The Biggest Challenges for Ecommerce Businesses in Search and LLM Marketing

1. Search Has Changed. Has Your Ecommerce Strategy?

Running an online store has always demanded adaptability. But the speed of change in search and AI marketing over the past two years has left many ecommerce teams caught between the old playbook and a landscape that barely resembles what it was. Google AI Overviews now appear above traditional results for a vast number of queries. ChatGPT, Perplexity, and similar tools are shaping purchasing decisions before a shopper ever visits your store. And yet, many ecommerce businesses are still executing strategies built for a world that no longer exists.

What follows is a direct look at the real challenges facing ecommerce operators in search and LLM marketing, and why solving them requires both strategic clarity and hands-on execution.

Ask any traditional ecommerce team what keywords they want to rank for, and the answers are predictably generic. "Dresses." "Watches." "Running shoes." These keywords used to make and break a company (or the agency) with a search position. Today these terms carry ego and aspiration, but they rarely carry commercial return. They are crowded, expensive to compete for, and increasingly dominated by AI Overviews that absorb the click before a user ever reaches your store.

Meanwhile, the shopper who types "organic leather laptop sleeve 15 inch" or "Marge Sherwood burgundy grandma handbag" is ready to buy. They have already made most of their decision. They need a product page that matches their intent precisely, not a category page optimised for a broad term that triggers an AI summary nobody clicks through.

The fix is not complicated, but it does require discipline. Long-tail, product-led query research, aligned to real purchase signals, performed at the category and collection level, produces the kind of visibility that actually converts. Transactional queries are one of the areas where AI Overviews are less dominant, which is a meaningful advantage for ecommerce stores willing to build their strategy around specificity rather than volume.

2. An Unhealthy Dependency on Paid Media

Large brands can afford to bid on broad terms indefinitely. They absorb rising cost-per-click as a cost of maintaining their position on page one, partly to protect the brand, partly to prevent competitors from owning that space. Smaller and mid-sized stores often try to follow the same model and find themselves trapped in a cycle where performance depends entirely on the ad budget remaining active.

When spend slows, visibility drops. When visibility drops, revenue follows. There is no organic foundation to fall back on because the investment was never put in the first place.

Paid media has a clear role in ecommerce marketing, particularly for product launches, seasonal peaks, and retargeting. But building a business on paid channels without developing organic search equity leaves you permanently exposed. The customer acquisition costs that look acceptable today become unsustainable as competition increases and bid prices climb.

Organic search, built on content with intent, strong technical foundations, and a clear site structure, compounds over time. The investment made in a well-structured collection page today continues to generate traffic and revenue months and years from now. It's the kind of return paid media can't replicate.

3. Themes and Templates That Work Against You

A significant portion of ecommerce stores are built on themes that were not designed with search in mind. They ship with heavy scripts that slow page load, limited internal linking capability, weak or missing structured data, and very little space for the kind of rich, contextual content that both search engines and LLMs use to understand what a page is about.

Teams end up spending their time working around these structural constraints rather than improving the things that actually drive rankings and revenue. The platform becomes an obstacle.

Shopify, WooCommerce, Webflow, Magento, and custom builds each carry their own patterns and limitations. Addressing them properly means understanding canonicalisation, permalink structure, schema deployment via GTM, and metadata systems that can scale with a growing catalogue without breaking under the weight of thousands of SKUs. This is not work that most theme developers think about. It requires a different kind of expertise.

4. Audits That Produce Reports, Not Results

The ecommerce SEO audit has become something of a ritual. A crawl is run, a spreadsheet is generated, and a document arrives listing slow pages, missing ALT text, duplicate metadata, keyword cannibalisation, and thin content. The document is thorough. The prioritisation is absent. The implementation support is non-existent.

This pattern produces activity without progress. Teams cannot act on a list of 400 issues without knowing which thirty of them will actually make a commercial difference. Without clear prioritisation tied to revenue impact, and without someone to support the implementation, those findings sit in a folder and gather no value.

A properly structured audit does something different. It identifies the technical health of the site, reviews category and product architecture, benchmarks AI and LLM visibility, maps keyword and topic opportunities, and produces a plain-English action plan with a clear order of priority. It answers not just "what is wrong" but "what should we fix first, and why."

5. Ignoring How LLMs See and Describe Your Brand

Traditional SEO dashboards show rankings and traffic. What they do not show is how ChatGPT, Google's AI Overviews, Perplexity, or other language models describe your brand when a potential customer asks for a product recommendation. This is a growing blind spot, and it is creating real competitive risk.

LLMs are trained on the web. They develop associations based on what is written about a brand across publications, reviews, backlinks, citations, and mentions. A brand with a limitedpresence across the broader web, few specialist backlinks, and limited third-party references may hold reasonable traditional rankings while being effectively invisible in AI-generated shopping answers.

