Welcome Back
This is the third in our series of interviews with eCommerce leaders who are putting customer intent at the heart of their strategy.
We spoke with Ellis Osborn, Head of eCommerce at Ironmongery Direct, whose data-driven, AI-powered approach turns curiosity into conversions. For Ellis, understanding what customers want – often before they do – is the key to driving meaningful, sustainable growth.
Read on as we continue exploring how eCommerce teams are shifting focus from offers and acquisition to intent-led experiences.
About Ellis
Ellis is a seasoned eCommerce pro who’s built his career around one key principle: understanding customer intent. With a background spanning digital strategy, search marketing, and conversion optimisation, he’s helping brands turn high-intent traffic into meaningful growth.
Ellis is one of the eCommerce professionals redefining how we measure success using AI, automation, and data to create smarter, intent-led shopping experiences. From optimising multi-million-pound search budgets to scaling campaigns with AI, he’s focused on driving results that matter.
The Q&A
1. How have you seen the eCommerce market change in the last five years?
The transformation has been remarkable, with the pandemic serving as a powerful catalyst. We’ve witnessed a fundamental shift from convenience-driven shopping to experience-driven commerce.
Mobile-first has become mobile-only for many segments, with over 60% of our traffic now coming from mobile devices. The rise of social commerce, particularly through TikTok Shop and Instagram Shopping, has created entirely new customer acquisition channels. And that’s before we start talking about the latest announcements from OpenAI and Google’s “AI Mode”, which are set to drastically reshape the shopping experience for both consumers and businesses.
Most significantly, customer expectations have evolved from “fast and cheap” to “personalised and predictive”. They expect us to know what they want before they fully articulate it themselves (hence us needing to understand their intent before they do!!). The bar for user experience has been set impossibly high by Amazon, Netflix, and other tech giants.
2. What are the biggest challenges you face online, in terms of meeting eCommerce customer expectations today?
The primary challenge is the “Amazon effect” – customers expect same-day delivery, one-click purchasing, and Netflix-level personalisation regardless of our scale or resources.
Specifically, we struggle with:
- Attention span compression – Research shows customers make decisions in under three seconds. That means there’s zero room for error. From flawless imagery to perfectly optimised content, every element must work together to capture attention instantly and guide the customer toward a decision.
- Delivering personalisation at scale – This is hard to do without it feeling invasive, and it’s a constant challenge, especially when balancing a vast product catalogue with seamless navigation. With over 20,000 products, the question becomes: how do we show the right products to the right customer at the right time? A “joiner” should see a completely different navigation experience than an “electrician”. Relevance is everything!
- Managing inventory visibility – How do we do this across multiple channels and prevent overselling, given a limited budget and a large product portfolio? Specifically, how do we allocate budget effectively across products to ensure optimal visibility?
- Competition. Many smaller “ankle biters” have entered the industry, which has led to intense price wars and ‘paying for clicks’ on Google/Bing.
3. Roughly how many products do you currently stock, across how many different categories?
Across our portfolio, we manage approximately 20,000 SKUs spanning 14 primary categories and hundreds of sub and sub-subcategories. The challenge isn’t the volume – it’s making every single product discoverable and relevant to the right customer at the right moment.
4. What are the key strategies that your business is using to win in today’s market?
Our core strategy focuses on “contextual commerce” – understanding not just what customers buy, but when, why, and how they make those decisions. Our key pillars include:
- Micro-moment optimisation: Capturing intent signals within the first 2-3 page interactions.
- Utilising many different tools to see how users interact with our pages so we can build a profile on them – we then use this profile to change how we promote products to them.
- Predictive inventory positioning: Using demand forecasting to ensure high-intent products are always available.
- Cross-channel consistency: Ensuring seamless experiences whether customers start on social, mobile, or desktop.
- Community-driven discovery: Leveraging user-generated content and social proof as primary discovery mechanisms. This isn’t something we’re doing yet, but something we’re really keen on exploring.
Retention focus: It’s 5x cheaper to retain than acquire, so we optimise for lifetime value over initial conversion. This is something we put into place with our core bidding strategies on PPC.
6. Are there specific pain points you’ve observed in your customers' journey, such as difficulties finding relevant products?
Absolutely. Our biggest pain points revolve around several key customer experience challenges.
First, we’re facing a “search desert” phenomenon, where a high percentage of customer searches return irrelevant or insufficient results, leading to frustration and drop-off. Second, choice paralysis is a major issue as customers often abandon their journey when presented with too many similar-looking options, which is common in our extensive product catalogue.
