Lean Analytics

Identify Your Current Stage: Understand where your business or project currently stands. Are you in the early stages, focusing on user acquisition? Or are you optimizing for revenue? Identifying your stage will help you determine your OMTM.

Choose Your OMTM Wisely: Reflect on what metric, if improved, would have the most significant impact on your business right now. This could be user engagement, conversion rate, average order value, etc. Ensure it's measurable and directly influences your success.

Communicate Your OMTM to Your Team: Make sure everyone involved understands what the current OMTM is, why it's important, and how their work contributes to improving it. This alignment is crucial for collective effort and focus.

Regularly Review and Adjust Your OMTM: Set a regular schedule to review your OMTM. As your business evolves, so too will your key metric. Be prepared to shift your focus as needed to adapt to new challenges or opportunities.

For a new e-commerce website, the OMTM might be the conversion rate—the percentage of visitors who make a purchase. Focusing on optimizing the website design, simplifying the checkout process, or improving product descriptions could be strategies to improve this metric.

A mobile app in its early stages might focus on user retention as its OMTM. Strategies could include enhancing user onboarding, adding new features based on user feedback, or improving app performance to keep users engaged and reduce churn.

Start Small: Begin by identifying the core feature or value proposition of your product. This is what you'll build as your MVP. Don't worry about making it perfect; focus on the essential functionality that solves a problem for your users.

Measure Effectively: Once your MVP is live, track how users interact with it. Use tools like Google Analytics for website interactions or in-app analytics for mobile applications. Pay attention to metrics that directly reflect the value your product offers, such as user engagement or conversion rates.

Learn and Iterate: Analyze the data you've collected to understand what's working and what's not. Look for patterns in user behavior that indicate satisfaction or points of friction. Use this insight to make informed decisions about what features to add, remove, or improve.

Repeat the Process: After making adjustments based on your learnings, go through the cycle again. Build updates or new features, measure their impact, learn from the results, and iterate. This ongoing process will help you refine your product and better meet your users' needs.

A startup launches a new app aimed at helping people track their daily water intake. Initially, they focus on the core feature: logging water consumption. After release, they notice that users frequently exit the app after logging their first drink. They hypothesize that users might be looking for more engagement or reminders. In the next iteration, they introduce hydration reminders throughout the day, which leads to increased user retention and engagement.

An online retailer introduces a recommendation engine to suggest products based on browsing history. Initially, the recommendations are based solely on the last item viewed. The retailer measures the click-through rate on recommended products and finds it lower than expected. They learn that users are more likely to click on recommendations that consider their entire browsing history, not just the last viewed item. The retailer then updates the recommendation algorithm to account for this, resulting in higher engagement and sales.

Identify Your Analytics Needs: Start by assessing which type of analytics will benefit your current situation the most. Are you trying to understand past performance, predict future trends, diagnose a problem, or decide on the next steps? This will guide you in choosing between descriptive, diagnostic, predictive, or prescriptive analytics.

Collect Relevant Data: Ensure you're collecting the right data for the type of analytics you need. For descriptive analytics, historical data is key. For diagnostic, you need data that shows correlations. Predictive analytics requires patterns over time, and prescriptive analytics needs data on potential outcomes of different decisions.

Use the Right Tools: There are various tools available for different types of analytics. Google Analytics is great for descriptive analytics, while tools like Tableau can help with diagnostic insights. For predictive analytics, consider machine learning platforms, and for prescriptive analytics, decision management software might be useful.

Apply Insights to Decision Making: Once you have your analytics, use them to make informed decisions. If your predictive analytics suggest a future trend, plan your strategy accordingly. If prescriptive analytics offers a course of action, evaluate its feasibility for your business.

A retail store uses descriptive analytics to review last quarter's sales data, identifying which products sold the most. They then apply diagnostic analytics to understand why these products were popular, perhaps due to a successful marketing campaign or seasonal demand.

An online service provider uses predictive analytics to forecast user growth in the next six months based on current trends. They then employ prescriptive analytics to outline specific marketing strategies and product improvements to ensure the predicted growth is met or exceeded.

Start Small: Begin by automating the deployment of small, non-critical features. This helps you get comfortable with the process and tools without risking major disruptions.

Automate Testing: Invest in automated testing frameworks to ensure that every code change is thoroughly vetted before it's deployed. This reduces the risk of introducing bugs into the production environment.

Monitor Performance: Implement monitoring tools to track the performance of your application in real-time. This will help you quickly identify and rectify any issues caused by new deployments.

Gather User Feedback: Use surveys, analytics, and direct user feedback to understand how changes affect user experience. This information is crucial for guiding future development efforts.

Iterate Quickly: Based on the feedback and data collected, make quick adjustments to your product. The goal is to continuously improve and adapt to user needs.

A social media platform deploys a new feature that allows users to react to posts with emojis. They use continuous deployment to roll out the feature to a small percentage of users initially, monitor engagement and performance, and then adjust based on real-time feedback before a full rollout.

An e-commerce website introduces a recommendation engine to suggest products based on user browsing history. Through continuous deployment, they can quickly iterate on the algorithm, deploying updates daily based on user interactions and purchase data, optimizing for better recommendations over time.

Identify Your Acquisition Channels: Start by pinpointing where your customers are coming from. Is it social media, word of mouth, or paid ads? Focus on the channels that bring in the most engaged users.

Optimize Activation: Make the first experience with your product unforgettable. Simplify sign-up processes, offer tutorials, or provide immediate value to ensure new users understand and enjoy your product.

Track and Improve Retention: Use analytics to find out when and why users drop off. Implement features or content that keeps them coming back, such as personalized emails, rewards for frequent use, or regular updates.

Encourage Referrals: Make it easy and rewarding for your current users to refer others. This could be through a referral program that offers benefits to both the referrer and the referee.

Maximize Revenue Opportunities: Understand what makes your users willing to pay and how much. Experiment with different pricing models, upsells, or premium features to increase your revenue.

A fitness app uses social media as its primary acquisition channel. By analyzing their data, they find Instagram stories drive the most sign-ups. They then double down on creating engaging stories to attract more users.

An online learning platform notices a drop in user engagement after the first month. They implement a 'streak' feature, rewarding users with badges and discounts for consecutive days of learning, significantly improving retention rates.