What data analysis metrics can you use to scale your product?

Here are pointers on how measuring product-market fit is useful for scaling: - Identify core value proposition - Prioritize product development - Optimize marketing campaigns - Guide pricing strategies - Inform go-to-market plans - Reduce uncertainty - Benchmark performance - Anticipate adoption trends In summary, measuring product-market fit provides invaluable guidance to scale in the right direction that customers want. It helps maximize resources towards areas with strongest demand while minimizing risk. Continuous fit tracking also establishes milestones to monitor scaling progress over time. I also want to add: optimising market campaigns are highly crucial for PMF observation.

Consider a real example: Instagram. In its early days, Instagram's founders assessed product-market fit by asking users how disappointed they would be if the app disappeared. A large portion responded "very disappointed," indicating a strong fit. This metric signaled that Instagram effectively addressed users' needs for easy photo sharing and social interaction. With this validation, Instagram could confidently scale its platform, leading to its massive user base and eventual acquisition by Facebook.

Understanding against what users we are building the product! How are we going to position the product for user segment! Building as per their custom requirement and resolving their pain points is the need for the hour. Key metrics: Time to market User adaptability Monthly active users Freemium to premium conversion rate

Product-market fit is evolving over time, not a binary state. To gauge it, consider four key signs: 1. Acquiring 100+ Users: Easily attaining 100+ active users can be assessed via surveys. Ask their feelings without the product and compare it to alternatives. 2. Measuring User Attachment: Analyze survey responses; a high "Very Disappointed" rate indicates strong attachment. 3. Assessing Relative Utility: Survey users to gauge if they see the product as superior to alternatives. 4. Studying User Behavior: Understand why users choose the product for specific tasks to determine if they actively use it as intended. Typically, users demonstrate attachment, find the product valuable, and actively engage with it.

?? Product-Market Fit Evolution: Think of Product-Market Fit as a dynamic puzzle ??. It's not a one-time achievement, but an ongoing journey. Regularly reassess your audience's needs and aspirations, then fine-tune your product to align with them. It's like being a chef ??? perfecting your recipe to suit changing tastes. Keep asking, "Are we still solving the right problems?" and adjust your course accordingly. Product-market fit isn't a static destination; it's a constantly evolving relationship between your product and your audience. ??

Here's my TOP 3 tips for smart measurements: 1. Don't blindly measure acquisition metrics, as a real gem is hiding in them: the segment of the RIGHT users for your product. 2. Be curious and break down your metrics results to find significant differences between user segments 3. Found a segment with higher signup rate or acquisition cost? You might have found gold, understand WHY these users behave differently, and use these insights to refine your targeting/acquisition strategy

I have observed that many product managers focus a lot on activation, conversion and retention - which is good but if you do not acquire how will you do the rest. User acquisition works differently for products, some drive by incentivising, referral bonus, free trail versions etc.. CPA is a metrics for this. But in my experince, you tend to ignore this when you are in 0 to 1 journey, only thing you focus is customer Acquisition. However as you achieve scale you tend to use CPA and keep it under control, a little more maturity and you introduce CLV to measure how are you doing with acquired customers.

Scaling a product involves understanding user behavior, market trends, and product performance. Think of user acquisition as inviting friends to your birthday party. You want many friends (users) to come (join), but you need to find the best way (strategy) to invite (acquire) them so they'll want to attend.

Consider the ride-sharing giant Uber. Uber closely monitors user acquisition metrics to assess its market growth. They use CPA to evaluate the cost of acquiring a new rider, factoring in marketing expenses and incentives for drivers. Simultaneously, they estimate CLV to determine the long-term value of a customer. By balancing these metrics, Uber can fine-tune its marketing strategies, set appropriate pricing, and ensure they're gaining more from each customer than they're spending on acquisition, contributing to their global expansion and success.

