Search Ranking

Imagine your search finds 200 products that match “running shoes”. All 200 are technically relevant, they’re all running shoes. But which ones appear first? This is what search ranking decides, and it has an enormous impact on what customers actually buy.

Studies consistently show that 70-80% of clicks go to the first three search results. Products on page one get 10 times more views than products on page two. If your best running shoes are buried on page five, customers will never find them. They’ll click on whatever appears first, whether those are the best matches or not.

This is why search ranking matters so much. It’s not enough for your search to find relevant products, it needs to show the most relevant products first.

How ranking actually works

When someone searches for “running shoes”, your search system gives each matching product a score. Maybe Product A scores 95 points, Product B scores 87 points, Product C scores 91 points. The products are then sorted by these scores, highest first. That’s your ranking.

But here’s the interesting part: how does the system decide how many points to give each product? This is where it gets sophisticated.

Modern search ranking considers many different factors, combining them into that final score. Let me walk you through what those factors are and why they matter.

Text matching: the foundation

The most basic factor is how well the product matches the search words. If someone searches for “blue running shoes”, a product titled “Blue Nike Running Shoes” should score higher than one titled “Athletic Footwear - Blue Available”.

The first product has all three search words in the title. The second has the words too, but they’re less prominent. “Running” appears in one, but the second product just implies running through “athletic”. These differences affect the score.

Position matters too. Words in the product title count more than words buried in the description. A match for “blue” in the title is worth more than “blue” mentioned once in the last paragraph of the description. This makes sense, titles are more important for identifying what a product actually is.

Popularity and social proof

Text matching alone isn’t enough. Imagine two products both perfectly match “blue running shoes” in their text. How do you decide which ranks higher?

This is where behavioral signals come in. If thousands of customers have bought one of those shoes and left great reviews, while the other has only a few sales and mediocre reviews, the popular one should probably rank higher. It’s a signal of quality and desirability.

The search learns from customer behavior. Products that get clicked often for a particular search move up in ranking. Products that get clicked but then immediately abandoned (customer returns to search without buying) move down, clearly they weren’t what people wanted.

Purchase history is an even stronger signal. If many customers search for “running shoes” and buy a particular product, that product becomes more relevant for that query. The search is learning: “when people search for this, they buy that.”

Stock and availability

Here’s a practical consideration: showing out-of-stock products first frustrates customers. They click, discover it’s unavailable, go back to search, click another result, and repeat. This is terrible experience.

Good ranking takes stock into account. In-stock products rank higher than out-of-stock ones. Products with low stock might get a small demotion, you want to show items customers can actually buy, not products about to sell out.

Some webshops handle this differently. They show out-of-stock items but clearly marked, letting customers sign up for restock notifications. Others hide out-of-stock items entirely from search. There’s no single right answer, but ranking should definitely consider availability.

Business priorities

Search ranking isn’t just about what customers want, it’s also about what makes sense for your business. Maybe you have two equally relevant products, but one has a much higher profit margin. Or one is overstocked and you need to move inventory. Or one is a new arrival you want to promote.

Good search platforms let you manually boost or bury products for specific queries. This is called merchandising control. You might create a rule: “For searches containing ‘summer dress’, boost products in the ‘New Arrivals’ category.” Now your new summer dresses appear prominently for that search.

This combines algorithmic ranking (letting the system figure out what’s relevant) with business strategy (promoting what you want to sell). The best approach uses both.

Personalization

Here’s where ranking gets really interesting: different customers searching for the same thing might see results in different orders. This is personalized ranking.

If you frequently buy Nike products, Nike running shoes might rank slightly higher in your search results. If you always buy size 10, products available in size 10 might get a small boost. If you’re on mobile, mobile-friendly products might rank higher.

This personalization happens subtly. It’s not dramatically changing results, just adjusting ranking to be more relevant to you specifically. Done well, it increases the chance you’ll find exactly what you want.

Seasonal and temporal factors

Ranking can also consider timing. In winter, “jacket” searches should probably show winter coats higher than light spring jackets. During Black Friday, products with the biggest discounts might get boosted. Right after Christmas, gift-appropriate items might rank lower.

Some systems even consider time of day. Morning searches for “coffee” might prioritize coffee beans and makers, while evening searches might show coffee table books or café chairs (people shopping while having evening coffee).

These temporal adjustments make ranking more contextual and relevant to what customers actually want right now, not what they wanted six months ago.

The balancing act

Here’s the challenge: all these factors pull in different directions. One product has perfect text matching but poor reviews. Another has amazing reviews but weak text matching. A third is slightly less relevant but is overstocked and you need to move it. How do you combine all these signals into one final score?

This is where machine learning helps. AI-powered ranking learns from millions of search-click-purchase patterns to figure out which factors matter most for different types of queries. For brand-specific searches like “Nike shoes”, text matching might matter most. For general searches like “running shoes”, popularity and reviews might be more important.

The system continuously learns and adjusts these weights, optimizing ranking based on what actually leads to purchases.

When ranking fails

Poor ranking is obvious when you see it. You search for something specific and the first results are barely related. Or you search for a popular product and it appears on page three, while less relevant items come first.

Common ranking problems include over-weighting popularity (popular products always win, new products never get discovered), ignoring stock status (showing unavailable items first), static rankings (not adapting to seasons or trends), and poor text matching (keyword games instead of genuine relevance).

Good ranking constantly monitors these issues. If a query has high search volume but low click-through rate, something’s wrong with the ranking. If customers constantly refine their searches, the first results weren’t good enough.

What this means for your webshop

Search ranking directly impacts your sales. When the best products appear first, customers find what they want quickly and buy. When ranking is poor, they leave frustrated or buy suboptimal products that they might return.

The good news is that modern search solutions handle ranking automatically. Systems like TextAtlas use AI to learn optimal ranking from your customer behavior, combining text relevance with popularity, availability, and personalization. The ranking continuously improves as it learns which products satisfy which searches.

You maintain strategic control through merchandising rules, boosting promotional items or seasonal products when it makes business sense. But the day-to-day ranking optimization happens automatically, learning from every search and every purchase to get better over time.

The result is search that consistently shows customers the products they’re most likely to want, driving higher conversion rates and customer satisfaction.