In today’s product landscape, data-driven decision-making has taken center stage, and Product Managers have a new best friend: Machine Learning (ML). No longer just the realm of data scientists and algorithm whisperers, machine learning is transforming how product managers analyze customer data, prioritize features, and make critical decisions—all without staring at a spreadsheet for hours. In particular, managing AI machine learning products requires unique skills and challenges, such as understanding data science, adapting to complex user needs, and preparing for specialized interviews in the AI/ML field.
Machine learning offers product managers powerful predictive insights, often uncovering trends and customer behaviors that traditional data analysis might miss. Whether it’s predicting customer churn, automating A/B testing, or personalizing product experiences, machine learning enables product managers to make smarter, faster decisions, keeping their products ahead of the competition.
So how do product managers use machine learning to make better product decisions, and what software tools make it happen?
1. Personalization: Creating a Tailored Experience with Machine Learning
Customers today demand personalized experiences, and product managers are increasingly turning to ML to deliver. Personalized product recommendations, individualized onboarding flows, or even unique UI layouts based on user behavior are becoming the norm.
Tools for Personalization:
Segment: This customer data platform collects and organizes customer behavior data across different touchpoints, making it easier to personalize user experiences.
Optimizely: Primarily used for experimentation and A/B testing, Optimizely now includes AI-driven personalization features that recommend content and product experiences to users.
Amazon Personalize: Powered by the same technology used by AWS/Amazon.com, this machine learning service provides recommendations for users based on past behavior.
Example:
Imagine you’re the PM of a SaaS tool designed for marketers. By integrating Segment with your app, you collect data about user actions (e.g., which features they use most, which tutorials they skip). Then, using Amazon Personalize, you can offer each user a customized dashboard with prioritized tutorials or content tailored to their role and past behavior. This improves engagement, reduces confusion, and ultimately leads to higher user satisfaction—all without manually adjusting the dashboard for each user.
2. Churn Prediction: Catching Customers Before They Leave
Retaining customers is often more cost-effective than acquiring new ones, and machine learning has emerged as an invaluable tool for predicting when users are at risk of churning. Instead of waiting until it’s too late, machine learning can flag users whose behaviors indicate they’re about to jump ship.
Tools for Churn Prediction:
Gainsight PX: This product experience platform uses ML to analyze customer behavior, enabling PMs to detect churn risks and proactively engage users.
ChurnZero: A platform specifically designed to help SaaS companies reduce churn by analyzing user engagement and providing actionable insights.
Amplitude: With advanced behavioral analytics, Amplitude identifies patterns in user behavior that indicate whether users are sticking around or likely to leave. Free Starter account available if you want to test out their software on your machine learning projects.
Example:
Suppose you’re managing a subscription-based fitness app. By integrating Amplitude, you analyze the behavior of long-term users versus those who recently canceled their subscriptions. The machine learning algorithm identifies that users who stop logging workouts for two weeks are at high risk of churning. With this knowledge, you can set up an automated campaign through ChurnZero that sends personalized workout reminders or incentives (like a free virtual class) to re-engage users before they cancel.
3. Feature Prioritization: Building What Matters Most
Deciding what feature to build next is one of the hardest parts of product management, especially when you have a backlog as long as a Tolstoy novel. With machine learning, you don’t have to rely on gut feeling or which feature request is shouted the loudest. Instead, machine learning can analyze user data to predict which features will have the greatest impact on your product’s success.
Tools for Feature Prioritization:
Productboard: A product management system that helps PMs prioritize features based on user feedback and predictive analytics. Free account available.
Heap: This tool tracks every user interaction and provides insights into which features drive the most engagement, helping product managers prioritize. Free account available.
Pendo: A product analytics platform that uses AI to track user behavior and provide insights on how to improve the product experience, including which features to prioritize.
Example:
As a product manager for a project management tool, you have 50 feature requests to sift through. You integrate Heap to automatically collect and analyze data on which features users interact with most and which parts of the app receive the most complaints. The machine learning models reveal that users who frequently export tasks to CSV are far more likely to churn. Armed with this insight, you prioritize building a better in-app reporting feature over a shiny new notification system, because the data shows it’ll have a bigger impact on user retention.
4. Predictive Analytics: Knowing the Future (Sort of)
Machine learning also enables predictive analytics, helping product managers forecast trends, user behavior, and even how new features will be received. This allows for strategic planning and more confident decision-making, without having to rely solely on historical data.
Tools for Predictive Analytics:
DataRobot: A platform that automates the development of machine learning models, allowing product managers to easily build predictive models without needing to be data scientists.
Google Analytics 360: With machine learning capabilities built in, Google Analytics provides predictive insights, such as the likelihood of users making a purchase or converting.
IBM Watson Studio: A robust AI-powered platform that helps PMs build and scale AI models for predictive analytics and decision-making.
