Saying no to stakeholders is easily one of the most talked about responsibilities of a product manager (PM). What’s not talked about as often is how to achieve that.
The answer lies in the words of W. Edwards Deming, "In God we trust, all others must bring data."
Data is the lifeblood of product management and the foundation for successful products. By using data to identify which features and initiatives are most valuable to customers, PMs can make informed decisions about where to focus their resources, and which ideas to say no to.
Assumptions are the termites of relationships, and data help product teams avoid getting sidetracked by their own assumptions and biases and improve relationships with internal stakeholders, as well as their customers.
Data is also the key to measuring progress, understanding customer needs, and making informed decisions about product development. Without data, PMs would be forced to rely on guesswork and intuition, leading to inconsistent and often misguided decision-making.
PMs use data in different ways to improve products at different stages of the product life cycle.
Identifying Customer Needs and Preferences: By leveraging data, PMs can identify the needs and preferences of customers, which in turn enables them to create products that align with customer expectations. This data is usually in the form of surveys (using tools like SurveyMonkey, Qualtrics, and Typeform), focus group sessions, and 1:1 user interviews. Some product management tools like airfocus, allow you to do your discovery work on the platform and include multiple integrations to connect other tools you use, so you have a home for all your product work.
Market Analysis: Market research provides data on the size of the market, the target audience, and their behavior, which helps PMs validate assumptions about their product's potential success and identify opportunities for growth. PMs also use competitive analysis to gain insights into the existing products in the market, what features they offer, and how they are priced. Together, this helps them identify gaps in the market and develop unique value propositions for their product.
During the design stage of a product life cycle, PMs use data to ensure that the product meets the needs of the target audience and achieves the desired outcomes.
Design Research: By utilizing data obtained from design research, PMs can make informed decisions about product design to ensure that it caters to the requirements of the target audience. This data includes user personas, user journeys, and other design-related insights that help PMs develop effective and user-friendly product designs.
Design Testing: Data from user testing is used by PMs to evaluate the usability and effectiveness of the product design. This helps them identify areas for improvement and make changes that optimize the user experience. In addition, they use data from A/B testing tools (Optimizely, AB tasty etc) to compare the performance of different design variations. This data helps PMs determine which design elements are most effective at achieving the desired outcomes, such as increasing conversions or reducing churn.
The extent of a PM’s involvement during the (actual) development stage varies from company to company. However, every PM needs to keep a close eye on the development progress to be able to keep other cross-functional stakeholders informed as well.
Development Reports: Reports from modern software development tools (e.g. JIRA, Trello, Monday, Azure DevOps, etc) include data points like development velocity, resource utilization, and burn rate, which are used to track progress, identify bottlenecks, and address delays. In collaboration with the scrum and engineering teams, PMs use also use data from issue-tracking tools to identify bugs and prioritize fixes, data from CI/CD tools (e.g. Jenkins, Bitbucket, AWS, Google Cloud etc) to track the build and deployment process, and data from code quality tools (e.g. SonarQube) to monitor the quality of the code being produced.
Performance tests: Data from performance tests is analyzed to ensure that the product and the infrastructure meet the production-grade requirements. Tests like penetration tests, stress tests, static code tests, and load tests are used to evaluate the product's quality, security, and performance. The data obtained from these tests is used to identify any possible vulnerabilities, optimize the product's performance, and ensure that it meets the desired quality standards.
After the product launch, the job becomes more challenging as a plethora of new data points needs to be taken into account, adding to the complexity of the task.
Sales data: Sales data provides information on revenue, sales volume, and customer acquisition rates and cost, among other metrics, which can help PMs understand how the product is performing in the market and identify opportunities for growth. These data points are often collected and stored in a CRM like Salesforce, Hubspot, Intercom, etc. With airfocus you can integrate most of these tools, which makes data collection and analysis easier.
User behavior and feature usage data: PMs use data from analytics tools (e.g Google Analytics, Mixpanel, Amplitude) to track user behavior and feature usage, including metrics such as time spent on the product, user retention rates, and feature adoption rates. This data can help them identify trends, user preferences, and areas for product optimization.
Performance monitoring: Production logs from sources like AWS Cloudwatch, Elastic, Kibana, Crashlytics, etc help track the product's uptime, response times, and error rates. It can also help them identify and resolve issues that may affect the user experience.
Support tickets and customer feedback: By analyzing data from support tickets (usually part of the central CRM) and customer feedback (CSAT, NPS), PMs can identify common issues and areas where the product can be improved. In addition to helping prioritize new feature development and bug fixes, this data, in turn, also allows product teams to tweak their customer onboarding process and help documentation. This is where valuable features like airfocus Insights and Portal come in handy, as they allow you to centralize feedback and engage with your users easily.
Despite all the value that comes from collecting and analyzing data, there are several challenges and limitations associated with using data in product management.
Firstly, data isn’t just a collection of numbers, but a story waiting to be told. Collecting and analyzing data can be challenging for PMs, who must ensure that they are collecting relevant and accurate data from the right sources. Instead of getting overwhelmed by the data overload, smart PMs leverage the use of data visualization tools (e.g. PowerBI, Tableau, etc) and set up product dashboards to get a central picture of everything in a single place, without having to spend all that extra time extracting reports manually.
While it is crucial to track all of the data points mentioned above (and many more), data can easily be segregated into tiers of daily, weekly, and monthly review. Additionally, PMs can set smart rules to observe changing trends automatically and generate triggers and alerts for an action. With the rise of AI, it is becoming more and more common to leverage AI technology to generate insights automatically (e.g. using machine learning, regression analysis, clustering, etc).
Secondly, in the words of Albert Einstein, “Not everything that can be counted counts, and not everything that counts can be counted.” Many times, PMs are faced with the risk of overreliance on data. While data can provide valuable insights, it is not a substitute for human intuition and creativity.
There is also an issue of bias in data. Incomplete or biased data can result in erroneous conclusions. It is important for PMs to be aware of this and take steps to mitigate bias in their data analysis.
PMs must balance data-driven insights with human-centered approaches to product development. Other factors such as user experience and product strategy must also be considered to promote data-informed decision-making, instead of data-driven decision-making. By taking a more holistic approach, PMs can make well-rounded decisions that better align with the needs and expectations of their users. Additionally, data-informed decision-making allows PMs to be more flexible and adaptable to changing circumstances, as they are not solely reliant on data.
To overcome these challenges and limitations, PMs must be skilled in data analysis and interpretation. They should ensure that they are collecting both qualitative and quantitative data and use their experience and empathy to interpret and act on the insights provided. PMs should also collaborate with data analysts and subject matter experts to gain a deeper understanding of the data and the context in which it is being used.