The development of E-commerce has created more competition in the online environment. E-sellers looking all the time for something that will outweigh the scales in their direction. Way to gain this advantage is personalized experience for the customer. To achieve them need to first know the individual users and their habits. However, with the growing number of elements, the growing number of users and the changing environment, you can not manually prepare a personalized experience.
The bigest companies have already understood the value of personalized recommendations some time ago and hired a large group of data specialists to develop and maintain their recommending engines. Some companies such as Amazon and Google are focused on their data so intensely that they can be treated as a company engaged in the science data.
The recommendation system can be used to personalize the content of pages for each user individually. Other channels, such as e-mail newsletters or mobile notifications, can also be personalized. User interactions from multiple channels power the recommendation system, increase the accuracy of recommendations and increase the personalized user service.
Data is crucial for good recommendations. You must start with user profiles and attributes of your products. You can start with anonymous users of your site and remember their interactions. Once you recognize their identity, you should merge profiles on all of your channels. The most valuable data for recommendations is the history of users and their interactions with objects. For new products, at least some attributes (e.g., text description) should be provided to improve the recommendation.
Before a company decides to make a move to build and maintain an internal recommendation engine, it should consider the costs and benefits of such a decision. Storing and maintaining such data is not cheap, but it can be used in many other ways.
What costs have to be incurred? When you decide to develop internal recommendation system, you need a team of experts dealing with the data to create prototypes to developers who are preparing a production code and engineers preparing and preserving infrastructure. The development phase usually lasts 2-3 years and costs considerable money, depending on the cost of personnel and resources HW. To this must be added the cost of living (infrastructure, equipment costs) for medium-sized companies, without taking into account specialists, who are not cheap, but the company needs them to maintain the quality of algorithms and improve the system.
However, you can save most of these expenses using an external system recommending.
Let’s start with the most general benefits. Thanks to the recommendation system, you gain a comprehensive insight into the customer base and products. User profiles maintained in the recommendation program are based on previous interactions with objects and enable advanced analysis of business reports and dashboards generated on a regular basis. Such reports can anticipate possible problems, so you can avoid them. Your business decisions impacted by analysis can save you money.
Generated recommendations usually shorten the time needed to find an item and significantly increase the probability of detecting other interesting things. The result is increased user loyalty and satisfaction through network services. Typically, users interact with more elements, and this behavior leads to increased consumption and higher profits. In addition, newsletters, personalized content promotion and push notifications encourage users to return, increase the frequency of visits of regular users, reduce the number of cancellations and increase their value.
Many companies regularly define and evaluate their key performance indicators (KPIs), simplifying accurate measurement of the impact of the recommender. This measurement is then carried out using the AB test in which personalized recommendations are provided to users in group A, while group B receives standard recommendations or best-selling content.
The domain in which the benefits can be easily evaluated is e-commerce. Measuring income generated on the basis of a personalized recommendation can be accomplished with a token or simply by calculating the price of purchased products that have been recommended less than a few minutes ago. However, this number does not reflect the fact that the customer could buy another product in a different way, or can buy without even personalized recommendations.
The costs of the recommendation system can be significantly reduced by ordering a recommendation service. What’s more, the quality of internal recommendations depends on whether the team has sufficient capacity or knowledge to develop algorithms for recommending a state of scale.