Example:
Imagine you’re leading a product managers team for an e-commerce platform. By leveraging DataRobot, you build a model that predicts which product categories will see the highest growth next quarter based on past seasonal trends, customer purchasing habits, and external market data. With these predictive insights, you can better allocate resources toward developing features that support those categories, ensuring that your product remains competitive.
5. Automated A/B Testing: Because Waiting for Results is So 2020
A/B testing is essential for optimizing user experience, but manually setting up and analyzing tests can be a slow and painful process. With machine learning, you can automate A/B testing, dynamically adjusting variables in real-time based on early performance indicators.
Tools for Automated A/B Testing:
VWO: This platform uses AI to optimize A/B tests in real-time, automatically pushing the better-performing version to more users as the test progresses.
See below for more details on how and why to use VWO.Google Marketing Platform: Google’s A/B testing tool, which integrates with Google Analytics to offer personalized experiences and automated experimentation.
Optimizely: Known for its robust A/B testing tools, Optimizely now includes machine learning capabilities to streamline experimentation and personalization.
Example: VWO – AI-Powered A/B Testing for Smarter Decision-Making
When it comes to optimizing your product’s user experience, A/B testing is essential. But manually managing tests, interpreting results, and deciding on next steps can be time-consuming and, frankly, frustrating. VWO is designed to take the pain out of A/B testing with the help of artificial intelligence.
VWO (Visual Website Optimizer) stands out from the crowd by leveraging AI to optimize A/B tests in real-time, allowing you to quickly see what’s working and what’s not. As your tests run, VWO analyzes user interactions with different variations of your site or app, and as soon as a trend emerges, it begins pushing the better-performing version to more users. This dynamic approach ensures you’re always optimizing towards the most successful outcomes, and you don’t have to wait weeks or even months to declare a “winner.”
Why Use VWO for A/B Testing?
Here are several reasons why VWO is becoming the go-to tool for product managers and marketers alike:
- Real-Time Optimization with AI
- VWO’s AI-powered engine continuously monitors how users interact with each variant in your A/B test. If the AI detects a statistically significant trend in user preferences, it will automatically push the higher-performing version to more users. This not only speeds up the testing process but ensures you’re maximizing conversions even while the test is still running.
- Example: Say you’re testing two different landing pages. One has a video hero section, while the other uses a static image. VWO’s AI notices that users interacting with the video version are converting at a significantly higher rate within the first few days of the test. Instead of waiting for the entire test period to run, the AI automatically starts serving the video version to more users, helping you capture better results faster.
- Simplified Experiment Setup
- One of the best things about VWO is how simple it is to set up tests. Its visual editor allows you to create variations of your site without needing coding skills. Whether it’s swapping out headlines, changing button colors, or redesigning the layout, VWO makes A/B testing accessible to anyone, regardless of their technical background.
- This ease of use makes it a fantastic tool for product managers who want to quickly test ideas without needing to rely on a development team, or for those working in fast-moving environments where quick iteration is crucial.
- Multivariate Testing and Personalization
- VWO doesn’t stop at basic A/B testing. It also offers multivariate testing (MVT), allowing you to test multiple variables simultaneously. If you’re trying to figure out the perfect combination of headlines, images, and call-to-action buttons, VWO’s MVT can help you determine the most effective mix.
- Additionally, VWO’s personalization tools use machine learning to offer tailored experiences to different user segments, creating a hyper-targeted product experience that boosts engagement and conversion rates.
- Built-In Heatmaps and Session Recordings
- Understanding why users behave the way they do can be just as important as knowing what they prefer. VWO integrates heatmaps, click maps, and session recordings into its platform, giving you a visual representation of user interactions. This allows product managers to track user behavior beyond simple conversion metrics and gain a deeper understanding of how users navigate through different variants of a page.
- Example: If you notice that users in one test variant frequently hover over a call-to-action but don’t click, you can use the heatmap and session recordings to see if there’s a design flaw, such as an unclear or misleading button, and iterate accordingly.
- VWO Has a Free Account
- For product managers just starting out or those working with smaller budgets, VWO offers a free plan, which is ideal for running smaller-scale tests. This is particularly beneficial for startups, smaller businesses, or individual product managers who want to get a feel for the platform before committing to a paid plan.
- Even the free tier provides access to essential testing features, making it a great option for getting a taste of AI-powered optimization without financial risk. If you’re working on a budget but still need high-quality A/B testing capabilities, the free plan can still offer enough to deliver actionable insights.
- Comprehensive Analytics and Reporting
- VWO provides detailed analytics and reporting for every test you run, with a dashboard that breaks down conversion rates, statistical significance, and user behavior patterns. This allows PMs to track results in real-time and share easy-to-understand reports with stakeholders.
- Why It Matters: Sharing A/B test results with your team or upper management becomes effortless. The platform makes it easy to communicate which changes should be implemented based on clear, data-driven evidence.
