Big Data Behind Recommender Systems
Whether you are responsible for customer experience, online strategy, mobile strategy, marketing, or any other customer-impacting part of an organization, you’re already aware of some of the ways recommendation technology is used to personalize content and offers.
Recommender systems are one of the most common and easily understandable applications of big data. The most known application is probably Amazon’s recommendation engine, which provides users with a personalized webpage when they visit Amazon.com.
However, e-commerce companies are not the only ones that use recommendation engines to persuade customers to buy additional products. Recommender systems can also be used in other industries as well as have different application, from recommending music and events to products and dating profiles.
Let’s have a look what recommender systems are and how you can apply them in your organization.
Science behind recommendations
There are two major ways most of recommendation engines work. They can either rely on the properties of the items that each user likes, discovering what else the user may like (content-based filtering method);
or, recommendation engines can rely on likes and desires of other users in order to compute a similarity index between users and recommend items to them accordingly (collaborative filtering method).
It is also possible to combine both engines in order to build a more prosperous recommendation engine.
Types of data used by recommendation engines:
User behavior data
- log on-site activity (clicks, searches, page and item views);
- off-site activities (tracking clicks in emails, in mobile applications and in their push notifications).
Particular item details
- device used
- current location
- referral URL.
All the three data sources are equally important for a consistent operation of both types of algorithms. In order to get full picture of your customer you should be aware of not only what he or she is viewing on your website and your competitor’s one, but also how often, from which location and on which devices. Having this information brings you closer to 29% increase in sales, which Amazon experienced firsthand after recommendation engines were implemented on their website.
Why integrate recommender systems:
Recommender systems have proved themselves efficient in the following areas:
- increase the number of items sold;
- sell more diverse items;
- increase the user satisfaction;
- better understand what the user wants.
Examples of recommendation engines put to work:
Personalized product recommendations
Such engines help understand the preferences and intent of each visitor and show the most relevant recommendation type and products in real time. Recommendations improve as the engine learns more about each visitor.
Allow to Increase sales and conversions by segmenting and targeting visitors with real-time personalized messages and offers.
Such engines help brands build trust with their customers and create a sense of presence and urgency while showing real-time notifications of shoppers’ activities on the website.
Personalized loyalty programs and offers
Number of studies show that people are more interested in personalized offers than cookie-cutter solutions, which is especially true for loyalty programs. Such engines are able to customize recommendations based on real-time interactions with each customer. Data analytics algorithms are focused on different product categories with different purchase behavior and the integration of contextual information, which improves recommendation quality.
Recommendation engines are at the front and center of predictive marketing. The key point is that they can be utilized in almost every industry to optimize and improve customer experience.
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