Best sellers are dead: how marketplace algorithms redefined what wins

The idea of a "best seller" once carried weight. It was shorthand for a product that had won consumer trust, stood the test of time, and moved in volumes impressive enough to be noticed.

The designation conjured images of front-row shelf placement, proud promotional banners, and bestseller lists carefully compiled by human hands. These were products with legacy, celebrated for popularity earned through visibility, reputation, and actual sales.

Today, that legacy has been quietly rewritten. Somewhere along the lines, algorithms slipped in and claimed the role of arbiter. The products we now see as "top" are often not the highest selling, not the most loved, and not the most reviewed. They're simply the most aligned with an invisible set of machine learned criteria. Success is no longer about history; it's about hitting the algorithm’s sweet spot. The new question is not “what’s popular?” - but “what’s being promoted, and why?”

The Silent Shift: From Best Sellers to Best Signals

There was no press release when this shift happened. No memo went out announcing the end of traditional metrics. Yet the effects are clear for anyone watching closely. Legacy brands that once dominated categories on name alone are now flanked - and frequently outranked - by brands most shoppers have never heard of. What changed? In a word: signals.

Marketplace algorithms now optimise for signals that reflect engagement, immediacy, and freshness. Click-through rates, add-to-cart behaviour, time spent on product pages, image quality, and even the diversity of your listing titles contribute to whether your item gets surfaced. What the algorithm wants is movement: real-time indicators that a product is compelling right now. Yesterday’s high sales volume might help, but it won’t guarantee top billing today. Algorithms live in the now.

Velocity Over Volume

Velocity matters more than volume. A product that sells 50 units in a day might outperform one that sold 500 last week if its signal velocity is higher. It’s not about how many have bought - it’s about how fast they’re buying, and how recently. This subtle redefinition changes everything for sellers. The shelf is now digital, infinite, and reordered in milliseconds based on behaviour, not reputation.

Algorithmic Criteria: What the Machines Really Want

If you peel back the curtain on how modern online marketplaces operate, you’ll find a curious mosaic of decision-making inputs. Product ranking is no longer a meritocracy of reviews or even price. It’s a swirling stew of user signals, catalog quality, listing freshness, search engine keywords, and behavioral predictions.

The Ranking Puzzle

Some marketplaces weigh click-through rates more heavily, others prioritise conversions. Many factor in “bundle logic”—the likelihood that a user buying one product will buy another in conjunction. Products that play well with others earn higher rankings. There’s a kind of gamified incentive for sellers to create ecosystems of interlocking items that feed off each other’s momentum. Think of it as a digital form of merchandising, where adjacency and compatibility matter as much as product quality itself.

Visuals and Structure

Images play a starring role. Algorithms scrape and scan for resolution, composition, and context. Listings with clean, optimised, professional-looking images tend to surface higher. Descriptions that are structured clearly - with title tags, bullet points, and variants cleanly marked - are also rewarded. This is where feed quality becomes the unsung hero of success. AI tools are reshaping the omnichannel game, enabling retailers to adapt swiftly to these algorithmic preferences. An optimised feed - clean, structured, up-to-date—communicates fluency in the machine's language.

The new best seller is algorithm compatible, not brand-dominant. In the online marketplace for e-commerce sellers, structured data plays a central role in influencing algorithmic visibility. Its rise to the top is choreographed through engagement metrics and structured listings, not through historical data. Machines don’t care about your legacy - they care about your layout.

The Fall of Legacy Brands (and the Rise of the Unfamiliar)

Legacy brands are experiencing something of an identity crisis in the marketplace economy. These once untouchable titans are finding themselves edged out by upstart labels with no marketing budget, no name recognition, and no shelf space history. And yet, these new players keep showing up first.

Agility Over Authority

Why? Because newer brands often have the advantage of being built for the current system. They don’t carry baggage - they adapt freely to algorithmic preferences. They tweak product titles to emphasise trending keywords. They rotate images to test engagement. They A/B test copy and bundle aggressively. Legacy brands, by contrast, often rely on name value and outdated catalog structures that confuse modern systems.

There’s also the speed factor. Agile sellers with smaller catalogs can move faster. They can optimise faster, respond to trends faster, and push updates faster. This velocity is rewarded. Algorithms don’t value tenure - they value traction. Brands that experiment and evolve in real time can outperform incumbents still operating on quarterly cycles, as evidenced by the 2024 RTIH Innovation Awards winners.

What used to be a moat - brand recognition, extensive catalogues, rich histories - is now a weight. Platforms like Amazon, for instance, take a larger and larger cut of sellers' earnings through the various fees it levies on them, further pressuring profit margins. In the new paradigm, success comes not from being known, but from being discoverable. And discovery is algorithmic.