?? User Acquisition Alchemy: Think of it as your magical potion ??♂? for growth. User acquisition isn't just about numbers; it's about crafting the perfect formula. Experiment with marketing channels ??, refine your value proposition ??, and stay ahead of your competitors ??. ?? Metrics that Matter: Look beyond the surface. Measure the Cost Per Acquisition (CPA) ?? to see how efficiently you're gaining users, and calculate Customer Lifetime Value (CLV) ?? to predict their long-term impact. ?? Balancing Act: Find that sweet spot where acquisition costs meet the value users bring. It's like a high-stakes game of balance ??, and your ROI is the prize. Unlock the secrets of User Acquisition, and watch your product soar ??!

Were your users really "Active"? Beware of poorly defined metrics that will fool you and your decisions: - Many PMs get tempted to define "Active Users" in their metrics as a simple action such as "opened the app", or "spent more than 15sec in-app" - Shallow definitions will results in inflated DAU or MAU metrics that will be very disconnected from Retention metrics - Instead, define "Active" as the smallest action that reflect VALUE to the user, i.e. "1 message sent/opened"

Let's look at Twitter as an example. Twitter measures user engagement through metrics like daily active users (DAU) and session duration. High DAU indicates that users frequently return to the platform for real-time updates and conversations. Twitter also tracks feature usage, observing which features, like tweets, retweets, or hashtags, are most popular among users. Additionally, their Net Promoter Score (NPS) surveys gauge user sentiment, reflecting how likely users are to recommend Twitter to their network. These metrics help Twitter improve user engagement and tailor their platform to user preferences.

?? Dance of User Engagement: Picture user engagement as a captivating dance on the digital stage ????. It's not just about numbers; it's about the rhythm of interaction. ?? Metrics that Groove: Track Daily Active Users (DAU) to see who's hitting the dance floor each day, and Session Duration to measure the length of their groove. ?? Feature Showcase: Highlight the stars of your product show ??! Feature usage is like the applause after a spectacular performance. ?? Creating Fans: Think of Net Promoter Score (NPS) as the cheers from your audience ??. The more enthusiastic the crowd, the bigger your fan base. User engagement is your product's dance floor. Keep the energy high, and your users will keep dancing back! ????

Now, this is probably the most important metric there is in terms of growth. While subscription-based products will naturally seek to keep users using the product, it should be also a focus for one-time fee offers. If your users remain active, that means they are happy with the product and it does solve their problem. With that, comes good word of mouth and lots of (untrackable) growth :)

From a CSM perspective, user engagement is gold. It shows how much users enjoy our product and how likely they are to stick around. When I see high daily active users and long session durations, it's a sign that users find value in what we offer. Additionally, positive NPS scores indicate they're likely to recommend us, which is fantastic for our growth.

If defined well, retention is the ONLY type of metrics that can reflect product-market-fit. - You can move acquisition metrics by spending ads $ - You can "hack" your engagement metrics with notifications - But only a product that REALLY solves a problem well for its user will see healthy short and long term retention metrics - Use "% Active on Week 2" to measure short term retention and understand how efficient your onboarding process is - Use J-Curve Charts on a week and month scale to visualize you long term retention

Consider Netflix as a prime example of user retention tracking. Netflix measures user retention through metrics like churn rate and cohort analysis. Churn rate indicates the percentage of subscribers who cancel their subscriptions, offering insight into customer attrition. Cohort analysis helps Netflix understand user behavior over time, such as how different user groups engage with content. By tracking these metrics, Netflix can optimize its content library, recommendations, and user experience, ultimately ensuring that subscribers continue to find value in their service and remain loyal.

I believe behavioral analysis plays an important role in enhancing user retention: - Segmentation by Behavior: Divide users based on actions within the product for tailored strategies. - Event Tracking: Monitor user interactions to pinpoint high-impact actions. - User Flow Analysis: Identify drop-off points and optimize the user journey. - User Personas: Create personas to personalize content and features. - Predictive Analytics: Forecast user behavior to prevent churn proactively.

