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Let’s admit it – we are all holding our breaths in anticipation of what tomorrow will bring in terms of tech news. Artificial intelligence stepped out of the pages of science fiction and into our daily lives so quickly that many of us are still in a state of either shocked disbelief or desensitized denial. Stoics would probably say that there is no need to worry about the future if you cannot do anything about it, but we doubt product managers subscribe to this philosophy – having a tight deadline and a myriad of tasks to attend to is not exactly conducive to a worry-free state of mind. So, how do we fight against anxiety in a world that’s changing so rapidly? In this article, we will take a look at the analytics products that PMs can use to glean actionable insights into what lies ahead thus making their jobs a tad more predictable.
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From the early days of the internet up until now, one thing has been ubiquitous – those who have more data will always be one step ahead. Big business players have always relied on data to drive more and more sales, even before free products like Google Analytics changed the game for everyone – from focus groups to customer surveys, corporate always found a way to think ahead of the game. More recently, a whole infrastructure of data analytics products was born from the necessity to deliver user behavior data at scale to businesses of all sizes.
This infrastructure doesn’t have a clear-cut distinction of categories. We tend to think of it as a mix of overlapping analytics tools that aim to deliver data in an easy-to-read and easy-to-act-upon format. Web analytics, product analytics, UX analytics, customer success analytics… To make things simpler, the emerging meta category of Digital Experience Analytics combines the best of all of these worlds to provide a holistic grasp of all the moving parts of digital products and services.
To further complicate things, there are many challenges in front of data analytics today. Difficulties in gathering complex data (the need for manual instrumentation might put off smaller businesses from reaping the benefits of using analytics altogether), difficulties integrating one analytics platform with another, difficulties mapping the results from one platform to another, difficulties achieving a consensus on how to read the data…
With all of these problems on our minds, we are currently at a point in the development of data analysis tools that focuses on functionality, interoperability, and accessibility. Analytics products are now more user-friendly, providing us with more granular insights and easier, more visual navigation. There has been a significant shift towards cloud-based solutions, enabling businesses to store and access their data more efficiently. Additionally, advancements in machine learning and AI have led to predictive analytics becoming more commonplace. On the flip side, data regulations are becoming increasingly restrictive, and the future of AI is all but certain.
To sum it up, it’s a marvelous time to grab that popcorn and enjoy our humble attempt to
predict the future help product managers navigate the changing analytics products landscape.
Get used to getting used to new stuff… Quickly.
We agree that this is not very specific advice but we would be lying if we said that we have it all figured out. What we do know is that data is still king when it comes to navigating consumer needs, product or service usage patterns, customer experience satisfaction, and product or service optimization to improve the user experience. So, without further ado, let’s discuss a few of the hot topics that product managers around the globe might currently have on their minds…
‘By 2030, the global AI market is set to be worth over $1.5 trillion, marking an impressive Compound Annual Growth Rate (CAGR) of 38.1 percent from 2022 to 2030.’ – Oberlo
As AI and machine learning continue to advance, analytics products are becoming even more powerful tools. In the past, dedicated data analysis departments were needed to manage the vast amounts of data that businesses gathered from shop floors, e-commerce platforms, customer service centers, etc. Nowadays, data analytics products use machine learning algorithms to analyze data and predict future trends – spewing the results within one simple and visual user interface.
This enables product managers to make data-driven decisions that anticipate market changes, not retroactively. AI-powered chatbots can even be used to help product managers quickly access actionable insights and data, without having to navigate complex dashboards or search through data manually. It might be as simple as asking a question and frankly, we cannot wait for this time to become the norm.
‘83 percent of CEOs say that they want their company to be more data driven.’ – Tableau
To streamline the product management process, various analytics tools are increasingly being integrated with other tools and platforms. For example, digital experience analytics products like SessionStack now offer direct integration with popular tools like Sentry or Mixpanel, enabling product managers to track product performance and make decisions within their existing workflows.
Other analytics products are integrated with customer relationship management (CRM) tools like Salesforce, providing a more comprehensive view of customer behavior and enabling more personalized product development. Last but not least, Customer Success teams can integrate co-browsing tools with the platforms they already use (think Zendesk or Intercom) to provide in-the-moment assistance when needed. The result? Better user experiences and increased user engagement.
‘… one oil and gas company was able to increase its profit margins by 12% to 15% after adopting real-time analytics.’ – VentureBeat
As customer behavior and market trends continue to shift rapidly due to fears of recession, and Gen Z-ers entering the workforce, real-time analytics are becoming essential for product managers. Tools that provide real-time data and insights enable a more timely approach to product iterations to respond to changing market conditions and sentiment.
For example, even ubiquitous analytics products like Googe Analytics now offer built-in real-time dashboards that enable product managers to quickly identify trends and adjust their strategies accordingly. A word of caution, though – sometimes, acting fast might get you on the wrong trajectory. We need to always assess the latest data that we have against an existing benchmark, or we might just start trend-hopping and drain our available resources creating specific features that are not widely adopted after deployment.
“Data is the new oil of the digital economy.” – Wired
As more touchpoints become available, various product analytics & UX analytics tools are making it easier to access and weave data into our decision-making processes. Insights from digital channels and beyond are fed into self-service dashboards and intuitive user interfaces that enable product managers to easily uncover trends without needing to rely on data analysts or IT teams. Modern data analytics tools include much less instrumentation than before, as little as a single custom script snippet.
The magic is often cloud-based which means that businesses don’t need to upgrade their existing workflows or hire data analysts to accommodate the incoming data flows. As discussed, some analytics products offer built-in natural language processing (NLP) capabilities, allowing product managers to ask questions in plain language and receive data-driven answers in real time. Another (quite) recent development is making data available cross-functionally, which helps in the decades-long war against a siloed approach to decision-making. Platforms that provide reports and dashboards that can be read and understood by more than one department are certainly winning in our book.
While all of these things look good on the surface, there is also a darker side to data collection. Poor data practices enable data leaks which enable malicious actors to strike big. The scope and scale of recent malware attacks have prompted the creation of task forces to tackle the problems arising from the increasing amounts of data traveling across the internet. Whether they will succeed in taming hackers with legal action is dubious. But the future will inevitably force product managers to reassess the data collection and sharing policies of their organizations to make sure they don’t become an easy target.
In an ever-changing data landscape, one thing is for sure: we cannot be sure about anything. What we can do, though, is try to stay ahead of the curve with the help of data. Digital experience analytics products can be an invaluable analysis tool to combine heaps of actionable data in one package, saving time and resources, and serving as a data hub between different departments (among which are product development, customer success, and marketing). Weaving data visualization, AI and machine learning, and powerful new integrations will certainly help us move forward more strategically and ultimately – more confidently.
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