CRS Online

Conscious Recommendation System (CRS) is an intellectual property in the data science sphere aimed to optimize and improve the internet-trading profitability.

The system can be used in the existing online stores as well as in the newly created ones.

With the help of data mining, the CRS provides the end user with precisely targeted and conscious recommendations for those goods that seem really appealing to the customer during the buying process. A conscious recommendation represents the final result of data analysis system about consumer decision-making process during a long-term period.

Areas of application
Functional characteristics
General operation principles – CRS
System use in business
What kind of information does the CRS need?
How to connect your store to CRS?

Areas of application

The recommendation system is used in the retail sphere where there are stable combinations of the sold goods (in this case the system is capable of producing the conscious recommendations after learning).

The system was originally developed for the pharmaceutical retail sphere since the stable combinations of medicines and medical supplies are widely used for the treatment of various diseases there.

The system can be also used in the following spheres:

  • sports equipment
  • furniture
  • tools
  • building materials
  • clothing
  • food
  • household chemicals
  • etc.



Functional characteristics

The main difference between the CRS and the traditional recommendation systems is that the CRS makes recommendations for creating a complete list of the in-demand set of goods at a certain moment. The recommendations of type “the best-seller”, “people also buy” or “you used to buy this” are taken into account with different weighting factors while showing the results to the clients.

For example, if a customer has chosen some alpine skis, the system will select and recommend the most suitable goods related to this particular product – a pair of ski boots, some gloves, a pair of ski poles etc. And, as well as in a case with medicines (recipes), the recommendations are based not on the frequency of goods appearance but relying on a research of all consumer demands for similar sets of goods. Such a behavior of the system is grounded on the usage of NLP (Natural Language Processing) principles in regards to the demand research.


The general recommendations are also taken into account by the system, and, basing on the wishes of the commercial network management are added to “conscious recommendation” increasing the marginality and specific goods promotion.

Within the system, we have also found the solution for so-called “cold start” (is used in case there is not enough information to offer for a certain choice). The majority of recommendation systems has some problems when providing the results to the new customers or when specifying the related goods for new items. We have developed the new methodologies for covering these needs.

Providing the commercial network management with the list of the products that could have been bought by the buyers but were out of stock (“drug shortage” in medicine) is another important and fundamental difference between our recommendation system and the analogs.

Implementation of the above-mentioned functionality is possible because the system predicts the items that can be added to the cart and checks their availability. The shortage list gets constantly updated before the user gets any recommendation on the website when checking the availability of the product in stock and getting it replaced if needed.

General operation principles – CRS

CRS accommodates the client’s needs and predicts the largest number of goods that can be purchased along with the first product that was selected. Once new items are added to the cart, the system generates the recommendations based on the entire list of the goods,  not on the first or the last ones only.

Fig.1. The main functional blocks of CRS

At the same time, the system tries to find such goods that belong to the largest possible subgroup from the cart. For example, if a pair of skis, ski bindings, a pair of gloves and a soccer ball are in the cart the system will detect that the first three goods are the main ones and will recommend buying a pair of boots, ski poles, and other possible options. At the same time, the recommendation “ball pump” will also get generated but will not probably be shown to the client (since the purchase would be categorized as skiing group). If the users will not be interested in one of the suggested items from skiing group and will skip it (e.g. nobody wants to buy the recommended ski boots), the system will change it to the “ball pump” in the recommendation. The results will be monitored and if the sales of pumps get increased, the system will take it into account and reorganize the recommendations to optimize the trading process.

The main functional system block that predicts the shopping cart, in general, helps to avoid one of the most important risks – so-called “cold start” (the situation when a merchant knows nothing about his client after the first purchase and has nothing to offer except “related to this item” goods or an equivalent of the recently chosen item). In this case, the CRS provides the list of conscious recommendations in which the first selected product remains the main one. After every other item is added to the cart, the system recommendations get even more “conscious” and appropriate.

Every functional block considers different trade chain needs while organizing the workflow:

  • Specific products promotion;
  • Replacement of goods by more marginal ones;
  • Special offers;
  • A list of some additional recommended items;


If the client is registered in the system and has ta certain shopping history, the system will consider the following factors:

  • The user preference to the price categories of the products;
  • The user preference to the trademark and the quality;
  • Routinely purchased goods, if they do not belong to the type of long used goods;
  • Some accompanying items to the recently purchased goods of long use.

System use in business

Fig.2. The main functional blocks and their interaction in e-commerce

What kind of information does the CRS need?

CRS uses the information about the ongoing sales that allows the system to adjust the data and follow the market’s constantly changing situation. The system tracks and analyzes the client’s behavior on the website and correlates it to their further consumer activity. It is necessary for ensuring the efficiency and the appropriate choice of the triggered notifications.

Besides, the CRS requires the information about the availability of the item that is being recommended. It helps to avoid shortage (to keep the list of the items that should always be in stock) and plan the goods procurement.

How to connect your store to CRS?

  1. Contact us at and sign an initial data analysis agreement.
  2.  Provide us with non-personalized sales data of your company for the past year. These are the order details (name of the goods, price, user id, sales point number);
  3. We will review the data and identify meaningful patterns. After that, we will be able to formalize the concept of the consumer cart (the combined groups of goods) for your business type and present the results of the research to you;
  4. After that, our team along with your technicians will develop the scheme of providing the data to the CRS on a regular basis;
  5. The system analyzes your data and creates communications between the goods;
  6. The system starts working with your online-store full-time and provides conscious recommendations to the requests from your clients (using API commands);
  7. We consult your employees answering the support requests;
  8. We also discuss and agree on the ways of user activity logging;
  9. The system improves the quality of conscious recommendations with the accumulation of real data.