Predictive Analytics for Product Owners and PMs - With Example Use Cases

What is predictive analytics?

Predictive analytics is a part of data science that uses statistics and machine learning to look at past data and predict future results.

It involves identifying patterns, trends, and relationships within data to forecast future events or behaviors.

For example, a software company can use predictive analytics to predict product demand, optimize resource allocation, and improve customer satisfaction.

You can develop predictive models to 

• identify at-risk customers, 
• recommend relevant products and features, and 
• forecast feature adoption. 

This data-driven approach eventually enables you to implement targeted retention campaigns, personalize product recommendations, prioritize development efforts, and optimize sales strategies.

What are the use cases of predictive analytics for a product?

You can use predictive analytics for a variety of product-related use cases, including:

Product Development:

• Predicting product success: Analyze customer preferences and market trends to identify the most appealing product features and focus your investments.
• Optimizing product design: Analyze customer feedback and market trends to identify issues and improve your product for better user experience and satisfaction.
• Identifying potential product risks: Review past development data and market conditions to identify potential issues and mitigate risks.

Product Management:

• Predicting customer churn: Use predictive models to identify customers likely to cancel or switch based on their usage and support interactions.some text
  • 💡 Tip: You can also use UserGuiding’s NPS survey to quickly gauge whether a customer is dissatisfied with your product or service. This one-question survey is a great in-app tool that collects real-time feedback.

• Optimizing pricing: Test various pricing models, such as tiered, subscription, or user-based, to find the best fit for each customer group.
• Personalizing product experiences: Offer discounts, upgrades, or extra benefits to create personalized retention campaigns for at-risk customers.

Product Marketing:

• Predicting customer acquisition: Identify the key traits of your ideal customers and assess how well different marketing channels, such as social media, email, and paid ads, work.
• Optimizing marketing campaigns: Find the best times to launch marketing campaigns based on customer behavior and trends, and create targeted messages that address your audience's needs.
• Measuring marketing ROI: Track key marketing metrics like traffic, leads, conversion rates, and customer value, and evaluate your campaigns to find successes and areas for improvement.

Product Usage:

• Predicting user behavior: Monitor customer interactions with your products, including feature usage, time spent on tasks, and challenges they encounter.
• Optimizing user experience: Use customer feedback and usability tests to find issues, then adjust your products to improve their experience.
• Identifying upsell opportunities: Create personalized offers and recommendations to encourage customers to purchase additional products or services.some text
  • Check out this in-app announcement from Crunch’s story that aims to upsell or have users join their referral program:

And the best part is… Yes, you guessed it correctly. 

They used UserGuiding’s no-code announcement modals and integrated UserGuiding with Mixpanel to see 

• how their guides perform, 
• how a certain user’s experience with their onboarding content is, and 
• what they can do to improve.

In other words, they were able to make predictions based on the data they collected from UserGuiding. 

Here’s what Product Lead Dan Harris has to say:

Product Support:

• Predicting support issues: Identify common support issues and their causes, then look for patterns in requests, like seasonal trends or links to features.
• Prioritizing support requests: Sort support requests by urgency and impact, and use a ticketing system to track and manage them efficiently.
• Improving support efficiency: Build a knowledge base for self-service support to reduce the need for human help. Use automation tools to handle routine tasks like password resets and common troubleshooting.

Step-by-step guide to predictive analytics

So, if you think that predictive analytics can help you improve your business, here is how to do it for your product. 

Step 1: Collect data through various channels

The first step in predictive analytics is all about gathering the right data from a variety of sources. 

This means analyzing customer data (such as demographics, purchase history, and feedback) to gain a solid understanding of what your customers like and need. 

You’ll also want to track product usage data, which tells you how often customers engage with your products and which features they’re using most. 

Don’t forget about marketing data, like email open rates and conversion stats, to see what campaigns are hitting the mark. 

Finally, incorporating external data, such as industry reports and economic indicators, can give you a broader context for your analysis.

Once you’ve gathered all this data, it’s time for some cleanup. 