Example Use Case: Testing Product Page Layouts
Let’s say you’re a product manager for an e-commerce site, and you want to increase the conversion rate for your product pages. Using VWO, you set up an A/B test to compare two layouts: one with product images prominently displayed above the fold, and another with customer reviews in the same spot.
Within days, VWO’s AI identifies that users respond far better to the image-heavy layout and automatically starts directing more traffic to that version. Meanwhile, VWO’s heatmaps show that customers on the review-first layout are scrolling right past the reviews and looking for images. Armed with this information, you decide to move forward with the image-heavy layout across the site, ensuring more customers are presented with the most engaging content as soon as they land on the page.
Why Should You Use VWO?
If you’re a product manager looking for a smarter, faster way to optimize your user experience, VWO should be on your radar. Here’s why:
- AI-driven real-time optimization ensures you’re always maximizing results without having to wait for the test to finish.
- Simple and accessible test setup means you can start experimenting without any technical expertise.
- Free plan availability allows you to test the waters without financial commitment.
- Advanced features like multivariate testing, heatmaps, and session recordings provide comprehensive insights into user behavior.
- Personalization tools let you offer tailored experiences for different user segments, further improving engagement.
Pros:
- Tightly connected to behavioral insights analytics tools that allow you to make data-driven decisions
- No flickering is ensured for client-side testing with a single-line asynchronous smart code
- SDK-based server-side tests mean no latency and allow users to conduct omnichannel experiments across devices
- Bayesian-powered SmartStats shows accurate test performance even without requiring a large sample size and in real-time
- User data, segments, and customer profiles from VWO Data360, the CDP, enrich the personalization of experiences across digital properties
Cons:
- Less advanced feature management as it is not the core offering of the platform.
- A product recommendation engine is missing which can take personalization of customer experiences to the next level.
For a good article on A/B testing check out VWO’s blog here
Why Machine Learning is Hot in Product Management
Machine learning isn’t just a trend; it’s becoming an essential tool in the product management toolkit. Here’s why:
Deeper Insights: machine learning digs deeper into your data than traditional analytics ever could, uncovering patterns and trends you may not have even considered.
User-Centric Development: By anticipating user needs and behaviors, machine learning allows PMs to develop products that better align with user expectations and preferences.
Automation for Efficiency: machine learning automates many time-consuming tasks, from A/B testing to churn prediction, freeing up PMs to focus on higher-level strategy.
Scalability: As your product grows, so does your data. ML scales effortlessly with your product, continually improving as more data is fed into its algorithms.
By using machine learning tools like Segment, Heap, Amplitude, Optimizely, and others, product managers can make better data-driven decisions faster. Whether you’re predicting customer churn, prioritizing features, or delivering personalized experiences, machine learning helps you cut through the noise and focus on what really matters: building a product your users love.
And hey, if it makes you look like you can predict the future along the way, even better.
Understanding Machine Learning
What is Machine Learning?
Machine learning is a fascinating subset of artificial intelligence that empowers machines to learn from data and make predictions or decisions without explicit human instructions. Think of it as teaching a computer to recognize patterns and make decisions based on those patterns. This technology has surged in popularity over the last decade, thanks to the exponential increase in computing power, particularly in GPUs. Machine learning is now a cornerstone of modern AI, driving innovations across various industries.
How Machine Learning Works
At the heart of machine learning are deep learning neural networks, which mimic the way our brains process information. Imagine a neural network as a series of interconnected layers, each made up of neurons. These neurons are essentially functions that take in data, process it, and pass the results to the next layer. There are three main types of layers: input layers (where data enters), hidden layers (where data is processed), and output layers (where results are produced).
Training a neural network involves feeding it a set of inputs along with their correct outputs. Over time, the network adjusts its internal parameters to minimize errors, effectively “learning” from the data. This process allows the network to make accurate predictions or decisions when presented with new, unseen data.
The Role of AI Product Management
What is AI Product Management?
AI product management is an exciting and dynamic role that merges the strategic skills of a product manager with the technical expertise of an AI specialist. An AI product manager needs to have a deep understanding of artificial intelligence, data science, and computer science, along with traditional product management skills. Their primary responsibility is to develop and implement AI and machine learning models to solve complex business problems.
AI product managers work closely with various teams, including product development, engineering, and data science, to ensure that AI solutions are effectively integrated into products. They play a crucial role in bridging the gap between technical teams and business stakeholders, ensuring that AI initiatives align with overall business goals and deliver tangible value.
Disclaimer: As an affiliate partner to VWO I may earn from qualifying purchases. This means that if you click on an affiliate link and make a purchase, I may receive a small commission at no additional cost to you. I only recommend products that I genuinely believe will add value to my readers.
For further reading on how technology is affecting product management Check out our article on no-code/low code tools for product managers
Recent Comments