What is Product Analytics

To achieve success in the digital age, companies must continually deliver digital products and services that exceed customers’ rising expectations. To do that, businesses must continually monitor and assess the performance of the products they build. 

Many companies are turning to product analytics to understand how customers engage with their digital products. By analyzing behavioral data, they can identify opportunities to create value and improve the user experience. 

What is Product Analytics?

Product analytics refers to the tools and processes that help companies understand and act upon the volumes of data generated by customer interactions with their products. These solutions help teams keep track of user engagement and behaviors in real-time and make product decisions that have an identifiable impact on the user experience. They also provide insights into product usage, helping companies identify patterns in average daily, weekly, monthly, and annual users. 

Why is Product Analytics Important?

In this era, every company is a technology company. Regardless of industry, most customers’ first interaction with a business is through digital mediums. With competition and customer expectations continually rising, companies must embrace a culture of digital transformation, self-evaluation, and constant improvement. Product analytics is the essential cornerstone for companies to embrace this paradigm shift by understanding customer needs and thoroughly analyzing their usage patterns. 

Product analytics is essential because product strategy aligns directly with the objectives of the business. Business success always comes down to a simple concept: Acquiring customers and keeping them. 

For a digital company to grow its customer base and avoid churn, it must first understand what users want. The good news is any company selling digital products and services already has all the data they need to understand their customers’ wants and needs. Product analytics solutions solve this problem by combing through large volumes of data and providing actionable insights about customer behavior in real-time. 

Who Benefits From Product Analytics?

One of the central underpinnings driving digital transformation is that every action, task, or initiative should link back to the core business strategy. That’s why the value of real-time, product-level, customer-centric data insights should not be limited to one team of specialized analysts. Beyond the apparent applicability to product team members like PMs, engineers, and analysts, these solutions serve essential functions in decision support across all business units and technical teams. Here are a few common examples:

• PMs
• Engineers
• Business Leaders
• IT Operations
• Sales
• Marketing
• Analysts

Product analytics works best when teams across the business are leveraging the insights and sharing their findings in the context of their roles. Despite this, the primary beneficiary of product analytics is the customer, having their needs more readily addressed by products and services. 

The Essential Features of Product Analytics

Products and services are incredibly dynamic, with a near-constant stream of releases, features, and customer support changes all happening across multiple suites of product baselines and permutations. All of these things can have an immediate impact on the user experience and their subsequent behavior. The right product analytics solution can help the business answer the most critical questions about their customers and how their products perform: 

• What are our usage metrics?
• Who are our most valuable customers?
• Who are the customers most likely to churn, and why? 
• Are there indicators we can identify for users at risk of churn?
• Are there common characteristics for the users who enjoy our products the most?

Manual processes built around monitoring tools are often insufficient at answering these questions on time. To get there, companies need an automated product analytics solution to monitor all relevant product data sources. Solutions augmented with machine learning can understand everyday and seasonal user behavior and alert companies to mission-critical deviations in real-time. 

Aside from anomaly detection, it’s also critical for product analytics solutions to track key product performance metrics across multiple data points. Information about usage, logins, registrations, repeat users, conversion rates, and payments can inform product decisions in real-time. It’s equally essential for a solution to communicate this information in a clear, actionable, and consumable way. 

Product Analytics Drives Product Success

Any industry or company that reaches customers with a digital platform can benefit from product analytics. Whether companies offer products and services directly to consumers, B2B, e-commerce, or social media platforms, product analytics can help drive growth and reduce churn by informing critical product decisions. 

Here are just a few examples of what product analytics enables companies to achieve:

• Develop products and features informed by data-driven strategies that align technology with business objectives
• Measure successes and react to challenges in real-time
• Gain a visualized, contextual understanding of how users interact with their product to hone in on the most impactful ways ahead. 
• Gain a clear picture of segmented customer profiles
• Use behavioral targeting to identify and drive growth opportunities.
• Identify and correlate the behavioral and product factors that lead to churn.

Common across all businesses, industries, and roles is the need to deliver products and services that customers love and keep them coming back. Product analytics empowers individuals and teams at every layer of an organization, from product teams to senior business leaders and everything in between. It does this by autonomously distilling millions of data events into consumable, actionable information that can drive real value into the product decisions that impact users the most. With the right solution, a team can detect and react to product and service incidents as they happen, and in some cases, before they happen at all.