Ozon Search Working Principles

Search on Ozon is not only a system for ranking products by their textual relevance to customers’ search queries, but also a system for distributing traffic and determining products’ quality offers. Both customers and sellers influence search results.

Product position in the search affects its commercial indicators: conversions, traffic, and sales. The way the search is set up allows sellers to influence the position of their products in the search results in a direct, transparent and predictable way. Since quality offers from verified sellers are ranked higher in the search results, they get more traffic and therefore more sales.

Ozon also determines what a quality offer is based on the product price, its quality, and the delivery speed. For those sellers who can provide such products, Ozon offers free tools for promotion in the search results.

Learn more about raising products in search results

Needs that Ozon search addresses

• Motivate sellers to make high-quality offers.
• Help sellers demonstrate their products to customers.
• Help customers find and choose the necessary product.
• Take into account the customers’ choice when distributing traffic between sellers’ offers.

Main principles of Ozon search

• Inevitability. Search results always react to the actions of sellers or customers.
• Non-discrimination. The reaction of search results to the same actions of different sellers in similar situations is the same.
• Speed. Ozon is actively working to make the search engine response of search results to sellers’ and customers’ actions fastest possible.
• Transparency. All the details of the search operation on Ozon are open. Ozon doesn’t affect the ranking of products in the search results and doesn’t hide its work principles.

Ozon search scheme

The process begins as a customer performs a search on the Ozon website. It consists of several stages.

1. Request generation and candidate selection

   Every day customers make millions of search queries on the Ozon search engine. To make sure that everyone finds the necessary product among millions of others, at the first stage the search engine generates a large list of candidate products, whose names and various attributes contain words from the search query. For each request, this list can contain up to 500,000 products.

2. Base ranking layer

   The search engine sorts the received candidates to sort out 2,000 products with the highest text relevance. If necessary, the range expands, and the engine loads extra 2,000 products.

3. Middle ranking layer

   For the resulting list of 2,000 products we now need to estimate the probability with which customers may buy them. The ranking of these products depends on the product features that have proven to be most important to customers in the past. To figure it out, Ozon compares the product features and customer behavior. The result is a score for each product on a scale from 0 to 1.

4. Boosters

   Specific predefined boosting coefficients are applied to 2,000 products ranked from 0 to 1 after the previous step.

5. Paid promotion.

   The products are sorted based on the paid promotion rates.

Let’s take a closer look at the search stages.

1st stage: forming a request and selecting candidates

Search for words by product attributes

When a search query arrives, the system starts searching for words from this query in all fields and attributes of products that are stored in the search:

• product name,
• product category,
• brand,
• description,
• color, size, material, type and other attributes.

We check several hundred attributes, the final list of them depends on the product category. The more attributes a product has filled in, the easier it is to find it via search.

This list doesn’t include reviews and questions about the product. Therefore, it’s very important for the seller to fill in namely the product attributes: the more fully the product is described, the more likely customers will see it in the search results.

Converting and normalizing a query

Before selecting candidates, the search engine performs a chain of query transformations to make sure that the suitable candidates won’t be missed:

1. Enrichment with synonyms. This step is added to make sure that the query covers more products.For example, so that the “phone” query would return smartphones as a result.
2. Request normalization. All the words of the query are brought to a unified form so that, for example, both the “builder Johnson’s emerald ship” and “builder Johnson’s emerald ships” queries would have the same output. At this stage the system also excludes words that don’t carry a semantic load, for example, conjunctions.
3. Selection of candidates. All products containing all query words are extracted from the search index for all the remaining words. If the number of such products is too small, all products that contain at least one query word are also retrieved.

At this stage, the search results contain up to 500,000 candidate products. Their list is passed on to the second stage.

2nd stage: basic ranking layer

Sorting candidates by relevance

At the second stage, the selected candidates are sorted by text relevance. Here the search engine focuses on:

• The fact of the product’s textual correspondence to the query: at first the number of matches between the words from the search query in the fields and the attributes of the product for each individual word in the query is determined by the usual linear formula. Then the number of matches for all the words from the query is summed up.
• Product rating based on customer reviews. At this stage the rating is used as a multiplying coefficient. The higher the product rating, the higher the coefficient. It ranges from 0% for the minimum rating to 25% for the maximum.