Additionally, frequent context switching across multiple category pages causes customers to lose intent and momentum. We also see significant gaps between mobile and desktop experiences; what performs well on desktop often fails on mobile, prompting ongoing testing of different features and designs across platforms.
Lastly, inventory transparency is critical. When customers are unsure about product availability or delivery timing, they often abandon the purchase, so we’ve prioritised displaying this information at key stages in the shopping journey.
7. How do you utilise customer data to inform decisions about product placement, recommendations, or user experience?
Data utilisation occurs across multiple layers within our strategy.
At the micro-level, real-time behavioural signals such as scroll patterns, hover duration, and click sequences, drive immediate personalisation decisions, tailoring the experience as users engage with our platform. At the macro-level, broader trends like purchase history, seasonal patterns, and demographic data inform longer-term merchandising strategies, helping us align our offerings with customer segments and market shifts.
We also leverage predictive modelling through machine learning to anticipate customer behaviours and identify those likely to churn, ready to upgrade, or interested in new categories before they exhibit clear signals. Most importantly, we’ve transitioned from reactive analytics to predictive commerce, enabling us to make proactive decisions based on what customers are likely to need, rather than relying solely on their past actions.
8. What strategies do you use to reduce "dark aisles" (whereby large ranges of products within categories are completely invisible in search results and therefore undiscovered by customers) or irrelevant search results that lead to customers drop-off when they don’t find what they want?
This is one of our most critical challenges. Our approach includes:
Long-tail optimisation: Every product must be discoverable through at least 3-5 different search paths, not just primary keywords.
Semantic tagging (something we are really trying to get live sooner rather than later!): Products are tagged with use cases, occasions, and emotional contexts, not just specifications.
Cross-pollination algorithms: We actively surface products from underperforming categories to high-traffic category pages when contextually relevant.
Search result diversification: Instead of showing 20 similar products, we ensure search results span across price points, brands, and use cases.
Negative space analysis: We regularly audit zero-result searches and failed customer journeys to identify gaps in our discovery mechanisms.
9. How do you measure the success of these strategies in terms of customer satisfaction and conversion rates?
Our measurement framework goes well beyond traditional conversion metrics. We focus on understanding and optimising the full discovery journey, not just the final transaction. Here are some of the key metrics we consider:
- Discovery efficiency: How quickly users find relevant products after landing on the site.
- Browse depth: How many products or options customers evaluate before making a decision.
- Cross-category exploration: Whether users are discovering related product categories beyond their original intent.
- Return engagement: How often improved discovery leads to repeat visits or ongoing interest.
Assisted discovery: The role recommendations play in driving purchases, compared to direct search behaviour.
We also track micro-conversions throughout the journey. This includes things like saving a product for later, sharing it, or adding it to a wishlist. Often, a customer who thoughtfully curates future purchases is more valuable long-term than one who makes a quick impulse buy.
This ties directly into intent-led commerce, which marks a shift from reactive to predictive engagement. Instead of waiting for customers to signal their needs through searches or clicks, we use behavioural signals, contextual data, and predictive models to anticipate what they might need next and surface it proactively.
It’s the difference between a traditional shopping experience, where users must search and sift, and a more consultative, guided experience, where the platform helps uncover relevant solutions before the customer even asks.
10. Have you explored intent-led strategies to anticipate customer needs? If so, what results have you seen?
This is critical and is something we want to do soon as a ‘formal’ project. We do smaller ad-hoc pieces that cover this naturally, however.
11. How important is it to anticipate customer needs before they explicitly express them, and what role do you see intent-led strategies playing in achieving this?
This is critical for competitive differentiation. In today’s market, reactive commerce is commodity commerce. Every platform can show customers what they’re searching for – the competitive advantage lies in showing them what they need before they realise they need it. Intent-led strategies will become the primary differentiator because they solve the fundamental problem of modern commerce: choice overwhelming decision-making ability. When you can anticipate and simplify customer decisions, you create both superior customer experience and sustainable competitive advantage.
The businesses that master intent-led commerce will own customer relationships, while those that remain reactive will become commoditised fulfilment services competing solely on price and speed. The future belongs to platforms that act as trusted advisors rather than digital storefronts!!
Just wait and see what happens with OpenAI & AI Mode.
Are you ready to get intent-led with your approach to commerce? Join our intent-led community and receive the latest updates by following FoundIt! on LinkedIn – and for regular tips and insight delivered straight into your inbox, you can subscribe to our newsletter too.
