🌟 How to Develop a Winning Product Strategy

Welcome to a special Monday subscriber-only edition. Today, we're offering a little thank you for being a part of our community.

July 01, 2024 • Estimated Reading Time: 11 minutes

Welcome, tech friends

Welcome to a special Monday subscriber-only edition.

Today, we're offering a little thank you for being a part of our community.

Let's dive in! 👇

🌟 SPECIAL EDITION

A little thank you to the community

Drive growth and unlock revenue with a winning product strategy

How to Develop a Winning Product Strategy

One of the most frequent questions I get is how to prioritize features to create a winning product strategy. 

The challenge lies in translating a long-term vision into actionable steps that align with both market needs and business goals. 

In this guide, I'll walk you through the essential phases of delivering a product strategy, from observation to action, and share some critical strategic questions to consider along the way. 

Whether at the beginning of your journey or refining an existing strategy, these insights will help you create a way forward that drives growth and unlocks revenue.

And, of course, I’m here as a Fractional Leader if you need a hands-on approach.

📖 Definitions

  • Vision: aspirational and long-term, describing where you want to see yourself in the future.

  • Strategy: a set of choices that define where to play and how to win.

  • Product Roadmap: the first prototype of your strategy.

🏁 5 Phases of Strategy Development

Product strategy process

Here are the 5 phases of delivering the product strategy:

1. Observe

Begin by gathering information about your environment. In a new business context, this could mean understanding market conditions, customer needs, or internal issues. The key is to collect as much relevant data as possible to form a comprehensive view of the situation.

2. Orient

Analyze the information collected to make sense of it. This involves synthesizing data from various sources and understanding how different elements interconnect. Develop a model for how everything hangs together, then socialize and validate it (if you can).

3. Socialize

Without strategic alignment your efforts will fail. This step involves socializing data, stories, and context to key stakeholders to build coalition and create momentum.

4. Decide

Based on your analysis, make informed decisions about next steps. The aim is to choose the best possible action quickly, even in the face of uncertainty. Game out other options (if you can).

5. Act

Implement your decision and take action. Acting swiftly and decisively will help your teams carry momentum forward.

🕹️ Where to Begin

Before you start the observation phase, ensure you have a clear understanding of:

  • Your vision

  • Your "Why"

  • Your ideal customer

  • Your unique selling point

  • How you will win

If you haven’t workshopped these elements, take a half-day to flesh them out before moving forward.

♟️ Strategic Questions

  1. What possible futures may or may not play out for us over the next few years?

  2. Are there emerging trends relevant to our industry?

  3. Are there changes happening right now (even at a small scale) that will influence our future?

  4. How can we tap into uncertainty and gain insights into potential outcomes?

  5. What can we do today to prepare for tomorrow?

  6. How can we ensure our organization is ready for a range of possible, probable, and preferred futures?

  7. What are some potential disruptions that could impact our business?

  8. What if we take a completely different approach to our business? What would that look like?

  9. What "wildcard" scenarios could play out, and how could we prepare for them?

  10. How can we ensure our strategies remain resilient in the face of unexpected change?

🧮 Observing and Gathering Data 

The earlier you are in the 0:1 journey, the more you will rely on qualitative and third-party data. The later the stage, the more likely you are to have access to performance analytics, user behaviors, and statistically significant experimentation. 

Product data types

Quantitative Data

Activation Metrics

Activation Rate: The percentage of users who complete key onboarding steps or reach critical milestones. Example: 30% of new users completing onboarding.

Time-to-Value (TTV): The time it takes for a new user to experience the product’s core value. Example: Average TTV is 3 days.

Engagement Metrics

Daily Active Users (DAU): The number of unique users who engage with the product daily. Example: 5,000 DAUs.

Monthly Active Users (MAU): The number of unique users who engage with the product monthly. Example: 50,000 MAUs.

Stickiness Score (DAU/MAU): The ratio of Daily Active Users to Monthly Active Users, indicating how often users engage with the product. Example: Stickiness score of 0.3 (30%).

Retention Metrics

Retention Rate: The percentage of users who continue to use the product over a given period post-activation. Example: 60% retention after 1 month.

Churn Rate: The percentage of users who stop using the product over a specific period. Example: Monthly churn rate of 5%.

Monetization Metrics

Customer Lifetime Value (CLV): The estimated total revenue generated from a user over their entire relationship with the product. Example: CLV is $500.

Customer Acquisition Cost (CAC): The cost of acquiring a new customer, including marketing and sales expenses. Example: CAC of $150.

Monthly Recurring Revenue (MRR): The total revenue from monthly subscriptions. Example: MRR of $20,000.

Conversion Metrics

Trial Conversion Rate: The percentage of users who convert from a free trial to a paid subscription. Example: 25% trial-to-paid conversion rate.