As a result, all candidate products receive an assessment of basic relevance as a combination of text compliance and product rating.

At this stage, the search engine sorts the candidates and passes a list of 2,000 most relevant candidates on to the third stage. If necessary, the range can be expanded, and the engine will load extra 2,000 products.

3rd stage: middle ranking layer

How ranking works

At this stage, the search engine needs to evaluate which of the 2,000 products customers are most likely to purchase. Since the probability of buying each product is unknown, you need to predict it to be able to rank the products.

The prediction is made in several stages:

1. All the features of each individual product, that may affect the fact of sale, are calculated. Here each feature is a real number. For example, the product rating or its price can be called a feature in this context.
2. A machine-trained model is applied to a set of features. It predicts the probability of a sale based on the value of the features. Ozon uses gradient boosting of decision trees as a model.
3. The model outputs a number that is used to estimate the product purchase probability.
4. The evaluation of the purchase probability of a product is put into the context of the query. The same product will have a different purchase probability for different requests.

How a machine-trained model works

The purpose of the model is to most accurately predict the product purchase probability based on the product features. Product features contain information about the characteristics of products purchased by customers in the past, and those of them that the ranked product has now.

The model predicts future buying behavior based on this data collected in the past. For example, one of the important factors is the average product conversion to purchase over the last 60 days. For many products it’s possible to state that the product conversion doesn’t change over time. Consequently, the model uses this feature to predict purchase probability, and it’s possible to say that the higher the conversion rate over the past 60 days, the higher the model prediction will be.

The same thing happens with other features — for example, other things being equal, customers prefer products with a large number of reviews, and therefore the more reviews, the higher the prediction of the model will be. At the same time, different features have different predictive power and a different effect on the purchase probability.

The model training is based on using the dependency data between product features and final purchases via a special algorithm. Since many of the features are based on customer behavior, we can say that Ozon customers influence the products’ position in search results by their actions. Primarily, the results are influenced by the product’s purchase, as the impact of feature weight on purchase prediction is based on the past sales of the product.

What is taken into account in ranking

This stage takes into account all the features of the product that affect its sale. These include the product characteristics, its rating, customer behavior, such as placing an order, viewing the product or adding it to the cart. In total, the Ozon search engine takes into account about 100 different features.

Sellers can influence the product position in the search results as they directly control the values of some features.

The main conceptual blocks of the features taken into account:

• Text relevance. This is a property that describes the product’s textual relevance to the query. In the current search model, text relevance has a weight of 20–40%.
• Product sales. This block combines characteristics derived from the past product sales: the product conversion into purchases, and the number of purchases of that product over the last 60 days. Sales weight is 15–25%.
• Popularity by query. This block combines characteristics that describe customer interactions with a product: product impressions, adding it to cart, and to favorites. A product impression is defined as opening the product description card, and viewing the product in the search results. Its weight is 10–15%.
• Personalization. This block links search results to customers, according to the specifics of their orders, priorities, interests, and other parameters. Its weight is 10-15%.
• Price. For example, the product price. Its weight is 5–15%.
• Reviews. These are characteristics derived from the product rating and number of reviews. Its weight is 5–10%.

The predictive power of features is fluctuating since both the formula gets updated regularly and new features get added.

At this stage, the search engine evaluates each product from 2,000 candidates on a scale from 0 to 1. The closer the score is to one, the higher position the product will take in the search results and the higher the probability of its sale. All scores are passed on to the fourth stage.

4th stage: applying boosters

At this stage, increasing coefficients are applied to candidate products that are ranked from 0 to 1. They are called boosters.

How boosting works

For example, let’s take a look at the “red socks” query. Let’s say that after passing through three stages of search, a product named “red socks” gets a score of 0.6. If this is a product with a favorable price index, a booster is applied to it. For example, × 1.075. In this case, the final product rating is 0.6 × 1.075 = 0.645.