?? User Retention: The Product's Ocean Tide: Imagine your product as an endless sea ??, and user retention as the gentle ebb and flow of the tide. It's about keeping users sailing within your waters. ?? Churn Rate & Retention Rate: Churn rate is the waves pulling users away, while retention rate is the anchor keeping them onboard ?. Striking the right balance ensures a steady voyage. ?? Cohort Analysis: Think of it as different fleets ?? setting sail at various times. Analyze how each group navigates and adapt your course accordingly. User retention isn't just about keeping users; it's about keeping them engaged and excited on your product's journey. ????

User retention is a big deal. It tells us if people stick with our product for the long haul. If they do, it's a good sign - our product has value and might grow. We look at numbers like churn rate (how many leave) and retention rate (how many stay) to check this. Cohort analysis helps us see how different groups of users behave over time. It's like a report card for our product's popularity.

Airbnb is a great example of leveraging user feedback. They actively collect reviews and ratings from both hosts and guests after each stay. These reviews offer valuable insights into the quality of listings, the booking experience, and the overall satisfaction of users. Airbnb uses sentiment analysis and text analysis to identify trends and common themes in user feedback. This qualitative data not only helps them address issues promptly but also informs product enhancements, ensuring a better experience for both hosts and guests.

?? User Feedback: Your Product's Heartbeat: Think of user feedback as the heartbeat ?? of your product. It's the direct line to your users' thoughts, wishes, and emotions. ?? Methods that Speak: Surveys, interviews, reviews - these are your tools for decoding the heartbeat. It's like listening to a symphony of user voices ??. ?? Analysis Avenues: Dive into the data with sentiment analysis, text analysis, and thematic analysis. It's like turning user feedback into a beautiful painting ??, revealing patterns and insights. ?? Fueling Innovation: User feedback isn't just about hearing; it's about using those insights to fuel innovation and make your product even better User feedback isn't just words; it's the pulse of your product's journey

As a Product Manager, user feedback is super important, but also potentially misleading. Don't use the feedback section as the source of your backlog tickets. This way a few loudest, unhappy customers become roadmap-defining stakeholders and changes they demand might not be something beneficial, or at least profitable, for the entire user base. Instead, treat the comments section as a source of research inspiration that, upon wider research and validation, can potentially become a roadmap item.

Defining what is "Impact" is where the Top PMs gets differentiated from the rest, here's a few tips: - Start from your company's top KPIs and goals. Is it users growth? Profit margin increase? - Your company doesn't have defined goals? Lucky you. Meet your leadership and ask, but be prepared with your own proposal based on your expert understanding of your company's state. - Create the connection between your personal PM Scope and the company goals, how are YOU moving the needle? - Communicate your definition of product impact for internal alignment

I'm not sure about this one. While it's important in theory, I find it hard to make this a metric you can measure. Besides, following vision and mission for vision's and mission's sake is not something that determines your success. Of course, goals are there to help you reach vision/mission and product market fit and profitability, but it sounds like something different than the topic of this article is.

Selecting the right combination of metrics for your product and business objectives is crucial. Apart from metrics like making sure your product fits the market, keeping users interested, getting new users, listening to feedback & assessing product impact, there are other numbers to think about. You could also look at how often people tell others about your business and how happy your customers are i.e, metrics like virality coeficient, CSAT & network effect. You might want to see how well your product could grow in new places and make sure not too many people are leaving your product behind i.e, customer churn prediction and demographic analysis. Make your own mix of metrics suitable to your product to assess product health score.

One thing that everyone should absolutely must have is product operations. It's necessary to monitor product downtime and how often it's down and why and take proactive measures. Why is this important? Because if you're product is having frequent downtime then user experience gets affected and can lead to churn. This can be solved by setting up real-time monitoring system and without user inputs. For example, if a payment on your platform is having a higher fail rate every 2 days then it would lead to higher failed orders and user frustration. Moreover you incur cost of customer contacting for refunds, etc. Using the data to determine how frequently it goes down and saving for the root causes on priority will help improve the operations.