Google's AI Overviews can reduce clicks to websites by as much as 34.5%, and this makes it more important than ever to be cited as a source rather than simply ranked below the AI summary. Brands that invest in building the kinds of citations and authoritative mentions that LLMs associate with credibility will appear more often in AI-driven product recommendations, both at the brand level and for specific product categories.

The competitive intelligence question is no longer limited to "who ranks above us?" It now includes "which brands does the AI recommend when someone asks for what we sell, and why?"

6. Catalogue Complexity and the SEO Consequences

Ecommerce catalogues are not static. Products move in and out of stock. Ranges change seasonally. Bestsellers appear and disappear. New SKUs are added at speed. Each of these changes carries SEO implications that most teams are not equipped to manage consistently.

Strong URLs get removed without redirects. Internal link equity gets lost when categories are restructured. Schema and metadata go stale when products are discontinued. The result is an accumulation of technical debt (or garbage) that gradually undermines organic performance, often in ways that only become visible months after the damage has been done.

Managing catalogue changes properly means maintaining URL structures where possible, implementing redirects where pages are retired, preserving internal link equity across structural changes, and adjusting schema so that content continues to match search intent through every season and range update. It is systematic work, and it demands both technical capability and genuine understanding of how ecommerce operates day to day.

7. Omnichannel Complexity and Attribution

The modern ecommerce customer does not always follow a straight line to purchase. They discover on social media, research on Google, check reviews on Reddit, compare on Amazon, and eventually buy somewhere in that journey. Understanding which touchpoints actually drive conversion, and where organic search fits into that sequence, is increasingly difficult.

When organic search is not properly tracked across the full funnel, it tends to be undervalued. Paid channels get the credit because they are easier to attribute. As a result, budget flows toward paid, organic gets under-resourced, and the cycle of dependency continues.

Effective ecommerce search strategy identifies where organic and paid overlap, spots the search terms where you are currently paying for clicks you could be earning organically, and builds a case for the commercial value of long-term visibility. Search that works alongside paid media, rather than competing with it, produces a more stable and cost-efficient performance model.

8. The Platform Knowledge Gap

Not all SEO expertise is equal when it comes to ecommerce. General SEO practitioners understand rankings and content. What they often do not understand is how Shopify handles canonical tags, how WooCommerce manages permalink structures under certain configurations, how Magento generates duplicate content at scale, or how to implement JSON-LD schema via GTM across thousands of product pages without introducing errors.

These are the day-to-day realities of ecommerce SEO, and they require someone who knows both the search landscape and the operational mechanics of the platforms these stores run on.

Working with someone who has platform-specific experience means the strategy can actually be implemented rather than blocked by technical constraints.

9. Measuring the Right Things

As LLMs reshape search, the metrics that have traditionally defined SEO success are becoming less reliable. Rankings matter, but a first-place position below an AI Overview that absorbs most of the clicks tells a different story than a first-place position in an unadorned results page. Traffic metrics can be distorted by bot activity, which now accounts for close to 50% of internet traffic by some estimates.

Ecommerce teams need measurement frameworks that connect search activity to actual commercial outcomes. Which keywords are driving revenue from products that can sustain attention commercially? Which categories are generating qualified traffic that converts? Where is AI visibility strengthening or weakening brand presence relative to competitors?

Citation frequency in AI responses, branded search growth, and the relationship between organic visibility and customer acquisition cost are all signals that belong in a modern ecommerce performance dashboard. Tracking the wrong things leads to the wrong decisions.

10. Strategy Without Execution

Perhaps the most persistent challenge in ecommerce search marketing is the gap between what is recommended and what actually gets done. Strategy documents seem almost pointless if development resource is limited, the internal team is stretched, or the recommendations lack the specificity needed to action them without further support.

The stores that grow their organic search visibility are the ones where strategy and execution remain tightly connected. Schema gets deployed. Category trees get restructured. Collection and product content gets rewritten based on real conversion data. Internal linking gets built deliberately rather than left to chance.

This is not a creative problem. It's an operational one. And it requires a working relationship where recommendations are followed through, where platform constraints are understood and worked around, and where the work connects directly back to the commercial goals of the business.

What Ecommerce Search and LLM Marketing Actually Demands

The ecommerce industry is one of the genuinely interesting spaces left in organic search. Transactional queries still live in traditional results. The AI Overview problem is real, but it is far less acute for specific product searches than it is for informational content. There is competitive advantage available to stores that approach this correctly.

Getting there requires long-tail keyword strategy built around real purchase intent, technical foundations that support crawling and LLM discovery, platform-specific implementation knowledge, and an understanding of how AI systems currently describe your brand versus your competitors. It also requires the kind of measurement that connects search performance to revenue rather than to metrics that feel good but do not move the business forward.

The brands that have done this well, including those in jewellery, gifting, fashion, and specialist retail, share one common characteristic: they built a durable organic foundation instead of chasing quick wins that disappeared when the conditions changed.

Kyoom works with ecommerce and retail businesses on SEO and LLM strategy. If you want to understand how your store is currently performing in search and where AI visibility is either strengthening or undermining your position. Book a call