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
Collaborative Filtering
Relevant Product Rankings
Forecasting Business Dynamics
New-age Solutions Personalized to Your Needs
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
Collaborative Filtering
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.
Content-Based Filtering
The system will process various attributes of data to define the best suitable products/services to personalize every touchpoint along the customer journey.
Demographic-Based Filtering
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.
Hybrid Recommendation Systems
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.
Streamlined Recommendation Channel Building Process
Acquisition and understanding
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.
Preparation and transformation
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.
Modeling and analysis
We engineer the entire data pipeline with data modeling and analysis processes to identify trends and patterns that help you better understand a problem or deliver a solution.
Visualization and reporting
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.
Fully realize your business benefits with Recommendation Engine Services
Exclusive free consultation designed to channel and address your recommendation solution needs.
How SoulPage help to experience the best of recommendation applications?
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 business owners, management, or employees across various industries unaware of how to leverage the best recommendation applications for their businesses. Here below we shared a few industry-specific recommendation engines used for major industries. For more assistance, SoulPage can potentially help to scale your business outcomes.
eCommerce
- Personalized merchandising
- Product and content recommendation for voice shoppers
- Better product search experience
Entertainment & social media
- Increase user consumption
- Promote personalized content
- Video or audio recommendations
Retail
- Consumer behavior analysis
- Automated marketing campaigns
- Upsell or cross-sell products
Banking & finance
- Personalized financial suggestions
- Upsell or cross-sell financial services
- Budgeting
How recommendations can scale your market potential?
Leverage SoulPage’s personalized recommendation solutions to develop and integrate a wide range of applications that can improve your sales efficiency without compromising customer service.
- Our recommendation systems will allow your business to collect customer data and auto-analyze it in real-time to generate accurate analytic reports about your customers to make effective decisions.
- With SoulPage, an AI-powered recommendation system a business can plan facts-based strategies to improve your customer’s loyalty, the share of mind, and reduce churn with minimum efforts and lower costs than practicing traditional marketing techniques.
Why Choose SoulPage As Your Recommendation Solutions Partner?
Complete Suite of Services
From strategy consulting to
development, our wide range of services
we will help you expand your data
capabilities.
Creative Engineering
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
industry-specific regulations.
We got your questions covered!
What is hybrid recommendation system?
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.
How is a recommendation engine different from a search engine?
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.
What is the future of the recommendation system?
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.
Perspectives, insights & updates on AI
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