Uncomplicated product analytics for critical product decisions.

Product analytics is the process of understanding how users engage with a product or service, and how to retain them. It enables teams to track, visualize, and analyze user engagement and behavior data. Different teams can use this data to improve and optimize their products. Digital products can include any type of digital property, from an entire website, mobile app or kiosk, to a specific journey, funnel, page, or feature. Tools to help understand usage and performance can include user analysis like cohort, churn, and retention analysis, as well as visualizations in the form of heatmaps, journey analysis, and session replay. Product analytics are primarily used by product managers and product analysts, but are increasingly also used by UX, CX, marketing, and even engineering teams. 

Product analytics is used to understand the behavior of users across products or services to inform decisions about how to improve the product experience and increase product engagement. Product analytics is different from experience analytics in that it focuses on engagement of users across the entire customer journey across multiple sessions. Experience analytics, on the other hand, tracks specific interactions within a session using heat maps or session replay, and aims to understand any struggles during an interaction, or what prevents a user from converting in the session. In other words, product analytics tends to focus on tracking unique users across sessions, whereas experience analytics tends to focus on activity within the session.

Product analytics is focused on understanding engagement of users who engage with your brand across multiple sessions and devices (native app and web). Web analytics, on the other hand, tends to focus on analyzing anonymous traffic to your website, understanding how they get there and how to convert them. In other words, product analytics is focused on understanding the behavior of users and segments over time, how frequently they engage, if they return and how they get value from your product. Traditional web analytics are often used by marketers for measuring attribution as they acquire and convert traffic arriving anonymously from email or paid marketing campaigns. However, to truly understand why users convert or drop out of the funnel, companies need additional analytics at the user level.

• Product teams can understand what users are doing, make data-driven decisions, measure, and run experiments to increase activation, conversion, and retention.
• UX/Design teams can get data on how people navigate feature sets, see what’s popular (and what’s confusing), identify roadblocks, and pinpoint key moments of abandonment.
• Engineering teams can uncover user friction to find and fix technical or implementation flaws, such as bugs, errors, or falling APIs.
• Analytics teams can get a complete view of user engagement to help define and refine business strategies and optimize them according to needs.
• Customer service & support teams can track the health of product features in real-time to answer customer questions, resolve concerns more quickly and reduce call volumes.
• Marketing teams can identify which programs bring in the most visitors, understand how users want to use the product to better market it, and uncover what users do with the marketing information they receive. 

Product analytics helps digital product teams improve KPIs related to engagement, retention, and customer lifetime value, for example: 

• Engagement
  • How customers use the product: which features used the most vs. least, how often users return, what actions they’re taking
  • How to make product stickier for new and existing customers
• Retention
  • Do customers come back: when, how often, and via what channels
  • What are the drivers to increasing return and retention, or churn
• Customer lifetime value
  • Identify and categorize the most valuable customers based on demographics, spend, behaviors, retention, etc. 
  • How to transition lower-value customers to high value customers, thereby increasing business performance

Traditional, stand-alone product analytics were built for specialized experts who have the bandwidth to instrument and translate complex data into product-specific KPIs. However, product teams aren’t the only ones who own the digital experience. Quantum Metric’s product analytics solution is different in these ways:

• We serve the complex needs of large B2C companies, simplifying digital complexity with out-of-the-box industry-based guides that provide step-step directions to diagnose behavioral and technical friction for every customer journey, from search to checkout.
• We deploy with a single tag or mobile SDK to capture dozens of behavioral and technical signals securely without configuring on day 1. No additional engineering resources are required to tag at the code level.
• Our data is captured in real-time and our platform generates baselines so teams are alerted in real-time to changes in their KPIs and can diagnose issues faster.  Teams across product, UX, engineering, operations, and customer service can be aligned with one view of the customer experience and what to prioritize next. 

Product analytics can help you answer a variety of questions about your digital offerings. Here are a few:

• Which customers are most engaged with our products and features?
• How can we improve customer retention?
• How can we test which new features convert higher so we can make rapid product decisions?
• Which features have the highest engagement and keep users coming back to our app?
• How can we get a complete picture of user engagement and conversion across mobile and native apps, from landing page to checkout?
• How can we target users based on their behavior to offer relevant products or services?
• How can we increase viewership and/or readership to drive content decisions?
• How can we create more personalized offers to increase customer loyalty?

Product analytics tools typically require manual data capture, which is often time consuming and inefficient. It requires knowing what questions to ask in advance, and waiting for engineers to manually code and configure your data implementation. With Quantum Metric’s autocapture, teams spend less time figuring out what interactions to focus on, improving time to value. Key digital interactions are automatically recorded with an out-of-the-box software installation, allowing user behavior to be monitored right from the start. Links, buttons, taps, swipes, rage clicks, and replay experiences are automatically identified and trackable — no element-level tagging required. It’s no surprise, though, that sometimes you need to track complex or customized metrics and KPIs. Quantum Metric’s UI based tracking allows you to configure custom metrics and attributes without ever touching your code.

When selecting a product analytics platform, it’s key to ensure it integrates with your current tech stack so you can avoid adding unnecessary tech debt or integration work. The right platform can boost workflows with a VOC survey solution like Qualtrics, a CRM like Salesforce, experimentation tools like Optimizely, service management tools like Salesforce Service Cloud, Servicenow, or JIRA, and even supplement traditional analytics tools like Google Analytics and Adobe Analytics. Here are a few sample use cases:

• Voice of Customer feedback: VOC survey solutions help you collect and understand customer feedback. Quantum Metric adds visual evidence and quantification directly with one click from a survey verbatim. Easily share replays with your digital teams to reproduce, resolve, and improve digital experiences, and — with one click — prioritize based on the impact to your business.
• CX and CRM: Improve contact center outcomes with real-time customer insights directly inside Salesforce Service Cloud or ServiceNow CSM. Quantum Metric’s embedded replay solution lives inside agents primary workflow, so agents can watch and troubleshoot a customer’s digital experience without leaving their CRM.
• Digital experiments: Using statistical significance to understand which A/B test recipe has won is table stakes. Going beyond that to understand why is critical to ensuring you capture appropriate learnings and apply them to future experiments. Quantum Metric lets you seamlessly integrate with experimentation solutions like Optimizely to bring behavioral analysis like heatmaps and session replay into your A/B test results, understanding what’s driving a specific recipe to outperform or underperform.
• Data platforms: When it comes to big data, speed usually doesn’t come to mind. But with Quantum Metric’s seamless integrations with other data and analytics tools (such as Adobe Analytics or Looker), you can seamlessly pass behavioral information for alerting, reporting or segmentation. This will enable you to visualize and discover new hidden segments, analyze customer cohorts, and better predict churn.