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Six pieces of data for product managers to avoid shiny object syndrome


Jim Barksdale Quote

The challenges of product management are numerous. Product managers often get pulled in multiple directions from sales, support or the executive team, each with their own input into setting priorities. A product manager must work to keep these groups aligned and often say "no" to specific items. Frequent meetings and a set process for identifying initiatives are key to this alignment, but you must also encourage each group to make data-driven decisions.

Product management teams are in a great position to lead this data-driven mindset and should make it a top priority to ensure reliable sources of information are available. Without this knowledge, teams will only be bringing opinion or anecdotal information. High quality metrics will allow teams to set expectations and reasonable forecast within the company and are also a starting point for working with other departments on priorities. While data collection could be done ad-hoc before a decision is made or annually to inform the product roadmap, it should ideally be done continuously through the year. This will ensure updated information, allows for improvements in the data collection process and keeps the product manager closer to the market and business needs of the organization. Data collection should include both quantitative metrics and qualitative research. Six areas that product managers should focus on are:

  1. Top line financials (Review with management)

Examples: New bookings, overall revenue, average selling price, gross margin

  1. Product usage (Review with product team)

Examples: Number of users, feature adoption, transactions or time using products

  1. Operations(Review with Customer Success/Operations)

Examples: Customer satisfaction/NPS scores and comments, support case reviews, cancellations, services time

  1. Sales (Review with Sales Team)

Examples: Win/loss interview information, voice of the customer

  1. Development (Review with engineering team)

Examples: Total bugs or issues, new features released, velocity

  1. Market information (Review with marketing)

Examples: Competitor feature analysis, market trends, market share, customer interviews

The specifics of the data to be collected in each area should be agreed upon by the product team and the functional group. With each of these items, the product manager should review the information with the functional group and work together to understand how this affects the product vision and project priorities. By analyzing and reviewing this information frequently, the product manager ensures up to date information and provides a touch point for interdepartmental communication. As these begin to trend in an unexpected direction, they should be analyzed during these review meetings for root causes and for possible new initiatives.

The goal of the product team should be to automate most of this collection such that both the collection and analysis accounts for just 15%-25% of the product manager's time in a given month. This data collection should be a fixed requirement each month and the product management organization should be held accountable for collecting this information by posting updates in a monthly report. This data collection is also a great tool for on-boarding new product managers by allowing them to focus on product and market facts before delving into any strategic projects. For new product managers, the data collection and analysis could take 100% of their time for the first few months before they become practiced. Once they are able to reduce the time spent with data collection, they will have the knowledge necessary to begin picking up initiatives.

Data is essential for making decisions, but a product manager can get bogged down in the data and lose sight of the big picture for the product. By setting up a process and a fixed amount of time for data collection a product manager can build alignment, have data available for decisions and still have time to maintain the holistic product vision.

While this data may be part of key initiatives, it should tie back to the business model and overall company objectives more than to specific initiatives. Initiatives should have specific metrics that are also tracked to ensure progress, but those metrics may not be maintained once the initiative is complete. The data collected here should be maintained and used for historical purposes. For more information on setting product objectives and initiatives, refer to the balanced scorecard approach for product managers.