The most successful products are the ones that solve the needs of users. Product analytics is a critical component of product lifecycle management that gives you a pulse into the effectiveness of your product, data about usage, customer churn, revenue optimization, and lifetime value of customers.
Digital products and services make the world go round. Using product analytics makes it easier for business stakeholders, product managers, and other relevant internal stakeholders to gauge the efficacy of products. While profitability is top of mind for everyone involved, revenue without product usage, product stickiness, and referenceabiity is a concern. Product analytics software helps to decode the user’s behavior to long-term value.
At the crux of it, product analytics takes away the guesswork from customer behavior and pain points and puts data at the center of decisions. Let’s take a detailed look into what product analytics is, how you can use it, various tools and softwares, and examples to help you make the best decisions.
Product analytics is the process of analyzing customer behavior with digital products to monitor and track usage patterns. It is a framework of using quantitative data from customers by analyzing behavioral data and trends by measuring various digital touchpoints with the help of specialized product analytics software.
Product analytics helps create high-value and impactful digital experiences that enhance the customer lifetime value. Not just that, it also aids in solving burning issues through a mix of continuous and long-term measurements of how products and services are used. Measuring user and behavioral data helps brands, product managers, and business stakeholders go beyond gut feelings and enhances data involvement in decision making.
You can go beyond top-level metrics like usage and generate enough insights to understand features, how and when parts are used, widely used features, features that cause users annoyance, and more.
With the help of product analytics, you can co-create with your customer’s concrete quantitative data and insights back constant active and passive listening.
Digital products are complex and multi-layered. While building a product, you start with a particular hypothesis and look to solve specific user problems. If you aren’t actively listening to your customers, there is the scope of customer churn, low adoption, low customer retention rates, and more. All of this ultimately affects the bottom line and profitability of your brand. Some factors that make product analytics important are:
One of product analytics first and most important benefits is using digital quantitative data in the product stack to understand user behavior and consumer preferences. With the proliferation of digital technology into products and services, there is a heightened awareness of customer experience and creative problem-solving.
Product analytics is the core of how businesses develop, nurture and monetize products and services. It helps organizations become highly agile and hyper-responsive with existing product gaps and enhancements in the product suite leaning into the digital age. A recent Statista article about digital-first states that by 2025, global digital transformation spending is forecast to reach 2.8 trillion U.S. dollars. By leveraging product analytics, organizations and brands can stay ahead of the curve and edge out the competition with product stickiness among customers.
Another critical component of using product analytics is that it helps validate and prove or disprove hypotheses about products, services, and feature upgrades. More often than not, product enhancements are made on the basis of guesswork, and a limited scope of stakeholders think it is necessary. With product analytics, it is possible to validate if this is what customers need.
Also, measuring various touchpoints has a granular focus on the user journey and if the product usage follows the steps you envisaged. You can also gauge the adoption of different products and services and how multiple demographics and user groups interact with the platform. This helps to align business, and revenue goals to the roadmap chalked out for the product.
When product analytics software is used on your product ecosystem, it brings data and insights to the forefront of the users. From daily users to power users and milestone-based reporting, a ton of data can be looked at and dissected to make informed business decisions. Various stakeholders can then look at this data to derive actionable insights and tweak products or services.
No longer do the product metrics lie with just one group. This also helps to eliminate tribal insights and brings everyone on the same page, with the product analytics platform acting as the single source of the truth.
Now that we know what product analytics is and who can use it let’s look at who the stakeholders in the organization can use it. It is important to note that product analytics is agnostic across products and services in various industries like software-as-a-service (SaaS), B2B, fintech, MedTech, media, consumer products, and more. The stakeholders that use it may differ from industry to industry; however, as long as the product or service has an extensive digital footprint, there is no limitation on industries using this.
Product managers and product owners are one of the most critical users of product analytics. Using various measurement tools helps them to understand the platform’s usability, scalability, and if it solves actual customer problems. It also helps to derive insights into whether the platform behaves how users expect and want it to behave.
Business owners benefit most from product analytics as it tells them if the product is performing at the level that is as envisioned. These analytics also help them decipher important metrics like returning customers, customers that churn, product gaps versus the competition, customers that return to the product, and more.
