How Product Analytics Helps Scale Products

Author of the article: Kristina Kurdyumova

Product Mentor, Product Manager

Product analytics is a powerful tool that allows companies to not only optimize their products, but also to scale them up in a sustainable manner. With deep data analysis, companies can better understand their users, tailor their product to their needs, and achieve their business goals faster.

In this article, I will tell you how leading companies in the Russian and international markets such as Yandex, Avito, Booking and Google use product analytics to scale their products.

Step 1: Understanding Users with Data

Product analytics starts with user behavior analysis. Only by knowing how users interact with your product can you make informed decisions. This step is especially important for scaling, as without a clear understanding of what attracts and retains users, scaling may be ineffective.

Example: Yandex uses analytics to understand how users interact with different products within the ecosystem. This helps them tailor interfaces to different audiences. Thanks to detailed analysis of user behavior, Yandex was able to improve recommendations in Yandex.Market, which led to an increase in purchases and an increase in the average check.

Step 2: Optimize with A/B Tests

A/B testing is one of the key tools for product growth. It allows you to test different hypotheses and choose those that really work. This is important not only for improving the current product, but also for scaling it, since testing helps find the most effective solutions for all stages of interaction with users.

Example: At Booking, every change on the site is tested using A/B tests. So, when changing the booking form, the company's analysts conducted dozens of tests to find the optimal design that would minimize the number of refusals at the payment stage. Such tests help the company increase conversions, which is critical for scaling a global service.

Tech companies like Google, Amazon, Facebook, Airbnb, Netflix have built a continuous process of experimentation. The number of experiments per unit of time is astounding:

• Airbnb tests 700 hypotheses per week,

• Uber, Amazon test 1200 hypotheses a week.

The growth of a company directly depends on the number of hypotheses tested. The more hypotheses are tested through A/B tests and other experimental methods, the faster companies can find working solutions, adapt the product to market requirements and increase its competitiveness.

How many hypotheses do you test per week?

Step 3: Product Analytics and Personalization

Scaling a product is closely related to improving the user experience. One of the key strategies here is personalization. Product analytics helps collect data on user preferences and customize the product so that each user receives the most relevant content.

Example: Avito uses analytics to personalize the ad feed. Depending on the user's behavior on the site, the most relevant ads, stories, and selections are shown to him. This not only improves the user experience, but also significantly increases the chances of a successful transaction, which, in turn, contributes to the growth of activity on the platform and its scaling.

Step 4: Impact on User Retention

Analyzing user behavior data helps identify factors that influence user retention. Reducing user churn is one of the most effective scaling strategies. Products that lose users cannot grow steadily, so it is important to identify problem areas in a timely manner and eliminate them.

Example: Facebook places a huge emphasis on user retention. The company’s analysts found that users who added more than five friends in the first few days after signing up were more likely to remain active on the platform. This discovery allowed them to focus on features that encourage adding friends, which helped improve retention and increase the active user base.

Step 5: Analyze Growth Metrics

When scaling a product, growth metrics such as LTV (Lifetime Value), Retention, CAC (Customer Acquisition Cost), and conversions at different stages of the funnel play a key role. Product analytics helps you track these metrics in real time and adapt your strategy based on their changes.

But to gain a deeper understanding of change, it is important to use not only target metrics, but also proxy metricswhich allow you to record intermediate steps along the user’s path. Proxy metrics are important at each stage, as they help you understand where difficulties arise and at what stage of the process it is worth making changes to improve overall conversion.

Example with a sales funnel: If the target metric is completing a purchase, then proxy metrics include steps such as viewing a product page, adding a product to a cart, completing payment details, and clicking the “buy” button. Each of these metrics represents a specific step, and if any of them show a sharp drop, it signals where the product needs optimization. For example, if adding a product to a cart is consistently high, but completing payment details drops sharply, this may indicate a problem with the interface or complexity of data entry.

Example: At Google, analysts closely monitor metrics such as time spent on services and retention rates. Google's North Star Metric (NSM) is essentially an engagement metric, such as active use of products and time spent on them (such as Google Search or YouTube).

This data allows Google not only to improve existing products, but also to quickly make decisions about launching new features or changing promotion strategies.

Step 6: Scaling Through New Markets

When a product is growing steadily in one market, the next logical step is to enter new markets. Product analytics helps a company assess how ready the product is to scale to new regions, as well as adapt the product to the specifics of local users.

Companies entering new markets actively use analytics to minimize risks and increase the likelihood of success. Before scaling or launching in new regions, a deep analysis of user behavior is conducted to adapt the product to local characteristics and preferences. Product analytics helps to adapt interfaces, content, and communications, which helps to avoid misunderstandings with the local audience and maximize the chances of a successful launch.

For example, data analysis helps you assess which features or approaches work best in certain markets, which helps you minimize costs and improve launch efficiency.

Step 7: The Importance of Cohort Analysis

Cohort analysis is another important tool for scaling a product. It allows you to analyze the behavior of different user groups (cohorts) over time and identify key factors that influence retention and engagement. Using cohort analysis, you can make more informed decisions about how to improve your product and which audience segments to focus on.

Example: At Avito, cohort analysis helps track the behavior of users who are interacting with the platform for the first time and compare them with more loyal users. This helps the company better understand what factors influence the engagement of newcomers and how to increase their activity on the platform.

Step 8: Analytics-Driven Automation

As the amount of data grows, product analytics should be automated. This allows product teams to quickly obtain the necessary information and make decisions in real time. Automated analytics also helps to manage product scaling more effectively, as many routine processes can be set up to be performed automatically.

Example: Google actively uses automated analytics tools that allow tracking user behavior and changes in metrics in real time. This helps teams make decisions faster and adapt the product to new conditions.

More about analytics – telegram channel: @blog_kak_business

Additional articles on product analytics:

1. A/B Test Design: Step-by-Step Guide with Theoretical Foundations

2. How a Product Manager Can Test Hypotheses Quickly

3. How to find growth points for a product?

4. Metrics tree – how to build, where to start?

Product analytics is a fundamental tool for scaling products. It allows you not only to optimize current processes, but also to find new growth points, improve user experience and increase revenue. Using product analytics, companies such as Yandex, Avito, Booking, Facebook and Google were able to build successful strategies for scaling their products, constantly adapting them to user needs and market demands.

You can learn how to solve product analysis problems (including working with SQL and Python, A/B testing and data visualization) on the online course “Product Analytics” under the guidance of experts.