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.
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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.
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.
