Product Analytics Market 2020 - Analysis, Size, Business Growth, Key Players and Future Scenario | Amplitude, Inc., Gainsight, Inc., Google LLC, Heap Inc., IBM Corporation, Latentview

Product analytics is an application of analytical software and Business Intelligence (BI). Many industries thrive to gain quantitative understanding of what users are doing with products, which is expected to drive the adoption of product analytics. Product analytics is used to analyze service reports, customer feedback, product returns, warranties, and information from connected sensors to enable manufacturers to evaluate defects in products. Product analytics is also used to find out future possibilities for product developments, identify designs in utilization of products, and connect every opportunity to customers. Product analytics can consolidate feeds from social media platforms to follow feedbacks about products and help companies to improve their products and services.

Rise in utilization of advanced technologies such as Big Data, Machine Learning (ML), artificial intelligence (AI), and other analytical technologies by product-specific industries such as manufacturing is the major factor that drives growth of the product analytics market. Moreover, product analytics solutions help vendors to get an increasingly detailed understanding of consumer attitudes and needs, that aides in deciding faster choices in everyday business and deliver improved execution. This, in turn, boosts growth of the market. However, rise in concerns associated with confidential data is a factor expected to hinder the market growth. Furthermore, rise in demand for cloud-based analytical solutions and managed platform are opportunistic factors for product analytics market.

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Owing to rapid technological advancements and diversified customer demands, firms can no longer dominate the product analytics market with only one product for a long time. To meet changing customer needs, many manufacturing and retail industries are compelled to adopt various business strategies such as convergence of technology to thrive in the fast evolving market.

Artificial intelligence increases functionality of business intelligence applications as it helps to breakdown large volumes of data into granular insights, allowing organizations to better understand the value of smaller components in a larger picture. For example, Amazon Go is changing conventional shopping background by utilizing analytics to drive deals and purchaser commitment. Product analytics makes that information valuable by combining all information sources into a single sorted out view, which can demonstrate accommodating for getting simple bits of knowledge.

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Vendors with an online and offline existence process all types of user data. User data can include personal data of customers, transactional data on online purchases, and financial data such as credit card. With this kind of data in vendor control, they need to make sure that it is stored and processed in a way that prevents any potential data breaches. Obligations and requirements differ depending on region, for instance, data security demands tend to be more stringent in Europe than in the U.S.

Cloud data storage adds some risk, particularly when it comes to handling transactional and financial data of clients. This information should be stored on a secure infrastructure and protected with encryption, two-factor authentication, firewalls, or other means. Since modules are hosted on external servers, there is no control over them, and only limited possibilities are there to detect potential breaches caused by malicious code modifications.

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Major players analyzed include Amplitude, Inc., Gainsight, Inc., Google LLC, Heap Inc., IBM Corporation, Latentview, Mixpanel, Pendo.io, Inc., Piwik PRO, and Plytix.

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