The Ultimate Guide to Data-Driven Product Management in 2023
Published at July 03, 2023

Data is key in today's digital era. Companies collect vast amounts of data but need to use it to make informed product development choices through data-driven product management.

According to Mckinsey, data-driven product management resulted in an increase in EBITDA by 15-25%. So, leveraging analytics can assist product managers in making more informed decisions for their products.

Let's discuss what data-driven management is and how to leverage analytics for data-driven product management.

Data-Driven Product Management

What Is Data-Driven Product Management? 

Data-driven product management is the process of using data to inform product development decisions

Product managers may make more informed decisions that lead to better products by leveraging analytics for insights.

This process can help identify customer behaviour trends, guide product development, and keep companies ahead of their competitors.

Benefits of Data-Driven Product Management

Forbes reported that companies utilising data-driven product management can experience an average EBITDA increase of 5%. 

It means that data-driven product management is beneficial for companies. The benefits of data-driven product development include:

  • Allows product managers to make informed decisions
  • Helps companies stay ahead of the competition
  • Increased product satisfaction
  • Improved customer retention
  • Increased revenue
  • Reduced costs
  • Faster time to market

Challenges of Data-Driven Product Management

When it comes to data-driven product management, organisations can face several challenges. Here are a few challenges of data-driven product management:

  • The sheer amount of available data makes it difficult to identify what's important.
  • Data can be misleading due to context and bias
  • Data-driven product management requires expertise in analytics and data interpretation

Leveraging Analytics for Data-Driven Product Management

Leveraging analytics is an important aspect of data-driven product management. It can help product managers make more informed decisions by providing insights into customer behaviour and product performance.

Analytics can also help identify trends and patterns, which can inform product development decisions.

Data Sources

  • User behaviour data: Data can come from websites, apps, and customer support. It shows how users use a product, where they struggle, and how the product can be improved.
  • Customer feedback data: This type of data can be collected through surveys, interviews, and social media. It helps to understand what customers are looking for and what they need, and to identify areas for product improvement.
  • Market research data: This type of data can be collected from various sources, such as industry reports, competitor analysis, and customer surveys. It helps to understand the competitive landscape, identify trends, and assess the potential market for a product.

Types of Analytics

Various types of analytics can be leveraged for data-driven product management, including:

  • Descriptive analytics: Summarises and describes past and present data to provide insights into what has happened.
  • Diagnostic analytics: Investigates past data to determine why something happened.
  • Predictive analytics: Analyses past data to make predictions about future events or behaviours.
  • Prescriptive analytics: Uses past data and predictive models to recommend courses of action for future events or behaviours.

Each type of analytics provides different insights into customer behaviour and product performance.

Challenges of Leveraging Analytics

While leveraging analytics can gain valuable insights for data-driven product management, there are also challenges. Some of these challenges include:


  • Gathering and analysing data from multiple sources: Companies struggle to gather and integrate data from different sources due to varying formats, quality, and availability of data.


    • Ensuring data accuracy: To ensure the accuracy of their data and algorithms, companies face the challenge of verifying them with large datasets. For this reason, it is necessary to establish proper processes.


  • Ensuring data security and privacy: Companies must ensure that the large amounts of data they collect and analyse are stored and processed securely, with robust security and privacy policies in place to protect against unauthorised access and meet data privacy regulations.


Companies must consider the costs of hiring data analysts and investing in technology and infrastructure when implementing and maintaining analytics strategies.

The insights gained from analytics can provide invaluable benefits for product management and a competitive edge in their industry.

Best Practice for Leveraging Analytics

Using analytics involves collecting and analysing data to make informed decisions and drive business growth. To do this effectively, product managers should follow some best practices that will help them achieve their goals. 

Here are a few of the best practices that can be used:


  • Clearly defining business objectives 


Before using analytics, it is essential to define the business objectives. 

This helps to ensure that the analytics are aligned with the overall goals of the organisation. It also helps product managers to identify the metrics that matter most and prioritise their efforts.


  • Identifying relevant data sources


The success of analytics depends on the quality of the data being analysed.

Product managers should find reliable and complete data from internal sources (e.g. sales, customers, and marketing) and external sources (e.g. industry reports and social media).


  • Ensuring data accuracy and consistency


To get reliable and actionable insights from analytics, it is essential to ensure the accuracy and consistency of the data being analysed. 

This can be achieved by using data validation techniques, data cleansing, and data normalisation. It is also important to establish data governance policies and procedures to maintain data quality over time.


  • Using appropriate analytical methods


Various analytical methods can be used to analyse data, such as descriptive analytics, predictive analytics, and prescriptive analytics. 

Product managers should use the appropriate analytical methods that best suit their business objectives. They should also consider the level of expertise required to use these methods and the resources available.


  • Communicating insights effectively


Analytics can provide valuable insights that can drive business growth.

However, it is essential to communicate these insights effectively to stakeholders. This includes using clear and concise language, visualising data, and presenting insights in a way that is relevant to the audience.

By following these best practices, product managers can effectively leverage analytics to make data-driven decisions and drive business growth.


Data-driven product management helps you for making informed decisions, identify trends, and stay ahead of the competition.

Leveraging analytics is critical, and while there are challenges, following best practices can help product managers overcome them and gain valuable insights for product development.

VirtualSpirit has skilled developers available to assist in implementing your data-driven products. Get in touch with us!

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