Booster types

The following boosters are currently enabled:

• Delivery speed boosters depend on how fast the product is delivered to the customer:
  • in 1-2 days: the product raises in search results;
  • in 3 days or more: the product drops in search results.
• Big Ozon promotions. For example, “Marathon of discounts”, “11.11 Sale”, and “Black Friday Sale”. The coefficient depends on the sale.
• Boost of products with favorable or moderate price index. The coefficient depends on the price and subscription.
  • 5% for moderate index,
  • 7.5% for favorable index,
  • 10% for favorable index with Premium subscription,
  • 12.5% for favorable index with Premium Plus subscription.

As a result, we can evaluate the organic relevance of the product.

5th stage: paid promotion

In addition to organic relevance of the product, its position in search results is also affected by paid promotion. Product placement is determined by its final score. It’s calculated by the formula: organic weight × organic score + promotion weight × promotion score.

Variables in the formula:

• Organic score: an evaluation of the organic relevance of the product calculated at the previous stage.
• Promotion score: an evaluation of the impressions cost in all paid promotion types. The higher the rate per click or order, the higher this metric is. If the product doesn’t participate in a paid promotion, the promotion score is zero.
• Organic weight and promotion weight: the regulatory coefficients that support competition and maintain the search results quality.

Promotion score depends not only on the rate, but also on the product conversion rate. It’s calculated by the formula: product click probability × rate per click + product order probability × rate per order.

This ensures the relevance of the products in search results. It’s determined both by the size of promotion rate and the probability of customers interacting with products.

How to raise the product in the search results

1. Promote it via pay-per-order. You can affect your products position on the search page or in the catalog by setting rates for them for the necessary search queries. To set a rate, navigate to the Promotion → Pay-per-order section of your seller profile.

2. Collect customer reviews. The higher the product rating and the more reviews it has, the higher the product appears in the search results. You can offer customers points for submitting reviews of your product. To enable this service, go to the Products → Reviews for points section of your personal account.

3. Fill in the product attributes completely. The more information about the product you add, the easier it is for your customers to find it via search. To edit product details, go to the Products section and choose the Edit product option on the necessary product. You can also edit information for several products at once using an XLS template.

   Learn more about editing PDPs

4. Improve the delivery time of your products. The average delivery time depends on the shipping cluster: the closer the product is to the customer, the faster the delivery. This makes the product more attractive to customers and improves its promotion in search results.

Frequently Asked Questions

I created a product and specified its quantity in stock. I can see this product on the Ozon website when I go to its page using a direct link, but it doesn’t appear in the search results. What should I do?

New products don’t get into the search results immediately—as a rule, it takes several minutes. Besides, the product may not be visible in the search results for 20 minutes after the changes are made due to the peculiarities of search results caching. If the product is still not shown in the search results after this time has elapsed, please check whether all the key product details are filled in: namely its price, name, and description. If the product doesn’t appear in the search results within 48 hours, contact the support service.

My product doesn’t appear in the search results for the queries where I expect it to appear.

If the product isn’t visible by the specific query, please make sure that:

• The product really doesn’t show up in the search results: the search results can contain up to several thousand products, and your product can appear anywhere on this list. To make certain that the product isn’t in the search results, use the corresponding filters, for example, by brand or price. This will reduce the selection.
• Whether the product will be located if you remove the category prediction. If the category prediction is triggered, the Search everywhere button appears next to the title under the search bar. In this case, please check whether the product belongs to the predicted category and whether it should belong to it.
• The product isn’t merged into a single product tile with other products in the search results. Check if there are different attribute options on the product tile, such as color, size, or number of pieces.
• The product isn’t found when searching for synonyms of the search phrase.

If you have performed all the actions above, and the product still doesn’t appear in the search result, please contact Ozon support team.

Is it possible to artificially increase the indicators to raise my product in the search results?

Many of the above indicators can really be increased artificially. It can be done either by fair means, for example, by purchasing high-quality external traffic, or by unfair ones, for example, by purchasing bots.

Using bots won’t help you raise the product in the search results as the ranking is determined by customer behavior. For example, you can increase impressions via bots. However, if the product has a lot of impressions, but the number of its sales is low, this means that customers don’t like this product much. It can negatively affect the product ranking.