What Is Product Analytics? (With Types and Benefits)
By Indeed Editorial Team
Published November 14, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
When a business launches a new product, it's crucial for the company to understand how customers react to the new release. Product managers often use different software and mechanisms to measure and monitor customer satisfaction. Learning more about the analyses you can perform on a company's product data may help you better understand how to market a good or service efficiently. In this article, we define product analytics, explain how it works, discuss why it matters to the product's success, and outline the types of analytics unique to different projects.
What is product analytics?
Product analytics, also known as behaviour analytics, refers to collecting and converting user-generated data into useful information. This information helps the product team understand how the end user interacts with a product or service. Many companies often use this data to track and visualize the user journey.
By analyzing the user journey, the company can determine how much value they provide to customers with its products and service. Analytics is essential in marketing, as it allows the business to allocate resources to sectors more likely to generate profit. Different analytic tools help the company's management assess how well the product that it designed performs.
Why is behaviour analytics important?
Before releasing a new product into the market, companies often rely on previous data to make well-informed, strategic, and profitable product decisions. Traditionally, product teams collect this data through customer surveys, interviews, and community discussions. These data collection mechanisms are often expensive and rarely provide real-time insights into the customer's satisfaction and how they interact with the product.
Analytics allows the product team to conduct a deeper analysis of user behaviour by understanding how end users interact with different aspects of the product. Analytic data can also help the team modify its idea-generation process and optimize the product development system.
Types of product analytics
The development team can implement different types of analytics to get quality information about the company's product and user satisfaction. Some of these analyses include the following:
This reporting provides an overview of market trends that affect a product over time. It can help the product team predict whether users might accept or reject a feature over a specific period. This analysis focuses on one or more specific points in the user journey by assessing their performance and how much time they spend on specific journeys. Other team members, like user experience (UX) designers, sales and marketing specialists, and customer support representatives, can use this data to develop marketing strategies and increase the company's revenue and customer retention.
The journey analysis provides a visual representation of the user's path. This journey allows the product team to gain valuable insight into the different issues affecting customer interaction with the product. Journey analysis uses a predictive design model that allows the development team to anticipate and resolve issues that may affect product performance.
Attribution analysis can help the business determine which point of contact signals success. Here, the product team monitors how data flows from the product and uses this information to segment users based on internal criteria set by the business. By analyzing these parameters, the business can accurately determine how many users complete their journey and how many prospects have become solid leads.
This analysis takes data from a particular subgroup and sorts it into related groups known as cohorts. These cohorts share similar characteristics, such as time, size, and data. Businesses often use cohorts to analyze customer behaviour throughout a customer life cycle within a specific segment.
Cohorts vary depending on the parameters that are relevant to the business. For instance, users who visited the website for the first time within a specific period or those who signed up from a particular landing page. It can also comprise potential clients who spoke to a sales lead for the first time and those who subscribed to a premium plan. The business may use these segments to provide more suitable offers for these target audiences.
Retention analysis can help the business understand why and how customers churn from the product. A churn rate can indicate how many customers have stopped using a product or service. With this insight, management can develop market policies and strategies that improve customer retention and lower acquisition costs. The data from this analysis can also help the company develop marketing efforts that improve customer lifetime value and customer satisfaction.
Advantages of behaviour analytics
Here are some benefits of utilizing analytics in product development:
Informed business decisions
Businesses use analytics to manage product development, reduce risk, and increase customer retention. By analyzing product and customer data, the team can predict what pain points customers experience with the product and monitor how those changes affect customer demand. Product data allows the business to build a competitive service that leads to higher customer satisfaction.
Personalized customer experience
Businesses gather customer data to improve their customers' digital experience with their products. By analyzing user-generated data, product teams understand customer behaviour and use that information to personalize the product experience. Product and marketing teams may segment users based on unique characters that signal success and growth.
More efficient functions and operations
Businesses often use analytics to enhance operational efficiency. By collecting and examining qualitative data about a product, you can determine the origin of production delays and forecast any future problems that may occur. For instance, if the product prediction reveals that users don't interact with a particular feature, the team can remove this feature to avoid a decline in customer satisfaction.
Reduced market risk
Access to data can help the business improve the quality of its bottom-up risk assessment system. Management can recognize shifting trends in product usage and incorporate changes when necessary. This information can help the company reduce waste due to obsolete and inefficient production.
Who uses behaviour analytics?
Here are some team members that use this process:
Product managers: These professionals use analytics to monitor and understand how users utilize the product. They also use analytics to make data-based decisions, improve product features, perform experiments, and increase activation, retention, and conversions.
Marketers: Marketers use analytics to understand which marketing programs generate traffic and determine which marketing platform provides the most conversions. They may also use this information to optimize marketing campaigns and social media promotions.
UX designers: UX designers use analytics to study how users navigate the product. Analytics can also help them understand which features are confusing to users and which features they prefer.
Development team leaders: These professionals use analytics to discover and eliminate bugs that affect product performance. They also improve features to make it more user-friendly to customers.
Growth managers: They use this data to develop detailed strategies based on the business needs. To achieve this, growth managers study product data and trends to understand the entire customer journey.
How can you track user data?
You can use analytics tools to track the actions users perform on the product. Here are some tools you can use to generate accurate results:
Automatic data capture: Product teams save time when collecting data with automatic data capture. Manually tracking product and customer data consumes resources and time and might produce errors.
Virtual events: With virtual events, product teams can allocate multiple labels to different assets with the same auto-captured relationship without altering the dataset. Virtual events also offer uninterrupted adaptability, as the development team can separate important events, answer business questions, and test different hypotheses without rewriting code.
Unit analysis: This analysis provides information on how many users return to the product. Access to this data can help the marketing and product team understand what channels provide the most conversion.
Data administration: This tool is necessary for organizing and safekeeping sensitive data. It easily categorizes segments, reports, and events, allowing each department to generate important insights into customer behaviour.
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