← Back

Product Management Data Analysis

2023-06-16 // Nick End, Founder

In today's data-driven world, the role of a product manager has become increasingly reliant on strong data analysis skills. Tech companies are constantly collecting vast amounts of data from various sources, and it is the responsibility of a product manager to extract meaningful insights from this data to drive informed decision-making. In this blog post, we will explore the essential data analysis skills required for product management positions at tech companies.

Overview of a product manager's responsibilities

Before delving into the specifics of data analysis skills, let's briefly outline the general responsibilities of a product manager. Product managers serve as the bridge between business strategy, user needs, and technical execution. They are responsible for defining the product vision, conducting market research, gathering user feedback, prioritizing features, and collaborating with cross-functional teams to deliver successful products. In short, product managers are responsible for coordinating activities across an organization that are critical to a product's success. In all these aspects, data analysis can play a crucial role. While not every decision can be driven by data, it is ideal to have supporting data or evidence for each decision. Product manager data analysis skills cover a broad range with some product managers limited to spreadsheet skills, others able to write SQL and use business intelligence tools, like Tableau, PowerBI, and Looker, and the more advanced PMs able to write Python to complete analyses. While more advanced skills are certainly helpful, they are not necessary to be successful as a product manager. Less technical product managers can always lean on business partners in BI to capture hard to track metrics or learn new skills in order to get the metrics they need. At Row Zero, we are creating an analytics product with the power of premium enterprise BI products and the simplicity of a spreadsheet, enabling everyone to perform the analysis they need.

Below we cover the key topics for product management data analysis

  • Data analysis responsibilities for product managers
  • Assess data quality
  • Common quantitative data analysis for product managers
  • Best data analysis tools for product managers

Data analysis responsibilities for product managers

Data analysis is an integral part of a product manager's role as it provides valuable insights for decision-making. Product managers should possess the following data analysis responsibilities:

  • Gather and Organize Data: Collect and organize data from various sources, such as customer feedback, user behavior, market trends, clickstream metrics, and purchase behavior. This involves utilizing tools like analytics platforms, CRM systems, user research apps, surveys, and data warehouses to gather relevant data.

  • Define Key Metrics: Identifying and defining key performance indicators (KPIs) to measure product success. Product managers need to understand the business objectives and translate them into measurable metrics that align with the product strategy.

  • Analyze Quantitative and Qualitative Data: Product managers should be adept at analyzing both quantitative and qualitative data. Quantitative data provides insights into user behavior, engagement, and conversion rates, while qualitative data offers valuable context through user feedback, anecdotes, surveys, and interviews. Balancing and interpreting both types of data helps in gaining a comprehensive understanding of user needs and preferences. This blog post will focus on quantitative data analysis.

Assess data quality

Data integrity and cleanliness are fundamental aspects of data analysis. It is essential for product managers to ensure the accuracy, completeness, and reliability of the data they work with before beginning an analysis. Some best practices include:

  • Data Validation: Product managers should verify the accuracy and consistency of the data by cross-referencing multiple sources, asking co-workers who have experience with various data sets, and scrolling through data sets to sanity check that values make sense and are consistent with the metric being tracked. This helps in identifying any data anomalies or discrepancies before starting an analysis. At big companies it is common to uncover legacy data sets with unreliable values. Tribal knowledge is sometimes the only way to figure out which data set is accurate. In more organized companies, resource exist to inform the analyst of the appropriate data sets to reference.

  • Data Cleansing: Even after finding the correct data set, data sets often contains errors, duplicates, or irrelevant information. Product managers should audit data and have the ability to clean and transform it to ensure its quality before performing an analysis. This can be performed quickly using filter, sort, and histogram features to identify anomalies. Transformation involves removing duplicates, filling in missing values, and standardizing data formats.

Common quantitative data analysis for product managers

Product managers should be familiar with various data analysis techniques to derive insights and make data-driven decisions. Here are three key types of data analysis frequently used in product management:

1. Market research

Before embarking on new products or features, it is often a product manager's responsibility to assess the product opportunity with market research. This type of analysis could mean gathering market data to identify the market opportunity a product will focus on or evaluating internal business metrics, like purchase and return behavior, to calculate the potential financial impact of a new product or feature.

2. Funnel analytics

Funnel analytics track user behavior across a series of steps in the product journey, such as sign-ups, onboarding, and conversion. It helps identify bottlenecks and drop-off points, enabling product managers to optimize the user flow and improve conversion rates.

3. Cohort analysis

Cohort analysis groups users based on specific characteristics or timeframes to understand their behavior and engagement. It helps product managers identify patterns and trends, such as retention rates, feature adoption, and user churn, within specific user segments.

4. A/B testing

A/B testing involves comparing two or more variations of a product or feature to determine which performs better in achieving specific goals. Product managers should have the ability to design experiments, set up control and test groups, analyze results, and draw actionable insights to optimize product performance. Product teams often use A/B testing software, like Optimzely, or custom built solutions, like Amazon's Weblab system. Analysis of A/B test results also requires a rudimentary understanding of statistical significance.

Best data analysis tools for product managers

To effectively analyze and derive insights from data, product managers should leverage suitable data analysis tools. Here are some popular tools used in the industry:

A blazingly fast spreadsheet for big data that connects directly to databases, data lakes, and APIs and can be shared across business teams.

A user analytics platform that enables product managers to track user actions, conduct funnel analysis, and perform cohort analysis through a pivot table UI.

A data visualization tool that allows product managers to create interactive dashboards and visually explore data to uncover trends and patterns.

Software tools that enable a product manager to write SQL queries against a database and extract data into other BI tools or spreadsheets.

A powerful web analytics tool that provides comprehensive data on user behavior, website traffic, and conversion rates with many built in analytics dashboards.

Key takeaways

To excel in product management positions, product managers should develop strong data analysis skills. Key takeaways include:

  • Data analysis skills are essential for product managers to make informed decisions and drive product success.
  • Product managers should be proficient in both quantitative and qualitative data analysis to gain a holistic understanding of user needs.
  • Data integrity and cleanliness are crucial for accurate and reliable insights.
  • Market research, funnel analytics, cohort analysis, and A/B testing are commonly used data analysis techniques in product management.
  • Utilizing the right data analysis tools can enhance the effectiveness of product managers in deriving actionable insights.

By honing their data analysis skills, product managers can leverage the power of data to drive product innovation, improve user experiences, and contribute to the overall success of tech companies. I this blog post we covered a general overview of product management responsibilities, the 4 key areas of data analysis:

Key topics for product management data analysis

  • Data analysis responsibilities for product managers
  • Assess data quality
  • Common quantitative data analysis for product managers
  • Best data analysis tools for product managers