5 AI-driven Analytics Tools that Make Product Development Easier

The best way to future-proof your role is… Actually, we don’t know what it is. And we doubt that anyone else knows. In this whirlwind of an era, security, and routine are words that increasingly feel like a memory from a distant past. While we believe it’s important to create personal rituals to get you through the turbulent changes that AI is bringing to product development, embracing change and learning how to thrive in a rapidly evolving world might just be the skills you need to succeed. Product teams are already facing enormous pressure to deliver better results faster – so help in every form is welcome. AI can be a true miracle worker when used strategically, and strategy begins with analytics. So, without further ado, let’s discover the best AI-driven analytics tools available on the market today!

What is AI analytics and how does it fit into a product team’s data-driven workflow?

Product teams already rely on analytics to make informed decisions and drive successful product development. With the advancements in artificial intelligence (AI), a new generation of analytics tools has been born, offering powerful capabilities to change the way product teams extract gold from their data. These tools combine the strengths of AI technologies with traditional analytical methods to provide more accurate and actionable insights – and way faster.

What are the benefits of using AI-driven analytics tools?

Using AI-driven analytics tools can help businesses get valuable insights into customer behavior, identify patterns and trends, enhance decision-making processes, improve efficiency, and ultimately drive growth and success. Here’s how.

• Get enhanced accuracy and efficiency when analyzing historical data:

One of the key advantages of these tools is their ability to offer more accurate and efficient analysis compared to traditional methods. AI algorithms can handle large and diverse datasets, identify correlations, and detect anomalies with greater precision and speed.

The result: Iterative processes are improved and sped up while your top minds do what they do best.

• Get advanced predictive capabilities:

AI-driven analytics also excels in predictive analysis, allowing you to forecast future trends, user behavior, and product performance. By training AI models on historical data, these tools can identify patterns and make predictions with a higher degree of accuracy.

The result: Your product team is always one step ahead, reducing the number of faux pas along the way.

• Automate and save time:

Now, you can automate various aspects of the analytics workflow – tasks such as data cleaning, preprocessing, and visualization. This frees up resources for more strategic analysis and decision-making (something product managers are always in need of, right?).

The result: You can finally stop staring at dashboards and get to designing great solutions more quickly.

• Get deeper insights and personalization:

Traditional analytics methods often rely on predefined metrics and hypotheses, limiting the scope of analysis. AI-driven analytics tools, on the other hand, can explore vast amounts of structured and unstructured data to derive insights that might have been overlooked using traditional approaches.

The result: Understanding your users’ behavior and the motivation behind it is the best way to tailor product experiences and deliver personalized solutions that make users put their credit cards to good use.

Action Time: AI-driven Analytics Tools for Product Teams

We scoured the web to pick the best tools that can make the lives of product teams easier. While Google Analytics and Microsoft Power BI might be the titans in the industry, it turns out that smaller and lesser-known names are shining in the spotlight…

The last one on our list is a treat that few product teams know about – and they are missing out.

1/ Tableau

Tableau is an analytics and data visualization platform that allows product teams to interact with their data and gain insights without the need for coding.

Tableau offers AI-powered features like Ask Data, which give you the opportunity to ask questions about your data in plain language and receive instant visual responses. This capability adds to the speed and efficiency of data exploration, helping you to quickly uncover insights for use in daily decision-making processes.

Moreover, Tableau’s integration with machine learning algorithms enhances the available advanced analytics capabilities. This integration enables you to use predictive analysis and explore deeper insights into user preferences, customer segmentation, or product performance.

2/ Polymer

Polymer is a powerful AI analytics tool that can meet (and exceed) the needs of product teams. With Polymer, you can easily transform data into a streamlined and flexible database without any coding required. The tool uses AI capabilities to analyze and improve understanding of the data, helping you put the data in data-driven decisions.

One of the key advantages of Polymer is its ability to quickly and effortlessly turn spreadsheets into searchable and interactive databases. This feature allows you to explore data in a more intuitive and efficient way and helps you discover difficult-to-spot insights and trends to inform product development strategies.

3/ Qlik 

Qlik is a business intelligence and analytics platform. With its flagship offering, Qlik Sense, you can seamlessly consolidate your data sources into a unified view, enabling a comprehensive understanding of the data landscape.

One of Qlik’s key strengths is its smart associative data model. It allows you to navigate and analyze data intuitively, making it easier to identify connections and patterns. Additionally, Qlik incorporates AI-powered features, providing insights and recommendations that guide you to meaningful findings.

Qlik is designed to accommodate various roles within a product team, from data analysts to product managers. Its flexibility allows for integration into existing applications, aligning with the already established workflows.

4/ Sisense

Sisense is an advanced data analytics platform designed to meet the needs of various teams. With Sisense, product teams can derive and navigate business insights from complex data, regardless of its size or format. The platform offers a user-friendly experience, helping analysts and developers to sort, visualize, and analyze data with ease.

Sisense’s strength lies in its incorporation of AI as a core strategy. By using machine learning, the platform facilitates the adoption of analytics, enabling its users to find the gold in their data. With AI-powered recommendations and suggested actions, Sisense empowers the entire organization to maximize the potential of data and boost its analytical capabilities.

We promised you something special – and here it is. Drumroll, please…

5/ SessionStack: real-time AI summaries for session replays

SessionStack is a Digital Experience Analytics platform that caters specifically to the needs of product teams – from product design to UX optimization. Its pixel-perfect session replay is paired with robust user segmentation, easy-to-build funnels, and an error tracking tool that makes bug reproduction and resolution a breeze.

SessionStack recently introduced an AI-powered functionality to complement its session replay. SessionStackAI helps you reduce the time analyzing user sessions by condensing even the longest ones into an insightful summary. Within the summary, you will find a short description of the session, a list of the most important user actions, and a few user experience recommendations based on the events in the session.

How can SessionStackAI help you? 

Just imagine having to spend hours scouring session recordings that might not even contain anything special. With SessionStackAI, you can get an overview of the user journey in under a minute and then decide whether you want to watch the entire session. To top it off, the AI-generated summary will also help you out with meaningful UX optimization suggestions. 

To sum it up…

In the age of AI, it’s simply not worth it to invest in digital analytics that doesn’t harness its potential, right? While it might not be able to take out the strategic part of your job completely (you wouldn’t want that anyway), these AI-driven analytics tools can support your business on its way to growth. Choose wisely which tools you keep in your stack and make sure your team is always up-to-date on how to use them effectively!