By: Vara Kumar, Co-Founder & Chief Product and Technology Officer at Whatfix
Let’s face it, data is the key to the future of technology, and the CIOs can’t get enough of it! IDC predicts that the Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025. According to the latest Gartner forecast on worldwide IT spending, in 2022 alone, more than 783 billion worth of enterprise software was sold. SaaS spending continues to grow by 15-20% annually, as organizations maintain an average of over 125 different SaaS applications totalling $1,040 per employee annually (Blog&Gartner Report). IT typically is aware of only a third of those due to decentralized ownership and sourcing. This results in enterprises not generating the intended ROI due to wasted licenses, application redundancies, and surprise contract renewals.
In order to reduce that user gap on thousands of tools in their technology stack, product analytics tools such as DAP (Digital Adoption Platform) play a vital role.
As businesses continue to embrace digital transformation and rely increasingly on software products, the need for comprehensive product analytics cannot be overstated. With the ability to provide invaluable insights into user behaviour, product performance, and conversion rates, analytics has become an indispensable tool for businesses looking to optimize their digital offerings. According to a study by Allied Market Research, the market for product analytics has been growing rapidly, with a valuation of $10.2 billion in 2021 and projected to reach a staggering $76.7 billion by 2031, demonstrating the significant impact that this technology is having on businesses across all sectors.
CX (customer experience) and EX (employee experience), uninterrupted
Imagine an enterprise’s customer-facing application that is not getting enough customer adoption. What do they do? Unless they identify where exactly in the digital journey is the customer experience interrupted, they cannot mend it. Product Analytics helps with such insights and more.
Be it an employee or a customer at the other end of any application, their journey is unique hence needing a personalized experience There is a growing requirement to enhance customer behaviour management to provide personalized recommendations of products, which will fuel the adoption of the Product Analytics Market. In addition, the rise of the digital age has led to the digitization of traditional businesses, creating a significant opportunity for the Product Analytics Market. A McKinsey report highlights the rapid spread of digital technologies and their potential value to the Indian economy by 2025 through public-private partnerships to create new digital ecosystems. Cloud computing is becoming the norm. People are no longer asking why they should transition to the cloud. And every individual/enterprise is transitioning to cloud-based solutions. Similarly, cloud-based analytical solutions and managed platforms are also gaining traction in the Product Analytics Market. As more businesses seek to leverage the power of analytics, they are turning to cloud-based solutions that can be easily scaled and customized to their specific needs. This trend is expected to continue in the future, driven by the need for more efficient and cost-effective ways to analyse product data.
Finally, there is a growing focus on higher employee productivity and digital employee experience, leading to an increase in employee-facing applications in enterprises. These applications are designed to help employees better understand and engage with the products they are working on, and they often rely on product analytics to provide insights into product performance and user behaviour. As businesses continue to digitize their operations and focus on improving employee productivity and the digital employee experience, product analytics will play an increasingly important role in driving growth and success. Analysts are realizing that employee productivity is an important KRA at a CTO/CIO.
Erstwhile challenges with Product Analytics
Despite the potential benefits of product analytics, there are several challenges associated with its implementation and usage. One of the main challenges is that product analytics platforms require someone with programming skills to set up tracking for events. This means that most of engineering resources’ time goes in implementing and managing the product analytics software, which can be costly and time-consuming. Also, traditional product analytics platforms provide only quantitative data, such as statistics and metrics. This data requires stringent analytical skills to interpret, making it difficult for non-technical stakeholders to derive insights from the data. Additionally, businesses need to predict everything they want to track in advance, which can be challenging as new insights may emerge over time that were not initially considered.
Furthermore, ensuring data accuracy and instrumentation can be a significant challenge in product analytics platform. Data may be incomplete, inconsistent, or inaccurate, which can lead to incorrect insights and decisions. Moreover, getting contextual and actionable insights from the data can be challenging, as businesses need to interpret the data within the context of their products and customers.
Today, Product Analytics is hasslefree
To tackle these challenges, enterprises need to adopt various measures such as conducting a thorough audit of the existing systems to identify the gaps that need to be addressed. This audit should cover the data sources, data quality, data governance, and data management processes. They also need to look for a no-code analytics platform that can help in quickly building and deploying custom analytics solutions without requiring any coding skills. Such a platform should also offer pre-built analytics templates that can be customized to meet specific needs.
Using an analytics platform that provides auto-tracking of user actions will help capture and analyze user behaviour across multiple channels and devices, without having to manually track each action. Also, selecting an analytics platform that offers fast setup and granular analysis capabilities will allow quick identification and analysis of the key drivers of business, and make data-driven decisions in real-time. Enterprises further need to develop a culture of data-driven decision-making through training and education to all stakeholders, and by creating a shared understanding of the importance of data in driving business decisions. They also need to create a cross-functional analytics team that includes members from different departments, such as marketing, product, and IT, to identify and solve business problems using data-driven insights. Identification of business initiatives where data-driven decision-making has already delivered benefits, and use these as case studies to promote the value of analytics within the organization will help to build buy-in and support for further analytics initiatives.