Building recommendation engines that boost revenue and improve customer experience is no more a static reality. Building applications that are capable of delivering a wide range of user personalization experiences is the door to achieving faster and anticipated customer outcomes. SoulPgae recommendation application development services help businesses to achieve the same, with no ML expertise required.
Increased user satisfaction
Build brand identity
Boost upsell & cross-sell
Relevant product rankings
Forecasting business dynamics
At SoulPage, we build custom recommendation engines to suit your business needs with WHAT, WHEN, WHERE, and WHOM to recommend your products and services to achieve enhanced outcomes. Our dedicated team of experts helps you to develop, integrate, and manage AI-driven recommendation engines that boost revenue and customer experience by offering
The recommendation systems are built to recommend products/services based on your users’ interests and shares to easily implement a personalized recommendation in days, not months.
The system will process various attributes of data to define the best suitable products/services to personalize every touchpoint along the customer journey.
The system is built to categorize users based on a set of demographic classes that suits individual user interests to deliver higher quality recommendations in real-time to your users.
We deliver custom solutions by matching any of the above two models that best suit your business needs to cross/upsell products/services by ensuring data privacy and security.
We streamline the data collected from various sources like cloud servers, apps, streaming applications, sensors, etc, to understand how, where, and what can be displayed, analyzed, and stored in data lakes.
During data preparation and transformation we preserve, curate, clean and transform the data form if required to discover the hidden insights and for analytical querying.
We engineer the entire data pipeline with data modeling and analysis process to identify trends and patterns that help you better understand a problem or deliver a solution.
Integrate transparency into your data processing units to generate real-time interactive reports and intuitive dashboards to make informed decisions and create an understanding of how data is being used.
Engaging your users with personalized suggestions by investing in smart recommendations can help your business in achieving the perfect predicted growth in the short term. However, still, there are businesses owners, management, or employees across various industries unaware of how to leverage the best of recommendation applications for their business. Here below we shared a few industry-specific recommendations engine uses for major industries. For more assistance, SoulPage can potentially help to scale your business outcomes.
Complete Suite of Services
From strategy consulting to
development, our wide range of services
we will help you expand your data
We understand recommendation technologies and we know how to leverage
them to build customized and innovative
products & solutions.
Complete Regulatory Compliance
We ensure proper and complete
compliance with the country, state, and
A product recommendation engine is a powerful data filtering tool that uses machine learning algorithms and data analysis techniques to understand the customer journey, product sales, market trends, and other relevant content that affects user decisions to recommend the most relevant product/item to a particular user in real-time
Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. For instance, the Recommender systems built with the functionality of collaborative and content-based filtering tools can generate and provide suggestions to the users by exploiting various strategies to benefit businesses in retaining both short-term and long-term users.
Recommendation systems, or recommender systems, are systems that make suggestions related to search history, customer profiles, and inventory metadata. In short, while search engines help users find what they want, recommendation systems help users find more of what they like or relevant alternatives to improve the overall user experience.
A well-built recommender system can lead to an excellent customer experience and can potentially reduce the customer churn ratio, which is why understanding how the recommendation systems work for a specified business objective is highly important from a data science perspective. Recommender systems were simple in their early days, and have since evolved into more complex models with rising demand and increasing volumes of data to build hybrid solutions.