Claro is the leading telecom operator in Latin America. In an intensely competitive market, to have a leading edge, Claro wanted a solution that could understand user's transactions, consumption patterns and behaviors in order to have "individualized offerings". Mooga made Claro's WAP interface more intelligent and dynamically adaptive thereby enabling Personalization, Recommendation and Intelligent Campaign Management on-the-fly in real time. Substantial revenue increase ensued.
One of the biggest challenges that WAP portals face today is to show the right content to the right user at the right time on the limited mobile screen size. According to an empirical research, it has been found that it takes almost 10-12 clicks and an average of 50-60 seconds for a user to get the content of his desire. Mooga's complete WAP portal management suite comprising Search, Personalization & Recommendation enabled Sony Music address this challenge and monetize its huge content repository more efficiently.
With over 900 TV channels and over 9000 daily programs, it is virtually impossible for a viewer to find out programs of his/her interest. Channels find it even more difficult to promote "all" their programs to the "relevant" viewers. WOI wanted a way to promote the Best content at the Best time to the most appropriate user from the TV Guide so that all contents could be made "visible". Mooga enabled WOI with a means to learn what people are watching/fetching information on and give Personalized Recommendations to each individual thereby increasing user's stickiness.
Mooga has empowered Hungama's off-deck portal (both Web and WAP) with Search which goes a step farther than conventional search engines by incorporating phonetics at multiple levels. Personalization and Recommendation is to follow soon.
Compared to traditional BI, the new wave of BI 2.0 goes beyond data and reporting. BI 2.0 is real-time and works on operational databases obviating the need of data warehousing.
It is a powerful state-of-the-art recommendation, matching, discovery and personalization framework supporting many kinds of products, structured contents and generic transactions seamlessly and uniformly; based on social (collaborative) filtering, item (content and contextual) filtering, intelligent matching and on individual tastes. This framework works in real-time and is completely programmable, configurable, and customizable based on products, contents and required functionality.
Matching solution backed by AI techniques compares requirements (criteria) v/s products, services, or contents (like electronic gadgets, cars, resumes, customer profiles etc.) intelligently. It also matches products/content v/s products/contents to get products/contents etc. It helps to find out cluster having homogeneous (logically similar) products/contents/profiles based on input specification (or product/profile).
The traditional approaches are more reliant on data warehousing where as Mooga uses transactional databases.
The intelligent systems collectively have features like learning ability, adaptation to changes, explanation capability, and flexibility in dealing with imprecise and incomplete information, etc. The limitations and strengths of individual systems is the central driving force behind having hybrid intelligent systems. By integrating these individual systems, their strengths can be increased and weaknesses can be reduced.
Other business systems automate business processes and deal with data while intelligent systems automate expertise and deal with knowledge. Intelligent systems facilitate to extract, acquire, represent, preserve, use and apply knowledge, make it available not only internally but also externally. The major advantages: objectivity and consistency in decision making, preservation of valuable expertise, extra time to train new stakeholders, free experts from routine jobs, flexibility in managing business rules, etc.
It is one of the first complete Online, Web-based, Integrated and Hybrid Artificial Intelligence Development Frameworks to develop and deploy applications in the areas of Decision Support Systems, Knowledge-based systems, Machine learning systems etc. Mooga is an extension of iKen studio for N=1 personalized experiences.
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