Recommendations - Why do they work? New possibilities for stores in 2021

Were it not for the recommendations, most stores would continue to build their marketing activities based on false assumptions that result from our perceptions about the needs of other people, their preferences, and not actual knowledge about them. These assumptions by marketers were proven in a 2015 study by Millward Brown (Millward Brown Poland dla FDN, 2015). The research was aimed at comparing who the offers of stores operating in a specific industry are directed to with the actual group of recipients of their stores. It turned out that 56% of users looking for sports articles on mobile phones are women, and 40% of users who buy products for children have no children at home. Our ideas about how recipients buy, what they need and who they are based on are not on objective knowledge but our ideas, which is why it is so important to be supported in this field with the latest scientific achievements, which are undoubtedly recommendation models. In this case, numbers are a marketer's best friend, helping them to respond to the real needs of customers, not just subjective opinions about them.

Start monitoring customer behavior

Let's consider why recommendations work and why they’re becoming a force in ecommerce. They are based on a few psychological tricks derived from the psychology of behavior. I am talking about the rules:

• The principle of shortening the distance
• The principle of being part of a group
• The principle of proof of equity
• The principle of choice from many options

The first one is the principle of shortening the distance between the company and the client by using recommendations as a form of advice. The better we know the company, the recommendations will have a stronger impact on us, as it happens in the physical world - attention from a stranger is less important than attention from someone who is important to us. The second aspect is the fact that a man as a social being has the need to be in a group and compare himself to it, therefore such slogans as "most often bought", "also bought" very strongly affect our subconscious, as well as build loyalty with the company. We want to be part of a larger whole. The proof of righteousness says that the opinions of others provide us with information about the quality of the product, hence the popularity of the bestsellers, most sold, sales hits, etc. Seeing that something is selling well, has good reviews and ratings, etc. makes us start to wonder if the product will be good for us too. Maybe we have missed something so far that others have noticed faster. 

The BrightLocal report shows that as many as 85% of customers believe that recommendations on a website can be trusted as much as personal ones. The last rule applies to the use of many options in recommendations, differing in price, appearance and sometimes use. The purpose of this distinction is to determine the middle value that will be ideal for the customer. For example, if the customer considers the price as a benchmark, having three evening dress options, we consider the cheapest one as not elegant enough, and the most expensive one as too extravagant, explaining that the middle option would be the perfect one. As you can see, the psychological aspects of the impact of recommendations on the purchasing behavior of recipients do not reflect the behavior of each customer, but their individual groups.

Why does it work? It’s simple and effective

No less important than psychological influences is another reason for the popularity of recommendations, which is simply its effectiveness. Along with obtaining more information about the customer and transferring recommendations to new communication channels, today we are able to recommend products not only for the benefit of the store that will sell more, but also for the benefit of the customer who will buy exactly what he or she needs. However, what is needed to maximize the strength of recommendations is knowledge about the customer - data on consumer behavior, shopping history, preferences, the shopping path or the most frequently searched products and keywords. The effectiveness is also based on the fact that we will create different types of recommendations based on customer and product data, and they will not always be based on the same pattern of action. We can easily divide them into those operating on the basis of customer data and data about other customers.

• Customer data - In this model, we verify what our recipients like. In this case - Bob likes what he is interested in, what he buys and based on this data we build "similar" categories, in which there are products associated with what Bob has already been looking for or bought. This method requires a certain amount of customer data about their tastes and preferences to find the most relevant suggestions for buyers.

• Data about other customer - This is a knowledge base that allows us to place the client in a wider context. We can understand what similar customers need, what are the current purchasing trends, how customer paths are changing in order to improve the website's ux. To this end, we start again by identifying what our recipient Bob likes, and then we move on to identifying other users with similar interests or purchase histories, and to recommending the most popular books among similar users.

Summarizing the effectiveness and continued popularity of recommendations, we can say that they are a correlation of several factors. The recipients are influenced by the psychological aspect of using the forms of "purchase suggestions", as well as getting to know customers better through the collected data about them and the use of various forms of recommendation. What indirectly affects the effectiveness of recommendations is the infrastructure, the recommendation engine that is used. It is best to use tools such as the AI Recommendation mode, because it opens up many more possibilities for the store, such as building recommendations for many products, many stores and with the possibility of creating recommendation models on your own.

