Personalization is a process that creates a relevant, individualized interaction between two parties designed to enhance the experience of the recipient. This process does not involve any consumer input besides the use of their purchasing or profile data. A perfect example is Amazon’s personalized recommendations that display as pop-ups showing suggestions of purchases that the consumers might like to make based on their previous purchase history.
Personalization engines are a type of information filtering system that can be applied on virtually any kind of website and help users find new content relevant to their interests – from music and events to products and dating profiles. Personalization engines are a useful alternative to search algorithms since they can guide visitors to items or content that they didn’t even know they were looking for. Product recommendation also allows for natural, logical upsell, and cross-sell opportunities.
At a high level, machine learning takes large amounts of data and generates useful insights that help the organization.
However, most organizations can benefit from traditional data analytics without more complex machine learning applications. One of the most important differences is that the data models typical of traditional data analytics are often static and limited in addressing fast-changing and unstructured data. When it comes to clickstream or IoT, it’s often necessary to identify correlations between dozens of inputs and external factors that are rapidly producing millions of data points. While traditional data analysis would need a model built on past data and a data scientist’s opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g., CTR, eCPM/Revenue, cost/chat for the seller). It then automatically looks for predictor variables and their interactions.
In general, machine learning can create value when you know what you want, but you don’t know the important input variables to make that decision. You give the machine learning algorithm the goal(s), and then it “learns” which factors are important in achieving that goal from the data.
Besides, machine learning is also useful for accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms continuously improve over time as more data is captured and assimilated. Machine learning algorithms can make predictions, see what happens, compare against their predictions, then adjust to become more accurate.
A/B testing is a way to compare two versions of a single feature (e.g., variants of a webpage or an algorithm). Typically, the test is carried out by comparing the customers' response to variant A against variant B and determining which one of the two variants is more effective. Multivariate testing is an extension of A/B testing when more than two variants are tested.
Personalization engines run A/B and multivariate tests continuously to determine which variant is the most effective by using statistical models. The aim is to improve the recommendation to resonate more with the user and to achieve business goals.
A/B testing can also be used to compare two or more personalization engine vendors against each other to determine which one performs better. The vendor that performs better wins.
Yusp is scalable both in terms of traffic and the size of the product or content catalog. Yusp serves clients with hundreds of millions of items and clients with 25 billion page views per month with peak traffic of 250,000 transactions per second.
Yusp’s product portfolio enables personalizing seven different business models - including e-commerce, online marketplaces, publishers, streaming video services - and the physical shopping experience.
Yusp is a personalization engine that turns data into powerful user journeys that convert, powered by machine learning.
The Yusp recommendation engine, developed by the Gravity R&D team, provides a scalable personalization solution for enterprises coming from various industries such as Retail, Marketplaces and eCommerce, Telecommunication, News and Media, Classified advertising, dating sites, and more. The engine predicts individual behavior and preferences to provide personalized recommendations, create a better shopping experience, and help product and content discovery.
The recommendation engine includes all the personalization features as defined by Gartner, and it consists of a core engine and four, independent modules:
Core personalization engine
The core Yusp algorithms consider various data sources, including user data, product data, and contextual information, to get a 360-degree view of the personalization opportunities of our client's business.
The Yusp team can then implement four different modules for the clients based on their business needs, and personalize physical and online experiences.
Site personalization
Yusp's machine learning algorithms help our clients personalize the experience by adding dynamically changing content to their sites.
Recommendations
Our clients can leverage machine learning to make product and content discovery easier for their visitors by placing the right recommendations at every step of the user journey.
Advanced Search
Yusp clients can provide best-in-class search experiences to their customers by personalizing search results based on prior online behavior and preferences.
Marketing Channels
Yusp's centralized engine extends the customer journey from our client's site to its marketing channels by delivering personalized interactions to the users in real-time, such as emails, push notifications and more.
With the help of Yusp, brands can deliver relevant product and content recommendations to the customers throughout the complete buying journey. As a result, Yusp clients can boost revenue growth and increase customer satisfaction.
Gravity R&D is a recommendation engine provider, using machine learning to personalize digital customer experiences for SMEs and enterprises.
The Budapest-based company has been focusing on data science since 2009, using machine learning and Big Data analytics to create personalized customer experiences for brands in various industries, such as Retail, Marketplaces and eCommerce, Telecommunication, News and Media, Classified advertising, dating sites, and more. Currently, they have two products on the market: Yusp for enterprises and Yuspify for SMEs.
These products allow businesses to deliver relevant content to both known and new visitors across the entire user journey. Personalization allows the end-users to discover products and content easier and navigate better on digital platforms. Simultaneously, personalization products help Gravity's clients deliver better experiences, drive revenue growth, and improve customer satisfaction.
Thanks to the versatile algorithmic portfolio, global cloud partners, and the flexible technology stack, Gravity can cover a wide range of use cases and serve clients in various industries. The client portfolio includes 10+ industries and brands such as Disney+ Hotstar, Cora, n11.com, Magyar Telekom, Group Godó, and many more. Gravity's personalization solutions provide 35+ billion personalized recommendations per month.
We are happy to provide you a demo to showcase Yusp’s capabilities. Since Yusp provides specific personalization solutions tailored to our customers' needs, please contact us to organize a demo specific to your business.
Yusp has a monthly fee proportional to the additional value it generates. The price also depends on the personalization modules that your business uses and the traffic size that Yusp needs to serve.
In certain implementation scenarios, there's also a one-time fee that covers our integration efforts. As the pricing depends on a couple of factors, first, we would like to understand your exact use case before we can provide you with exact pricing.
Yusp generates recommendations similarly to how a human merchant recommends goods to his regular customer. Yusp knows which products and offers are the most relevant for the customers based on their taste, past purchases, current mood, new/fresh products, and their search context.
Besides, with the power of machine learning, Yusp can do it at scale, even for millions of customers at the same time. It goes beyond just considering the behavior of a single customer. It can also detect generic patterns in the behavior of a large number of customers.
For example, how to differentiate between a support and a change request?
The monthly service fee includes:
If a request requires modification of the integration or the recommendation logic and can't fit into the agreed monthly support, it's handled as a change request and charged on top of the standard monthly fee.
Yusp is compliant with General Data Protection Regulation. More info is available under this link.