Lead Conversion Rate: The percentage of leads that convert into paying customers. Example: 15% lead-to-customer conversion rate.

Product Usage Metrics

Feature Adoption Rate: The percentage of users who engage with specific product features. Example: 40% of users use the collaboration feature.

Feature Usage Frequency: This track how often specific features are used by active users. Example: The collaboration feature is used five times a week.

Revenue Metrics

Revenue Per User (RPU): Average revenue generated per active user. Example: RPU of $50 per month.

Net Dollar Retention (NDR): Revenue from existing customers through upsells, cross-sells, or add-ons. Example: 114% Net Dollar Retention

Efficiency Metrics

Onboarding Completion Rate: The percentage of users who complete the onboarding process. Example: 75% onboarding completion.

Payback Period: The time it takes to recoup the customer acquisition cost. Example: 6 months.

Qualitative Data

User Feedback:

  • Surveys: User satisfaction surveys, NPS surveys, etc.

  • Interviews: Direct interviews with users to gather in-depth insights.

  • Focus Groups: Group discussions to understand user perceptions and experiences.

  • Usability Testing: Observing users as they interact with the product to identify pain points.

  • User Reviews: Feedback from app stores, review sites, and social media.

Behavioral Data:

  • Session Recordings: Video recordings of user sessions to see how users navigate the product.

  • Heatmaps: Visual representations of where users click, scroll, and spend the most time.

  • Journey Mapping: Detailed mapping of the user journey to identify critical touchpoints and pain points.

Support Data:

  • Support Tickets: Issues reported by users through customer support channels.

  • Chat Logs: Transcripts from live chat interactions.

Market Research:

  • Competitor Analysis: Insights into how competing products are performing and what features they offer.

  • Industry Reports: Data and trends relevant to the industry.

Mixed Methods

  • A/B Testing: Comparing two versions of a feature to see which performs better.

  • Cohort Analysis: Grouping users based on shared characteristics to analyze retention and engagement over time.

  • Performance Analytics: Detailed analytics on how users interact with various features.

Synthesizing Data

  • Use AI tools like OpenAI’s ChatGPT to analyze qualitative interview transcripts and extract key themes. Then correlate these themes with survey results to identify patterns.

  • Apply thematic analysis to identify recurring themes in your qualitative data. This involves coding the data and grouping similar codes into themes.

  • Validate findings using multiple data sources. For instance, create detailed user personas by combining qualitative interview insights with quantitative demographic data.

  • Use GenAI for sentiment analysis to gauge user emotions and opinions from qualitative feedback.

  • Use visualization tools to combine and present qualitative and quantitative data effectively. Tools like Tableau can integrate both data types into comprehensive dashboards.

🗓️ Now, Next, Later

The "Now, Next, Later" format is highly effective for presenting product roadmaps because it provides clarity, flexibility, and focus, ensuring stakeholders can easily understand and engage with the product development plan.

Now, Next, Later Roadmap

Reasons to use Now, Next, Later:

  1. Clarity and Simplicity: Clear prioritization and simplified communication.

  2. Focus on Current Work: Immediate focus and efficient resource allocation.

  3. Flexibility and Adaptability: Responsive to change and continuous improvement.

  4. Strategic Vision: Long-term planning and balanced approach.

  5. Stakeholder Engagement: Enhanced transparency and managed expectations.

  6. Prioritization and Decision-Making: Informed decisions and focus on value.

  7. Ease of Use: User-friendly and visually appealing format.

🗣️ Socializing Alignment

Here are some quick ideas on building a coalition for your strategy:

Top Down Guidance

  • Allocate resources to run an effective product strategy process.

  • Push the C-suite to allocate resources at a high level across broad categories.

  • Have every exec-level stakeholder provide a short, fully ordered list of their group’s needs.

Bottoms Up Approach

  • Push alignment on metrics (Northstar metric, strategic drivers, and constellation of metrics).

  • Use weighted scoring models for a structured, data-driven approach.

  • Leverage customer interviews as storytelling artifacts and media.

🚅 Data-Driven Decisions

Connect on-the-ground product motions with top-level metrics using the North Star Framework. Assign weights to prioritize features using DHM, RICE, and CBC models.

Product Feature Scoring

Weighted Scoring Models: Assign weights to different types of data based on their importance and calculate scores to prioritize features.

  • DHM: Delight, Hard to copy, and Margin-enhancing

  • RICE: Reach, Impact, Confidence, and Effort

  • CBC: Committed by Sales, Buzzworthy, and CEO Favorite

If you want to learn more about these weighted models or need specific examples, please email me at [email protected].

🎯 Taking Action

Congrats! If you've followed these steps, you have a comprehensive product strategy that translates your vision into customer experiences, driving growth and unlocking revenue.

⚡ Let's Collaborate

Based in San Francisco but working globally. Let’s connect and discuss how we can take your product team to the next level.

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