UI/UX leaders use product analytics to get a sense of the customer journey and various components working the intended. Looking at analytics also helps them understand the number of steps it takes a user to reach a custom goal.
RevOps leaders and growth managers look at product analytics to understand how they can keep customer acquisition costs low, reduce customer churn and see the money-making avenues for the brand.
Marketers can identify which campaigns bring in the most customers to each touchpoint and identify gaps to position the product in the market versus the competition. Go-to-market (GTM) campaigns are also easier to set up with this information.
Sales and customer success individuals use data from such platforms to identify sellable areas while speaking to prospective or existing customers. The data from these platforms also aids in identifying sales and pricing opportunities.
As a business, when conducting product analytics, you can measure many aspects of your consumer behavior. The top nine use cases of product analytics and various aspects that it helps measure are:
You can track your customers’ interactions and engagement with your product or service through product data analysis. You can get data on what features bring the most value to your business. You can identify the areas where customers engage most and leverage it to improve your product and increase revenue and profit.
While tracking the sales funnel through product analytics, you can measure the prospects converted into customers. The conversion rate for website-based products and services is called Click Through Rate. Here, you track the users clicking from one page to the next.
Once the product is launched, the growth rate is measured. This is an essential analysis aspect for marketers to understand as they use the growth rate to make marketing campaign decisions. It signifies the product’s life cycle and allows you to know when it is in the introductory stage, when in the growth stage, and when in the maturity stage.
Consumer switch ratio, also known as Churn is a commonly used metric to measure the loss of customers that the business has incurred compared to the new customers that the business has acquired. A minimum amount of churn shows that the product is meeting the needs of the consumers and is doing well in the market.
Cost per acquisition is also known as Customer Acquisition Cost. It shows how much it costs the business to acquire each customer. This metric is generally measured by marketing and sales parallelly as it impacts the pricing decisions of the product. The price must always exceed the production and the customer acquisition cost first to add margins and bring value through the revenues.
Customer Lifetime Value (CLV) indicates the value an average customer brings to the company’s worth. Customers with the highest lifetime value get the most value to your revenue. Using the behavioral patterns of your highest lifetime value customers, you can identify their buying journey and create targeted campaigns. You can also use the same for identifying aspects to increase conversion by bringing in new customers through the targeted campaigns.
Through product analytics, you can track and monitor current active users on your product or service. You can also identify their buying patterns on a daily or weekly basis.
Tracking session lengths assist you in identifying how long a customer typically uses a product. Tracking the number of sessions helps identify the product’s importance to the customer by measuring how many times they are searching for the same product. This helps create targeted campaigns and decide when to launch the campaigns.
When you provide different features in your product, it is essential to track which features are a hit among your customers. This helps you optimize the ones that are the most popular among the customers and remove the ones that bring the most negligible or no value.
As seen above, there are various use cases for using product analytics in a digital-first business that deals in products in services. However, from a statistical point of view, product analytics software helps run these various analysis models for better insight management.
Cohort analysis takes data from a subset and helps to sort it into various cohorts (smaller groups of people). With time, the needs and wants of different sub-groups of customers also evolve. This analysis method helps to identify how customer needs and behaviors develop over time. Cohort analysis aids in determining how the various changes in products and services also impact consumer behavior, customer churn, pricing trends, and more.
An example of cohort analysis in product analytics is an online dating platform that looks at consumer usage over months in different cohorts. While some regions and demographics of users would be happy with some features and use them more, various others may be apprehensive about using the same feature set.
Churn analysis shows how many users are sticking with your digital product and how many users are abandoning usage. Not just that, there is also a deeper insight into what stage customers churn at. Product managers and stakeholders can look at the churn analysis data in the product analytics tool to get a better insight into features and updates that are causing users to turn away.
An example of churn analysis in product analytics is an online shopping platform that introduces additional steps in the shopping experience. If other data is required, the process becomes cumbersome, or even the UX is not user-friendly, customers can leave without making purchases.
