How to manage product backlog with data-driven techniques

As a product manager, effective backlog management is essential to the success of your product development process. One key factor in achieving this is utilizing data-driven techniques to inform decision-making.

By collecting and analyzing product data, you can prioritize features based on their impact on your product's success and user needs. Plus, organizing and refining your backlog based on data insights can help you create a roadmap that aligns with your product goals.

While backlog management is crucial for product managers, it is also vital for cross-functional teams involved in product development. Prioritizing features and tracking progress leads all team members to work towards a common goal and ensure the product’s success.

Let’s now explore how to manage your backlog using product data and provide tips and best practices to implement in your workflow.

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Product backlog definition

In product development, a backlog is a prioritized list of tasks, features, and enhancements that must be completed to achieve a specific goal. The backlog constantly evolves as new tasks are added and completed and priorities change.

Effective backlog management involves regularly reviewing and prioritizing tasks based on their impact on the product and user needs. By properly managing the backlog, product teams can ensure they effectively deliver the most valuable features to their users.

Ultimately, backlog management is an essential part of agile software development that ensures the team is focused on delivering the most valuable work items first. Effective backlog management helps improve team productivity and ensures the team delivers high-quality software on time.

Approaches utilized by experienced product managers

Azer Aliev

Product Manager at Google

The practices I use for prioritizing items in a product backlog are HIPPO (Highest Paid Person's Opinion) and WSJF (Weighted shortest job first).

HIPPOs possess a profound understanding of the company's strategic objectives, financial constraints, and overall vision, making their insights valuable inputs in product management. Product managers can effectively incorporate the HIPPO's perspective into the decision-making process to ensure that product development aligns with the company's objectives and that the product roadmap supports the business's long-term goals.

However, it's crucial to balance the input from the HIPPO with data-driven insights and user feedback to arrive at the best possible outcome for both the product and the company.

In his Medium article titled "WSJF — Product Prioritization Framework," Azer stated, "After thorough deliberation, we have opted to adopt a modified version of the 'Cost of Delay and Weighted Shortest Job First [WSJF]' as the fundamental approach for Everphone Prioritization."

WSJF Score = Cost Of Delay / Job Size

Cost of Delay = Business Value + Time Criticality + (Risk Reduction OR Opportunity Enablement)

Importance of product backlog management

Effective backlog management is essential for successful product development. Here are some reasons why:

• The backlog helps to prioritize tasks and ensure that the most important features are developed first;
• Ensures that the product development process is aligned with the overall product goals and vision;
• It provides transparency and supports collaboration between cross-functional teams;
• Backlog management allows progress to be tracked and helps to quickly identify and resolve issues;
• Effective backlog management helps to reduce waste by ensuring that only the most valuable features are developed;
• The backlog can be adapted to changing user needs or market conditions, allowing the product to remain relevant and competitive;
• Prioritizing tasks and features based on user needs and impact on product success results in higher-quality products.

Who owns the product backlog?

As a rule, the product backlog is owned and managed by the product owner, who is responsible for defining and prioritizing its components, but it is crucial for all members of the product development team to contribute to it and understand how it relates to the overall product strategy.

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Collecting product data

Let’s discuss the importance of collecting relevant product data and tools to aid in data collection.

Types of product data

Product data can come in various forms, such as user feedback, analytics data, customer service tickets, market research, and competitive analysis. These types of data are used to provide insights into user behavior, product performance, market trends, and the competitive landscape.

Importance of data collection

Data collection is pretty much necessary because it enables product teams to make informed decisions based on insights and trends rather than assumptions or guesswork. Analyzing that data afterward helps identify user needs, improve your product performance, and remain competitive in your market or niche.

Tools for data collection

There are various tools available for collecting product data, including:

• Analytics platforms to collect and analyze website/app usage data
• User feedback tools to gather feedback from users via surveys, reviews, etc.
• Customer service software to manage and track customer support inquiries
• Market research services to collect and analyze industry and market trends

Analyzing product data

Now, how do you analyze product data to gain insights into user behavior, product performance, and market trends?

