predicting behaviour in near FUTURE
It predicts which options may be more interesting for users - which films will be picked more often, which books will be sold fastest and which shirts will be out of stock.
RECOMMENDATION SYSTEM IN anONLINE STORE TAKES INTO ACCOUNT:
- User preferences
- Interests
- Previous activity on the website - pages viewed, inquiries, selected products, etc.
- Location
- Education
- User actions - decisions, opinions, abandonment of purchases, etc.
B2B RECOMMENDATION SYSTEM ASSESSING POSSIBILITIES OF COOPERATION:
- Industry
- Form of business activity
- Number of employees
- Business profits
- The effectiveness of the company's operations on the market
- Awards
RECOMMENDATION ENGINE - TIPS OR MORE?
The recommendation system, also known as recommendation engine, prompts users what movie is worth watching, what product to add to the shopping basket or what article to read on a specific topic.
INFORMATION IS COLLECTED BASED ON COOKIES PRESENT ON THE PAGE VIEWED
Each user gets a unique, individual ID number. The recommendation engine links previous or later visits and user choices with the current visit.
INSIGHT INTO USER'S ACTIVITIES ON THE WEBSITE
Information about the user's activities on the website provides information about his or her behaviour. The company learns what products were viewed, how many pages were viewed and how long the session lasted.
Data visualisation aggregates user behaviour for analysis
The data can take the form of a graph, dashboard or Excel report. The company can monitor user behaviour and emerging purchasing trends on an ongoing basis.
Different APPROACHES TO RECOMMENDATION SYSTEMS:
Collaborative-filtering
Analyzing the choices of a specific user to offer the purchase of product/the choice of service to users with similar interests, preferences or purchasing habits. It occurs by classifying the user into a group of users with at least one common point.
- USER PREFERENCES
- FAST TO MAKE A BUYING DECISION
- HISTORY OF SITE NAVIGATION
DURING A SESSION
Content-based filtering
Suggesting which product may be of interest to the user due to the similarity of chosen parameters. This approach doesn't take into account the choices of other users, but the preferences of the individual user, especially the purchase history and the frequency of visits to the site.
- PRICE
- SPECIFIC FEATURES, E.G. PRODUCT COLOR, MATERIAL, APPEARANCE
- TECHNICAL VALUES
- PRODUCT CATEGORY
Complementary filtering
Analyzing the possibility for the user to purchase more than one product or order more than one service.Proposing the products/services that they have chosenby other users recently. It takes into account the popularity and frequency of choices made on the site.
- POPULARity
- FREQUENCY OF SELECTING
SPECIFIC PRODUCTS
DURING SHOPPING - RECENTLY VIEWED PRODUCTS
ADVANTAGES OF RECOMMENDATION SYSTEMS
for users
- Quickly find the necessary information
- Rejection of excess unnecessary information
- Suggest a similar product
- Intuitive site navigation
- Autocomplete contact details
FOR THE IMPLEMENTING COMPANY
- Classification of website/app users as returning and one-time users
- Generating high-quality leads
- No need to manually analyze the results
- Increasing the number of successful shares
You can introduce recommendation systems to the online store, auction platforms, to search for movies or music, as well as to social networking sites and online bookstores. There are many possibilities.
The basis for implementing recommendation systems is a large number of products or services that you sell or you will provide to users via the network.