The global product analytics market is projected to generate a revenue of $38,049.5 million in 2024, advancing at a 18.8% CAGR during the forecast period (2019–2024). The key driving factors of the market include the surging adoption of smartphones, rising focus of product teams to offer personalized services and products to their customers, shift from web-based internet surfing to mobile-based internet surfing, and advancements in cognitive technologies. In terms of service type, the market is divided into professional and managed services.
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The professional services category dominated the product analytics market in 2018 due to the increasing demand for expert training and insights into the deployment and usage of advanced analytics solutions and growing requirement for consultancy. In terms of deployment type, the market is classified into cloud-based and on-premises. The cloud-based classification held the larger share of the market in 2018 and is further projected to grow at the fastest pace during the forecast period. The reason for this is that the cloud-based deployment allows fast data access to product teams, which improve customers’ behavior tracking to gain competitive edge, and flexible storage.
When industry is taken into consideration, the banking, financial services, and insurance (BFSI) category is expected to account for the largest share of the product analytics market during the forecast period. This is attributed majorly to the shift in preference from conventional banking to digital banking, rising demand for personalized banking services, improved customer service experience, and growing preference of customers toward digital channels. The enterprises are increasingly adopting intelligent analytics in order to enhance their interactions with the customers, which leads to the business’s sustainability, growth, and profitability.
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A key driving factor of the product analytics market is the advancements in cognitive technologies. The cognitive technologies, which include deep learning technologies, such as convolutional neural networks, are based on the human brain’s ability to learn through inference and decomposition. These technologies are projected to become the standard approach for the processing of complex data streams produced for offering active insurance products to the customers as per their individual activities and behavior.
