Built to enhance customer experience and drive sales
Drive your customer interactions with the next best action
Recommend Me enables companies to easily and efficiently meet consumer desires and expectations. The result is a win-win for everyone involved: an uptick in sales as well as in customer satisfaction. That’s because Recommend me is a product that helps refine, understand and anticipate a your customers preferences and needs in a way that creates a more personalized experience.
Reasons why industries prefer Recommend Me over other systems
satisfaction by quicker responses
Complete track of
all your leads and opportunities
Enterprises rely on Recommend Me to enhance customer experience and increase sales
Recommend Me offers an effective form of targeted marketing by creating a personalized shopping experience for each customer. For large retailers our product is scalable over large customer bases and product catalogs, needs only few seconds to process and generate online recommendations, is able to react immediately to changes in a user’s data. It makes compelling recommendations for all users regardless of the number of purchases and ratings. Recommend Me analyzes over 10 billion customers’ data and over 100 billion recommendations every day.
Recommend Me uses algorithm based on data science that helps underwriters and brokers identify industry-specific client risks; then, recommend up-selling and cross-selling opportunities by offering access to parallel policies, products, marketing materials, and educational materials. As more products are consumed to an increasing customer base, Recommend Me becomes more mature and reliable, resulting in an exponential increase in revenues and customer satisfaction.
Every banking firm have sufficient knowledge about customer interests and behavior. Recommend Me uses memory-based collaborative filtering. It processes the data/information sourcing from various banking channels into the services that other customers have used most frequently and recent services that a customer has previously used.
Recommend Me generates recommendation based on user’s interests, social connections, blog entries, social activities and content analysis. It integrates with advanced machine learning algorithm which aggregates relationships among items, people and tags. It offers user-specific, tags-specific, popularity-based and combinations of these recommendations.
Recommend Me integrate news articles/information filtering method with the collaborative filtering method to enable personalized recommendations for easier news access. This combination has proven significant over the traditional collaborative filtering method. User satisfaction, rich experience, easier access to intended news article are some of the benefits gained.
Recommend Me integrate real-time performance monitoring, advanced data analytics, information dashboards. With real-time monitoring, you take right decisions at the right place and time. Our solution accelerators enable you to keep track of cost and quality performance. It helps healthcare organizations transform their businesses by integrating traditional report analysis with organization’s analytics-driven insights.
3 filtering techniques we use to optimize your businesses
Collaborative filtering technique is a domain-independent algorithm for unstructured data such as movies and music. It matches customers with relevant interest and favorites by determining similarities between their profiles to generate recommendations.
Content-based filtering technique is a domain-dependent algorithm and it concentrates more on the processing of the attributes of items in order to generate recommendations.
Hybrid filtering technique integrates various recommendation techniques in order to process different types of data to avoid some shortcomings and issues generating personalized recommendations.