper journey. The David Jones example shows that improving data quality first leads to better visibility, stronger engagement, and more efficient performance across both onsite and offsite channels.
How do product feeds, search, and merchandising work together to improve results?
Product feeds, search, and merchandising form a connected system that shapes the entire customer journey. Product feeds determine visibility across platforms, search helps shoppers find relevant products quickly, and merchandising guides discovery and exploration. When these elements are aligned, they create consistent relevance at every touchpoint. In the David Jones example, improvements to product data supported stronger discovery, while better search and merchandising reinforced trust and supported conversion.