Retention analysis is a crucial metric that most business owners and product managers track. This metric helps identify how many users return to your product during certain milestones – days, weeks, or months. It also showcases the digital footprint of highly engaging customers and why they return.
An example of retention analysis in product analytics is an OTT platform that builds, curates, and hosts entertainment and sports-driven content for users. Knowing what type of content, UX, recommendations, etc., works best for the customers to have them keep coming back is critical.
Visualizing and measuring the steps customers take as defined by the tool and business objectives offers a good insight into the funnel. This analysis method identifies success metrics such as how many customers made it into the top and bottom of the funnel and growth areas depending on their interactions with the digital platform.
An example of funnel analysis in product analytics is an online survey tool nudging users to take specific actions on signing up. From the moment they sign up to reaching milestones as decided by the stakeholders offers a good insight into how many users made it down the funnel to a pre-defined success metric.
With conversion analysis, you can get a pulse of all the people who converted specific actions pre-defined in a funnel. To understand the gaps, you can then compare this data to identify gaps in the users who did not complete a particular conversion. You can then use a product analytics tool such as Statwide to deploy custom workflows to increase the efficacy of such users to higher conversions.
An example of conversion analysis in product analytics is an e-commerce site that uses various hooks and pulls to ensure a user makes a purchase based on preceding interactions. Comparing the user journey basis certain metrics, changes can be made to the e-commerce site to increase conversions in the future.
Customers engage more with brands when they reach a moment that changes the brand’s perspective. With the help of product analytics and a data management engine that tracks specific positive steps or milestones, you can uncover the aha moment that creates sticky customers for your brand.
An example of milestone analysis in product analytics is a video streaming platform capturing the various steps users take before returning customers on a higher frequency than all customers.
Various A/B testing is needed at multiple touch points in a digital ecosystem, from the first interaction to final success metrics such as purchases. Customer experience analysis tracks all user steps to tick all checkboxes on your brand.
An example of customer experience analysis in product analytics is an online shopping platform where all interactions from the first marketing campaign to viewing the site and then making a purchase are captured. This offers a deep insight into purchase behaviors and consumer behavior.
Trend analysis looks at user behavior over time and outlines various metrics for product leaders, UI/UX leaders, and other stakeholders. This analysis method looks at adoption metrics for specific touchpoints to monitor changes in behavior over time. This method also accounts for slight differences in configurations to get a pulse on in-depth user behavior.
An example of trend analysis in product analytics is shopping portals monitoring user behavior across demographics for various seasons and launches with the addition of discount codes, specialized campaigns, and more.
Journey analysis is similar to customer experience analysis, except that the product analytics software tracks the historical behavior of consumers.
An example of journey analysis in product analytics is a fintech platform looking at customers’ financial and investing journeys to get a sense of various touchpoints from initial contact to interest to awareness and, finally, a purchase.
Attribution analysis identifies the most common digital touchpoints that are associated with success. In this case, the most common metrics and behavior that lead to a pre-defined success are captured to get a pulse on what works best. This is done to replicate the model for future achievements.
An example of attribution analysis in product analytics is an online retail store monitoring all the attributes that lead to the highest purchases and monitoring returning customers.
Product analytics platforms come in various types and with certain features and functionalities. But the most successful platforms have the below components in common:
Tracking is applied directly to consumer behavior across your digital ecosystem.
Segmentation allows you to look at micro-segmentation based on various demographic aspects and where the consumers came in and when.
Profiles allow you to establish smaller cohorts of users on the criteria that matter the most to you.
Notifications allow your internal stakeholders to alert users of various touchpoints and even A/B test features so that you can monitor the efficacy of campaigns.
Dashboards allow you to use point and click logic to draw up various reporting metrics in the styles of your choice.
The ability to set up funnels to capture various paths of conversions.
Measurement tools to monitor macro and micro behavior for all your consumers.
A mature product analytics tool such as Statwide tracks user actions across your digital ecosystem to help you get insights.
While not all tools are equipped with the same features, the most common features that each of these platforms should have, are:
Automatic data capture
Capturing data is the most crucial feature of product analytics tools. The ability to capture data at all digital touchpoints across the user journey is imperative and mandatory. Without automatic data capture, there could be gaps in data which could lead to gaps in insights that would render the data meaningless.
