Product data management: tips, tools, and best practices

Traditionally, product data management (PDM) is used in manufacturing, mechanical engineering, construction, and even buildings, when creating physical products. It began as a way to transform the chaos of constantly changing design files and revisions into a single source of truth for all product information, fuelling faster, more efficient production cycles. 

For digital products like software and apps, product data management looks a little different—but it’s no less important. PDM for digital products combines key usage, performance, and behavioral data to give you a complete understanding of how real users interact with your app or website.

Here’s why effective product data management is the foundation for improving product quality, enhancing customer experience (CX), and building long-term user loyalty—and what you need to do to get started.

Key insights

• Product data management (PDM) is the process of collecting, organizing, and analyzing data related to a specific product

• Managing product data and integrating it with other datasets—like data from your digital experience and CRM platforms—is crucial to understanding performance and identifying new opportunities. Connected product data can be used to enhance user experiences (UX), prevent churn, uncover new use cases, and streamline product development.

• To get more from your product data, empower all stakeholders (like product teams, marketing and UX teams, and execs) to self-serve and get the right level of insights they need to guide decision-making

Why product data management is important for customer-centric teams

PDM for digital products consolidates multiple datasets to create a single source of truth. Combine data about your product from sources like

• Product analytics tools that capture how users interact with your product from beginning to end (including user and session information, events, clicks, rage clicks, and metadata)  

• Behavioral tools such as heatmaps, session replays, and customer journey analysis that reveal engagement patterns, common pathways, and user friction

• Customer relationship management (CRM) tools that collect organizational information (like industry and company size) and transaction history (like subscriptions, renewals, and upgrades) throughout the customer lifecycle

• Customer support software that logs queries and tickets from users to uncover concerns or new feature requests

By bringing all of this product information together into your data warehouse, you create a unified ecosystem. This lets you break down data silos, find improvement opportunities, and make data-driven decisions that enhance the product experience and increase stickiness.

Here are 4 ways digital teams use product data management to improve their products and meet key business goals.

1. Understand and improve onboarding

Analyze product data to track how different user segments navigate the onboarding process. Conduct journey analysis to discover common paths, including where they get stuck and drop off, then create onboarding flows that guide them through these blockers and address their needs.

Next, combine product usage data with CRM data to understand the traits and behaviors of long-term, successful, and high-value customers. Then, work backward with tailored outreach that encourages more users to take similar paths.

💡 Pro tip: use Contentsquare’s AI-powered Session Replay Summaries to analyze hours of product data and instantly discover how different segments engage with your product during onboarding and beyond. 

This feature summarizes one or multiple session replays to quickly get key insights, identify potential issues, and spot trends. Drill down by segment, location, device, or filter by Contentsquare’s Frustration Score to pinpoint sessions where users experienced friction. It lets you quickly jump to specific time-stamped moments to watch exactly what happened, and learn what users did before and after they took certain actions (like bouncing or making a purchase) to further contextualize their behavior.

2. Fix and prevent churn

Export product analytics, customer support, and behavioral data into your warehouse, then use AI and machine learning (ML) to build predictive user models that identify early signs of churn. Spot warning signals that build into bigger problems, like rage clicks, repeated emails to your support team, or abandoned in-app flows.

Train models on these historical churn events to monitor (and address) dissatisfaction in real time—before it leads to revenue loss.  

💡 Pro tip: use data enrichment and integration tools to improve the quality of your data and create a more complete dataset in your warehouse. With our Data Connect capability, you can easily export rich insights from Contentsquare to your warehouse of choice, where it lands structured, clean, and ready for analysis.

3. Uncover new use cases and prioritize your product roadmap

Explore connected product data to learn more about your users and find trends, like emerging markets or unexpected use cases. For example, if your product usage and CRM data reveals a spike in users from a particular industry, use session replays, heatmaps, and product analytics data to understand how they engage with specific features. 

Use these insights to fuel marketing campaigns aimed at attracting more of these users, guide new product development based on common needs, or prioritize feature or product launches based on your most impactful segments.

💡 Pro tip: ask Sense Analyst to run deep, multi-step analyses and get suggested next steps. Give it detailed prompts like, “What unexpected paths are high-value users taking that we should optimize?” or “Analyze feature adoption across segments and recommend which improvements to prioritize based on potential revenue impact.” Sense analyzes your Contentsquare data and generates actionable summaries that show you exactly what’s going on, how it reached those conclusions, and what you should do next.

