Based on its recent analysis of the Indian retail analytics industry, Frost & Sullivan recognized Manthan, a leading Artificial Intelligence (AI) and cloud analytics software player, with the Indian Retail Analytics Industry Technology Innovation Leadership Award at its 2019 India Best Practices Awards Banquet in Mumbai.
The award acknowledges the company's significant achievements in enabling retail business users to focus on driving the best outcomes, respond quickly to the dynamics of the market, and gain better control over decisions and actions using AI.
With their solutions used by more than 200 consumer-facing businesses in over 22 countries, Manthan has the largest direct impact on the retail value chain today.
Frost & Sullivan's Best Practices Awards Program identifies outstanding industry achievements by companies globally. Frost & Sullivan industry experts conduct a unique; deep-dive research and analysis combined with a structured metrics-based evaluation process to pick the winners.
"With the rise of AI-based tools and technologies, the global analytics market is witnessing a boom. Manthan, with its highly advanced AI-based analytics platform, is gradually revolutionizing the retail analytics market by simplifying and accelerating the process of analytical decision-making from merchandisers to business executives. The platform leverages the power of Natural Language Processing (NLP) to provide a rich user experience and a dynamic interface to interact. Manthan through its descriptive, predictive, and prescriptive analytics capability is capable of meeting the evolving personalization needs of customers", said Anand S, Vice President of Manthan.
The key successes that differentiate Manthan from others as identified by Frost & Sullivan analysts are:
* A slew of cutting-edge technology capabilities within Manthan's solutions that include auto-generation of insights, anomaly identifications, business recommendations, and natural language voice and text interfaces to aid business users transform the way they work.
* AI-powered analytics solutions in addressing the industry's scattered data problem and providing meaningful and timely insights.
* AI-powered algorithms with the capability to go to granular attributes such as fabric style, colour, pattern, and fit level among others to forecast demand for the merchandise.
* Unique ability to integrate data management, data discovery, advanced analytics, reporting, and visualization into single unified architecture sets it apart from other competitive offerings in the Indian market.
* The multi-dimension customer approach through its flexible AI-based prescriptive analytics and closed-loop execution framework.
* Ability to provide users with the most relevant and accurate merchandise insights in real-time by evaluating external variables such as events, weather, trend, and demographic data to aid its forecast accuracy.
"We truly appreciate Frost & Sullivan's extensive research into the Indian Retail Analytics Technology Industry and we thank them for identifying Manthan as the industry leader in technology and innovation," said Seema Agarwal, Head- Merchandise Analytics at Manthan.
"It has always been Manthan's philosophy to invest in the development of new technologies and the best talent in the industry to continually introduce innovative solutions to the ever-changing and dynamic retail market", added Agarwal.
Frost & Sullivan Awards are presented to companies that demonstrate best practices across industries. The Awards are recognition of their innovation, commitment, technological innovation, customer service, and successful business strategies required to advance in the global marketplace. Frost & Sullivan's intent is to help drive innovation, excellence, and a positive change in the global economy by recognizing best-in-class products, companies, and individuals.
This story is provided by BusinessWire India. ANI will not be responsible in any way for the content of this article.
Disclaimer: No Business Standard Journalist was involved in creation of this content
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