Event monitoring
Another critical component of this platform is tracking events and allowing users to write various tags for it. This would ensure that multiple events can be clubbed into one reporting tool, and a single event can be a part of numerous reports. This ensures that the sanctity of data is maintained and various reports can be run on the data.
Data governance
How data is stored, accessed, and reported is critical. Offer role-based access to information so that the right stakeholders view the correct data without compromising the data quality to ensure your data is in good hands. As long as you can create micro-segmentation, monitor multiple events, and create different staging environments and scenarios while keeping the data safe and organized, it is imperative.
Behavioral segmentation
While demographic segmentation is necessary, a mature product analytics platform also should allow the ability to club consumer data into various cohorts and sub-groups based on their behavior and interactions with your digital platform. This allows for targeted segmentation and innovative interactions to allow for authentic interactions and reduce customer churn.
Integrations
Bringing in a product analytics platform shouldn’t mean updating your tech stack to accommodate the new software completely. It should be responsive to work within your existing ecosystem and allow for integrations to offer you a better experience with data and insights management. The more integrations the tool allows, the richer the experience.
Data warehousing
How your data is stored and accessed is critical to the success of such programs. Some softwares require you to spend time and resources to set up the backend for you, but better platforms like Statwide will have systems in place to set this up for you with a simple and intuitive interface.
Security and compliance
Every time data is involved, you must follow all local and federal state policies regarding how you capture and store data. Make sure to pick a platform that offers high data security and compliance level to give you the most out of your platform.
Now that you have a good idea of product analytics and how to pick the right platform for your business, let’s look at the top ten product analytics tools to help you transform your business.
Statwide
Statwide is a powerful product analytics tool that simplifies how you track and monitor consumer behavior. Built on an intelligent engine that leverages big data, the platform can help you transform quantitative data into powerful, actionable insights in an intuitive but robust manner.
FullStory
FullStory is a digital experience platform that lets you monitor user behavior and derive more profound insights into the customer experience. The tool helps you connect user interactions to metrics that matter.
Mixpanel
Mixpanel is a robust product analytics software that helps users create high-quality digital products for their customers. The platform also helps its customers to measure and predict user behavior across unique segments.
Amplitude
Amplitude continuously monitors user behaviors and patterns to provide real-time analytics into how to cultivate and maintain sticky customers. The platform allows tracking macro and micro level metrics about user behavior.
Pendo
Pendo is a growing product analytics platform that tracks qualitative and quantitative data to offer a well-rounded insight into the minds of your consumers.
Heap
Heap is a product analytics tool that specializes in tracking user behavior. By following granular experiences, product managers can transform customer experiences..
ImpactProduct
ImpactProduct is designed to understand better how user behavior can drive business goals. It is known for it’s intuitive nature that can be used by anyone and is not limited to data experts.
Quantum Metric
Quantum Metric allows you to put people at the heart of your products with intelligent tracking and monitoring. This product analytics platform aids in cultivating a culture of continuous product design.
UXCam
UXCam is a platform built for mobile-first applications and interactions. It is intuitive and friendly to use to aid with transforming mobile-first interactions.
LogRocket
LogRocket combines session replays and frontend analytics to create powerful mobile and app experiences. The tool is geared towards improving customer interactions and maximizing conversions.
While we have looked at various product analytics tools and platforms, Statwide stands out as it offers the most robust solution that’s easy to implement but offers high-quality, real-time insights. Built to work on mobile and application ecosystems, it is the perfect partner across a digital ecosystem.
With a powerful big data engine that looks at multi-variate quantitative data points and measures touchpoints across the entire customer lifecycle, you can transform customer experience across interactions. Powerful, role-based access dashboards offer a deeper insight into your data on which you can base revenue and business decisions.
To see how Statwide can transform digital experiences for your customers and how you can cultivate promoters for your brand, get started with a free 15-day trial!
Join companies that have successfully reduced their churn rate by up to 40% using StatWide’s predictive analytics.