Identifying patterns and trends

Identifying patterns and trends in product data involves analyzing large data sets to identify correlations, trends, and anomalies. This process typically involves using statistical methods and data visualization tools to identify patterns that may not be immediately obvious.

Some statistical techniques you can use to identify patterns and trends in product data include regression analysis, time-series analysis, cluster analysis, and hypothesis testing. These methods help identify relationships and correlations between different variables in the data.

Data visualization tools such as charts, graphs, and heatmaps are also used to visually represent the data and identify trends, patterns, and areas of opportunity or concern helpful for later backlog prioritization. For example, you can analyze your data using Google Analytics or Mixpanel.

Prioritizing features based on data analysis

This step involves reviewing the insights gained from analyzing product data and using this information to make informed decisions about which features to prioritize in the product backlog.

Your product teams can use previously gathered data (see: Types of product data) to determine which features are most important to users, have the most significant impact on product success, and align with the product goals and vision, and then assign priority to them and determine which ones to include in the backlog.

Collaborating with stakeholders based on data insights

Now, you need to share your findings from analyzing product data with key stakeholders, such as executives, developers, and designers, and using this information to inform decision-making. Explain the rationale behind the future changes and get buy-in from the team.

You need to present the data insights in a clear and concise way, then try to facilitate discussions and brainstorming sessions to identify opportunities for improvement. Working together to define and prioritize product goals and initiatives is key!

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Organizing and prioritizing the backlog

The product backlog prioritization is next.

Categorizing features based on priority

The next phase involves sorting product backlog items into categories that reflect their relative importance to the product vision and goals. This process typically involves using data-driven insights, user feedback, and stakeholder input to prioritize features based on user impact, market demand, technical feasibility, and business value.

Here are the techniques and methods you can use for product backlog prioritization.

The MoSCoW method (unfortunate name, btw) categorizes backlog items into four groups - Must-haves, Should-haves, Could-haves, and Won't-haves - based on each item’s importance to the product vision and goals.

Must-haves are essential features that must be delivered to meet the product's objectives, while Should-haves and Could-haves are important but not critical features that can be delivered in later iterations. Won't-haves are features that are not necessary or are out of scope for the current product iteration.

The Kano model, on the other hand, categorizes features into five groups:

1. Must-haves or basic needs - the features that customers expect to be present in a product or service, and they are taken for granted. If they are not present, customers will be dissatisfied, but their presence doesn't necessarily lead to satisfaction.
2. Performance needs are the features that customers explicitly request, directly impacting their satisfaction with a product or service. Customers are aware of them and will evaluate a product based on how well they are delivered.
3. Delighters or exciters - the features that exceed customer expectations and create a positive surprise. They generate high customer satisfaction and can differentiate a product or service from its competitors.
4. Indifferent. They do not significantly affect customer satisfaction either positively or negatively. Customers don't care about these features, and their presence or absence has little impact on customer satisfaction.
5. Reverse - the features that, if included in a product or service, would actually decrease customer satisfaction. They may be perceived as unnecessary or annoying and should be avoided.

Another helpful tool is The Eisenhower matrix. It involves dividing your items into four categories: urgent and important, important but not urgent, urgent but not important, and neither urgent nor important. Doing so lets you determine which items should be prioritized and which can be deprioritized or delegated.

Other methods include:

• RICE - a framework that factors in Reach, Impact, Confidence, and Effort to prioritize features.
• Value vs. effort - A simple framework that compares the value a feature will provide to users or the business against the effort required to build it.
• Weighted scoring - this method assigns weights to factors like user value, effort, and impact and then scores features based on these factors to determine priority.
• Cost of delay - this method quantifies the financial impact of delaying the delivery of a feature and uses this information to prioritize the backlog.
• Story mapping involves mapping out the user journey and using this information to prioritize features based on their impact on the user experience.

Using data to determine the urgency of features

Next is using your data to determine the urgency of features. For that, analyze customer feedback, market trends, and other relevant data to identify which features are most important and needed urgently.