4. Increase retention and revenue growth

Use comprehensive product data to understand user pain points, such as poor UX (like broken links, software bugs, or no mobile optimization) or process issues (like confusing navigation or looping journeys), and quantify how they impact revenue. Address the most pressing issues to increase engagement, accelerate time to value, and create loyal, long-term customers—ultimately increasing annual recurring revenue (ARR) and customer lifetime value (LTV).

💡 Pro tip: use Impact Quantification to connect good (and bad) product experiences to tangible business outcomes. See how issues, events, and actions in your product impact goals like conversions and revenue. Compare behavior across user segments to discover which improvements or fixes will make the biggest difference to your product—and your business. Align teams around the most important tasks to effectively prioritize your resources for maximum ROI.  

How to choose the right product data management tools

Traditional product data management software focuses on data required for physical products. Classic PDM solutions consolidate all product-related data, like bills of materials, product specifications, supply chain information, details of manufacturing processes, change orders, revisions and version controls, engineering change histories, and design data such as computer-aided design files from CAD tools. 

Digital products don’t require the same inputs, but the underlying principles are the same. You may not need a dedicated, all-in-one ‘PDM system’ (like SOLIDWORKS or Autodesk), but your PDM software should still capture everything you need to streamline workflows, accelerate time-to-market, and improve product lifecycle management (PLM).

When building your custom PDM tech stack, look for data management tools that

• Support data governance with traceability, customizable permissions, and role-based access control

• Maintain privacy and meet regulatory requirements to comply with relevant legislation (such as GDPR or CCPA)

• Capture real-time product data to ensure you’re working from up-to-date information and can act quickly to address issues as they arise

• Collect quantitative and qualitative data that go beyond numbers alone to show you not just what users do, but why they do it

• Transform raw data into insights using AI-powered capabilities and copilots that follow best practices to deliver reliable analyses

• Integrate with other tools and systems like your organization’s enterprise resource planning (ERP), CRM tools, or business communication software to create connected workflows that make your team more efficient 

How does Contentsquare measure up? Contentsquare meets all of the above requirements, enabling strong data governance, privacy, integrations, and comprehensive quant and qual data collection, all supported by a powerful AI layer for complex analysis.

3 best practices for product data management

Some of the main challenges when implementing PDM are

• Siloed or fragmented data: information about your product lives in multiple places, across disparate functions within your organization, making it difficult to consolidate everything for connected insights

• Ensuring data quality and consistency: data from different sources may follow different formatting or have gaps or duplications across systems, limiting its effectiveness and usability

• Scalability: your product data management system should be able to adapt to your needs as they evolve, including adding new data sources, workflows, and teams

Here are 3 best practices to overcome these issues and help you implement your PDM strategy more effectively.

1. Define your data needs

At the outset, map out the various inputs you need throughout the product lifecycle. This can include feedback from users when you conduct tests during the product development process, to ongoing usage and engagement metrics, to customer support tickets.

Then, outline where all this data lives and create a data integration strategy to bring it all together. 

💡 Pro tip: Contentsquare’s Smart Capture provides a complete retroactive dataset automatically—no complicated tagging or setup necessarily. Capture data you didn’t know you needed and have all the information at the ready  for in-depth insights, even as goals change.

2. Streamline processes with automation

Manually wrangling and updating data is a recipe for disaster, leading to human errors, gaps in coverage, and other mistakes that threaten the integrity of your data. 

Optimize your workflows by using automation to improve data processing. AI and automation handle time-consuming tasks like deduplication, validation, and standardization, enforcing best practices and improving data quality without draining your team’s time or requiring analyst expertise.

3. Empower everybody to access and use product data

To scale your product data management across the organization, democratize your data. Enable everyone—from analysts to non-technical teams like marketing, customer success, and sales—to access, analyze, and make informed decisions based on product data so they can enhance marketing campaigns, prioritize UX changes, and empathize with users.

💡 Pro tip: dashboards are a great way to align cross-functional teams around one central view. Use Dashboards in Contentsquare to quickly track key metrics related to your product—like rage clicks, bounces, average scroll depth, A/B test results, and behavior across segments—to monitor frustration, engagement, and optimization opportunities. 

Start from a pre-made template for instant setup or create your own custom dashboard based on your goals, then share with relevant stakeholders to keep everyone on the same page.

Streamline product data management to unlock new insights and opportunities 

Having a single source of truth for all data about your digital product or app is extremely powerful. Unifying previously siloed data points provides a more comprehensive understanding of how different segments use your product, what they love, and where they’re getting stuck, as well as how this impacts key business goals. Equipped with these insights, you can double down on the experiences, features, and improvements that drive satisfaction, success, and long-term customer loyalty—and make a real impact for your business.

FAQs about product data management