Feed Quality as Kingmaker

If there's one variable that consistently influences marketplace visibility, it's the quality of your product feed. This is the central nervous system of your presence online. It's how your catalog speaks to the machine. And when that feed is inconsistent, outdated, or messy, the algorithm takes notice - and takes action.

Structured for Success

Think of the product feed as the connective tissue between your content and the algorithm’s eyes. It includes everything from product titles and descriptions to metadata, pricing, availability, category tagging, and image references. A well structured feed creates clarity. A poor one generates ambiguity, which the algorithm doesn’t like. Ambiguity leads to demotion.

More than any single design or promotion effort, the structure and cleanliness of your feed can decide your product’s fate. Some sellers optimise for humans and forget that their first audience is a machine. But the smartest brands now treat feed optimisation as a full-time strategy. Because that feed, quietly running in the background, is what determines whether you show up in the first place.

This is where tools like Feedonomics enter the picture - not just as software, but as strategy, a focus highlighted in platforms like the RTIH AI in Retail Awards. Mastering how your data is presented, and continually refining it, is no longer optional. It’s the price of entry.

Marketplace Success Is Now Behavioral, Not Transactional

For sellers stuck in the mindset of measuring success by unit sales or revenue alone, there’s a rude awakening waiting. Modern marketplaces reward interaction, not just transactions. Algorithms are designed to identify intention, and surface listings that generate it.

The New Engagement Metrics

You might sell fewer items overall but earn higher placement if your product listings generate strong behavioral signals. These can include:

● High dwell time on product pages – When users spend more time on a product page, it signals strong interest, even if no immediate purchase is made.

● Scrolling activity within the listing – The deeper a user scrolls through a product listing, the more they’re engaging with the content, suggesting meaningful curiosity.

● Users sharing or saving the product for later – These actions reflect future buying intent and social validation, both of which are weighted positively by algorithms.

● Repeat visits to the same product – When users return to a listing multiple times, the algorithm detects this behavior as a sign of high relevance and potential conversion.

● Interaction with rich media elements – Clicking through product image galleries, watching videos, or zooming into photos indicates that users are immersing themselves in the listing.

● Click-throughs from search results – If a product consistently earns clicks in search, it's a strong sign that its title, price, and image are aligned with user expectations.

● Cross-navigation to related products – When users explore bundled or recommended items, it suggests compatibility and increases ecosystem value.

These signals aren’t always tied to purchase. But they signal something deeper: relevance. And in the algorithm’s eyes, relevance is more predictive of future success than past sales. This means traditional sales metrics are just one piece of a broader behavioral puzzle.

Retailers who understand this can shift their strategies accordingly. They can begin to design listings not just to convert, but to engage. They can treat product content as dynamic - not fixed - and create systems that respond to feedback. What matters most isn’t what the customer buys, but what the algorithm sees.

Algorithms Aren’t Biased - They’re Blind

It’s tempting to think that algorithms are favouring certain products or sellers unfairly. But what they really favor is compliance. Compliant listings - those that fit expected formats, contain relevant data, and produce the right signals - get surfaced. Non-compliant listings fall behind.

No Special Treatment

The game isn’t rigged. It’s rule-based. And those rules are largely invisible but surprisingly consistent. If a product listing includes outdated titles, low resolution images, or inconsistent availability info, it risks being demoted - not because of its value, but because of its unreadability. This isn’t bias - it’s blindness.

Sellers who succeed are the ones who remove friction for the machine. They don’t just sell products; they sell data that is easily digestible. They think in schemas, not slogans. Their success is subtle, systemic, and sustainable.

For retailers still focused on traditional promotional levers - discounts, brand campaigns, shelf placement - the shift to algorithmic thinking requires humility. It means realising that success now hinges on digital fluency more than creative instinct. And that fluency starts with understanding what the machine is looking for.

We’re All Data Sellers Now

The phrase “best seller” no longer represents a product that has simply sold well. It represents a product that has mastered its digital presentation. In the algorithmic marketplace, nothing is fixed - not rankings, not visibility, not even what qualifies as “best.” The winners are those who adapt fastest, learn fastest, and structure best, embodying the spirit to celebrate global tech innovation in a fast-moving omnichannel world.

This is the silent revolution of modern e-commerce: the rise of the feed as the foundation of visibility. As strange as it sounds, selling now begins not with a product, but with how that product is rendered into data. The packaging is invisible, the shelf is infinite, and the audience is a machine. Welcome to the new order.

The rules are quiet, the competition is relentless, and the rewards are algorithmic. For those who still believe great products alone rise to the top, it’s time to rethink. Because in the online marketplace for e-commerce sellers, it’s not the product that wins. It’s the feed.