For that, define your metrics. They should be specific, measurable, and actionable. Examples of metrics include user engagement, conversion rates, retention rates, and customer satisfaction scores.

Overall, product managers prioritizing their product backlog ensures that the most critical features are addressed first. This way, you can continually adjust your priorities and ensure you are delivering the most valuable features to your customers.

Creating a roadmap based on data-driven prioritization

To create a roadmap based on data-driven prioritization, product teams need to carefully analyze customer feedback, market trends, and other pertinent data to identify crucial features and initiatives.

This analysis requires a thorough evaluation of the potential impact, feasibility, and effort required for each feature or initiative.

Once this analysis is complete, your team can prioritize the features and initiatives based on their importance and create a timeline that outlines the key milestones and deliverables for each quarter or year.

Implementing and tracking changes

Time to implement those changes.

Implementing backlog changes based on data insights

As a product manager, you need to have a deep understanding of your product and its users, as well as the data that is available to you. Several methods for implementing changes include agile methodologies such as Scrum or Kanban, continuous delivery, and DevOps.

These methods involve breaking down work into small, manageable chunks, prioritizing based on customer feedback and data insights, testing and iterating frequently, and collaborating closely with stakeholders. Your primary goal is to deliver value to customers quickly and efficiently while continuously improving the product.

Tracking progress and measuring success

Once changes have been implemented, test them to see how they impact your metrics. Set targets for each metric based on historical data or industry benchmarks, and then regularly review progress against the targets using tools such as dashboards or spreadsheets. Identify areas where you are falling short and investigate the reasons why.

It’s also helpful to use A/B testing or other methods to compare different versions of your product and determine which is more effective. Analyze the results to gain insights into user behavior and product performance. Look for patterns and trends in the data that can help you identify areas for improvement.

Finally, it’s time to iterate and adjust. Use the insights you gained to adjust your backlog prioritization and management. Test and iterate based on your findings, making further changes as needed while continuously refining your backlog based on data feedback

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More tips for successful product backlog management

Here are a few more tips and tricks that will help you with your product backlog:

• It may be helpful to keep the backlog under a certain size and timeframe. The backlog should be kept under 150 items long and under six months of work for the team(s). A too-long backlog can lead to demotivation and inefficiency and increase the risk of essential items getting lost among other items.
• It is also important to use size management tips, such as auto-killing old items, active decision-making and communication, designing in-progress limits, and having a clear strategy and process for backlog management.
• Other good practices for effective backlog management include never allowing one-liner requests, communicating the full backlog transparently to all requesters, and using prototypes to verify what is planned to deliver.
• Regular refinements of the backlog should be done to maintain 1.5-2x velocity of the backlog in a refined state. The refinement should include having a status, label, or similar to indicate refined items, a definition of ready written and agreed with the team, spending monthly time on scanning the full backlog and doing spikes or feasibility studies for items lower down on the backlog or upcoming unclear large items on the funnel.
• Funnel visualization methods can be used to organize and prioritize the committed backlog into columns of "Now, Next, Later," and keep the wishlist items separate from the committed backlog. Other methods for dividing and conquering backlog include using epics, mother features, categories/classes, themes, components, and technical debt.
• The story mapping technique can be used to find items to organize your backlog under. It involves creating a user journey and organizing the backlog based on targeted releases, user journeys, and acceptance criteria.
• Finally, it is important to define criteria that are significant to the product and use them to grade each backlog feature. These criteria should include potential revenues, market fit, market uniqueness, and technical feasibility.

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Conclusion

In conclusion, data-driven product backlog management is crucial for any successful software development project. By using data to prioritize tasks, your team can ensure they are delivering the features and functionalities that will provide the most value to users.

To recap, if you want to effectively manage a backlog, you need to regularly collect and analyze data, involve stakeholders in the decision-making process, and keep the backlog organized and up-to-date.

Remember to prioritize tasks based on their value and impact and continuously re-evaluate and refine the backlog as new information becomes available. By following our tips, your team can optimize their backlog management and achieve their project goals in no time!