You can do it yourself with tailor-made recommendations

The product recommendation mechanism for displaying best-selling products is used by almost all online retailers as a strategic selling method. Since the method is well known and there are so many solutions that enable the implementation of recommendations on the website, can they be improved? Definintely yes—otherwise the largest technology companies would not invest huge amounts of money in their development. One of the biggest innovations and trends in 2021 will be the ability to use a stand-alone recommendation wizard instead of ready-made forms. Just as each user is different, each store has its own specificity, site navigation, different product selection and needs the ability to independently choose the most important recommendation criteria.

The best recommendation management systems allow you to independently create and select the type of recommendation, select criteria or define the product base on which the engine will learn to create the most effective recommendation models. In addition to the ability to create recommendations on your own, we also have editions of existing models to suit current market needs. As a result, each consumer will receive a unique list of recommendations that highly correspond to his preferences and tastes.

No matter how many stores and products you have, it will fit you

The appeal of effective recommendations makes them an attractive solution for operations of all sizes. To meet the market needs, the new AI recommendation module created by Synerise allows you to manage multiple stores within one customer profile, which is extremely helpful from two points of view. First, it is not only how we collect data for recommendation and how we present it, but also the technological facilities. Infrastructure that allows us to make recommendations based on any number of product catalogs, different data formats open up many possibilities.

Imagine a company that sells shoes and runs an online shop in several languages. We want to create recommendations for each language version, taking into account cultural differences (different holidays, children's days, other promotions, etc.). In the traditional model, marketing activities should be carried out for each language version on separate customer profiles. However, based on the AI Recommendation module, you can run multiple stores under one account and create recommendations for each language version separately, while implementing different pricing policies.

On the screen from the Eobuwie store, we can see how the offer looks for two language versions with the same search guidelines: Geox shoes, children, winter shoes. We received a slightly different personalization at a price adjusted to the currency of a given country and the price level. As you can see, you can effectively create separate recommendations for each language version of the store, while operating within one customer profile.

Many companies also struggle with the problem of offering various types of products, often in parallel with a stationary store, an online store, workshops, training, film, etc. are run. It is difficult to include such different products within one type of recommendation. There is a high risk that the model created in this way will not be equally effective for each of the products. Here we must consider the possibility of creating many recommendations separately for each product within one customer profile, i.e. one for the online store, another for films and workshops. It is enough to divide the product feed (catalog with products) into separate files. Let's follow an example. 

A sporting goods store has a location in the center of a large provincial city and an online store, additionally, as part of its activities. A year ago it created online training on choosing sports equipment and creates its own brand of running accessories. In the traditional recommendation model, all products offered by the store would be included in one recommendation model or would require the creation of separate customer profiles for each of the products. A modern recommendation model allows you to create separate recommendations for a stationary store, others for an online store, and still others for a newly created brand and, of course, for online training. In order to use this option, the customer must create separate product catalogs for each type of recommendation, but all data can be collected within one customer profile.

Recommendations based on product catalogs can take any value. They don’t have to be a professionally created product feed, they can also be an xml file or any database. An example may be recommendations for Spotify, Player or Buzzsprout, where we have the same price, descriptions, title, popularity data and we can use them to create a recommendation module. Going further, the customer can manage multiple pricing policies separately for each store or product he proposes. The use of multiple pricing policies allows stores to enter new ones, and the ability to create different marketing strategies and recommendations for each product or store separately allows you to develop and scale your business.

Summary

A product recommendation engine is a tool that, based on the purchasing pattern, allows you to finalize the best purchasing choices, based on the psychology of consumer behavior and combine them with artificial intelligence algorithms that are designed to analyze data about customers and their purchases. A properly prepared recommendation module helps to generate sales and maintain the user's interest in the offer where interest was most often lost. If we add to this new possibilities, which is the independent creation of recommendations, and the implementation of any number of product catalogs into the system, the possibilities of the recommendation module become even greater, allowing for business development, especially for those customers who plan to enter new markets. Recommendations understood in this way are today the most popular but also the most effective form of using artificial intelligence in business.