Why Are Retailers Using AutoML for Offering Personalized Products?

The automated machine learning (AutoML) market is expected to accelerate at a monumental CAGR of 43.7% during the forecast period (2020–2030), on account of the increasing importance of predictive lead scoring, spurring demand for efficient fraud detection solutions, and soaring need for personalized product recommendations. The market generated $269.6 million in 2019, and it is projected to touch $14,511.9 million by 2030. Moreover, the rising preference for cloud-based platforms, such as software-as-a-service (SaaS), will support the market growth in the coming years, as it allows users to access AutoML solutions Securely.

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With the rising penetration of e-commerce platforms, the demand for personalized products has increased significantly. Online shopping companies are offering personalized goods to meet the specific requirements of individual customers. Moreover, personalized product recommendations assist companies in escalating their average order value. AutoML solutions detect patterns in the consumer behavior by analyzing previous product searches, prior purchases, clickstream data, browsing history, and demographics. This allows companies to create a 1:1 customized product recommendation list that matches customers’ preferences and requirements.

Moreover, the surging importance of effective product assortment is also expected to accelerate the automated machine learning market in the coming years. Selecting the correct mix of products in a retail store is important for retailers to meet customers’ needs and retain their customer base. AutoML solutions are being deployed by retailers for having an attractive assortment of products, as these solutions consider the store location, weather pattern, customer segments, past sales records, and store display space to assess the best fit of products for a given location.

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In recent years, solution vendors, such as DataRobot Inc., Squark, Aible Inc., H2O.ai Inc., Big Squid Inc., Determined AI, dotData Inc., SAS Institute Inc., EdgeVerve Systems Limited, Microsoft Corporation, Amazon Web Services Inc., Google LLC, and Determined AI have launched new solutions to cater to the widening application base of AutoML. For instance, Aible Inc. introduced Aible Advanced in August 2019. This fully automated ML AI platform for developers and data scientists helps in generating predictive models in less time and automates all the repetitive components of ML processes.

Globally, Europe and North America are expected to emerge as the leaders in the AutoML market in 2030. This can be attributed to the improving IT infrastructure, technological advancements, and high adoption of futuristic technologies in these regions. France, the U.S., Canada, the U.K., and Germany have been making much of the autoML investments in the regions. The Asia-Pacific (APAC) market will register the fastest growth in the forecast years due to the rising government initiatives to develop the AI technology, surging investments in the IT infrastructure, and increasing adoption of emerging technologies.

Thus, the rising application of AutoML solutions in several sectors will amplify the demand for such offerings in the foreseeable future.

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