Start by removing duplicates to keep things tidy and make sure each entry is unique. 

If you run into missing values, you can either fill those gaps or exclude them, depending on what makes sense. 

Lastly, make sure everything is formatted consistently. Standardizing things like date formats and currencies will make your life a lot easier down the road.

Step 2: Analyze behavior to identify patterns and trends

With your data cleaned up, it’s time to dig in and analyze it for patterns and trends that can inform your decisions. 

I would recommend you use data visualization tools, like charts, to reveal insights easily. 

Here are some other tips that can improve your behavior analysis: 

• Methods like correlation and regression show how variables interact. 
• Time series analysis can be especially useful for forecasting future values based on historical trends.
• Establishing key metrics to track success (like customer lifetime value and churn rate) is crucial. 

This way, you’ll know exactly what to measure to evaluate the effectiveness of your predictive models and ensure they’re driving the results you want.

Step 3: Segment users based on their behavior

By diving deep into user behavior, you can start to identify distinct segments within your audience. 

These segments can be based on demographics (like age, gender, and location) or purchase history and usage patterns. 

Understanding these differences allows you to tailor your marketing and product strategies more effectively.

Creating personalized strategies for each segment not only enhances customer satisfaction but also drives better engagement. 

When you meet specific needs and preferences, you build stronger relationships that can translate into long-term loyalty.

Step 4: Work on prediction for your use case

Once you’ve got a handle on user segments, it’s time to develop predictive models tailored to your specific needs. 

Choosing the right modeling techniques is key here. Options like regression models, decision trees, or machine learning algorithms can all be effective depending on your goals.

Training these models using historical data helps ensure they’re accurate, and evaluating their performance with metrics like accuracy and precision keeps you on track. 

The goal is to continuously refine these models based on real-world feedback, making sure they evolve alongside your business.

Step 5: Discuss the next best action

Now that you have insights and predictions, it’s time to decide on the next best actions to take. 

This could mean launching targeted marketing campaigns, enhancing product features from customer feedback, or improving support processes.

Prioritizing actions based on their potential impact and feasibility is crucial. 

By focusing on what will drive the most value, you can create a clear path forward that aligns with your business goals.

Step 6: Personalize onboarding experiences according to findings

Predictive analytics can also enhance your onboarding experiences for new customers. 

By personalizing product experiences based on what you know about customer behavior and preferences, you can make the transition smoother and more engaging.

Delivering tailored information and relevant resources can really help new users feel comfortable and supported. 

For example, Unico was able to create a smooth introduction for new users, helping them quickly grasp the platform's features and benefits. 

They used interactive walkthroughs and tailored tips that directly addressed users’ specific needs and challenges. 

This not only improved user retention but also created a more supportive experience, allowing customers to get the most out of the platform from day one.

Automated prompts or checklists that guide them through the process can significantly improve engagement and reduce drop-off rates. 

When customers have a great onboarding experience, they’re more likely to stick around for the long haul.

Step 7: Run A/B Testing regarding your product experience

Finally, to ensure the changes you’ve made based on your predictive analytics are effective, A/B testing is your best friend. 

This involves running experiments that compare different versions of a product or experience to see what works best.

By measuring the impact on user behavior and business outcomes, you can gather valuable insights. 

Plus, regularly conducting A/B tests and iterating based on the results allows you to optimize the user experience continuously. 

This ongoing process helps boost customer satisfaction and keeps your offerings aligned with what users truly want.

PS. If you need some inspiration, A/B testing examples can be your ally.

Key Takeaways

Predictive analytics uses statistics and machine learning to analyze past data and predict future outcomes, helping with product development, marketing, and support.

Here are some things to keep in mind before you leave:

• The process involves collecting and cleaning data and then analyzing it for insights. 

• Segmenting users allows for tailored strategies while refining predictive models ensures accuracy. 

• These insights guide actions like targeted campaigns and feature improvements. 
• Enhancing onboarding engages users early, and A/B testing helps validate changes for ongoing improvement.

Try UserGuiding for free to see how it can help you improve your predictive analytics.