Introduction to Product Analytics#

This chapter covers what is product analytics and how it is done - its role, tools, types of researches and how it helps to optimize ranking and recommendation models.

In today’s data-driven world, businesses rely on insights derived from data to drive decision-making and improve product performance. Product analytics is a vital discipline that involves the measurement, analysis, and interpretation of user behavior within a product or service. By leveraging data, businesses can gain valuable insights into user engagement, conversion rates, and overall product effectiveness. This chapter focuses on the application of product analytics specifically for ranking and recommendation models, exploring the techniques and methodologies used to evaluate and optimize these essential components of user experiences. This discipline helps you to understand “who” did “what, when, and where”. It complements qualitative methods and tools that helps you understand “why and how” a user did something while interacting with your product.

This tool helps data people and, consecutively, business to get insights on:

  • What aspects of product users engage with;

  • What are the bottlenecks to improve the product;

  • What are opportunities to reach a ceratin business goal;

  • What are general patterns of successful/unsuccessful fearure

The Role of Product Analytics#

Product analytics encompasses a wide range of practices and methodologies aimed at understanding and optimizing user experiences. It involves the collection and analysis of data to gain insights into user behavior, preferences, and interactions with the product. Product analytics enables businesses to make data-driven decisions, identify areas for improvement, and drive product enhancements. It encompasses various aspects, including:

  1. Dashboards and Visualizations: Product analytics often involves creating interactive dashboards and visualizations that provide a comprehensive view of key metrics and performance indicators. These visual representations allow stakeholders to monitor the product’s performance in (near)real-time and quickly identify trends, anomalies, and areas for improvement.

  2. Monitoring and Alerting: Continuous monitoring of key metrics and user behavior is crucial for effective product analytics. Monitoring systems help track user interactions, identify performance issues, and alert the relevant teams when anomalies occur. This proactive approach enables businesses to address issues promptly and ensure optimal user experiences.

  3. Metrics Tree: A metrics tree is a hierarchical structure that defines the key metrics and goals of the product. It provides a framework for measuring the product’s performance and aligning it with the organization’s objectives. By breaking down high-level goals into specific metrics, businesses can gain a deeper understanding of the factors that contribute to the success of their ranking and recommendation models.

In addition, there are another vital drivers to get valuable knowledge via different types of researches like:

  • Funnel analysis - classic task which aims to show you data on the passthrough and conversion of users who have gone through a funnel you define (with a various starting point like signup, onboarding, activation, purchase, click etc.);

  • Growth analysis - that help you visualize sources of user growth (e.g., breakdown of your weekly user base by new, current, dormant vs. churned users), including where users came from (i.e., acquisition sources). Aggregate engagement stats show insights into active users, and frequency and duration of sessions (e.g., DAU, WAU, MAU, stickiness, session length);

  • Retention and churn - it is a critical to product growth and various methodologies can be applied: n-period, unbound, bracketed periods (what percentage of users come back to my product next week/month etc.). Retention is a lagging indicator of habituation and a leading indicator of desired results such as customer satisfaction or virality rate;

  • User/evenet segmentation - user segementation aims to to understand who your users are and what they have in common (for example, percentage of users in a geography, using a certain platform or device, social-demographics such as gender, age, status). Event segmentation depicts aggregate stats on user actions (for instance, number of registrations, subscriptions, number of movies interacted);

  • Clickstream - individual user details to see chronological order of actions that a user took and CRM-like capabilities to show details on the user cumulative actions until some state is achieved;

  • Financial metrics - it includes customer acquisition cost (CAC) per channel or campaign, customer lifetime value (CLV), Gross Merchandise Value (GMV), Contribution etc.

This list is not limited to these metrics, but generally this is a good starting point to execute your researches. Overall, you can think of product analytics of three steps:

  1. Data collection - extracting, processing and managing raw data;

  2. Analysis - allows data person to define the goal of analysis to generate thorough report;

  3. Reporting - converting analysis results into conclusions and action points to make data driven decisions on how to proceed with product/service improvements

In practice, most end users will focus on what you have in step 3. The result can be in various forms like report document, dashboard, metrics table with interpreation.

Optimizing Ranking and Recommendation Models#

Product analytics not only provides insights into model performance but also offers valuable guidance for optimizing ranking and recommendation algorithms. By analyzing user behavior, feedback, and performance metrics, organizations can make data-driven decisions to improve the relevance and effectiveness of their models. Some key strategies for optimization include:

  1. A/B Testing: Conducting controlled experiments by comparing different versions of ranking and recommendation models to identify the most effective approach. A/B testing helps evaluate the impact of changes and improvements on key metrics and allows for data-driven decision-making. Also, more advanced approach is to estimate the potential improvement of the product metrics without executing A/B tests. After some time and controlled experiments, you can gather the history and use online data to estimate offline product metrics. However, this requires a lot of effort and great data process to come to this level.

  2. User Feedback Analysis: Collecting and analyzing user feedback, such as ratings, reviews, and surveys, provides valuable insights into user preferences and satisfaction. This feedback can be used to tailor and refine the ranking and recommendation algorithms to better meet user needs.

  3. Continuous Monitoring and Iterative Improvement: Implementing robust monitoring systems to track user interactions, evaluate model performance over time for data drift, and identify areas for improvement. By continuously analyzing data and iterating on the models, businesses can refine their algorithms to deliver more accurate and relevant recommendations.

  4. Feature Engineering: Iteratively improving the input features of the ranking and recommendation models based on user feedback and observed patterns. Feature engineering involves selecting, transforming, and creating relevant features that enhance the performance of the models.

Summary#

Product analytics serves as a powerful tool for evaluating and optimizing ranking and recommendation models. By leveraging metrics, dashboards, monitoring systems, and user feedback analysis, businesses can gain valuable insights into user behavior, measure model performance, and make data-driven decisions.