As a product manager, you need to use data and analytics to understand your users, measure your product performance, and solve problems and pain points. But how do you do that effectively and efficiently? In this article, we will cover six steps to help you use data and analytics to identify and solve product problems and pain points.
Before you dive into data and analytics, you need to have a clear vision of what you want to achieve with your product and how you will measure your progress and success. You need to define your product goals and metrics that align with your business objectives, user needs, and market conditions. Your product goals and metrics should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound.
As Saqib mentioned in the other thought, customer has to be at the center of whatever you are doing. However, even to define what you need to go after you have to start from some data to identify the opportunity. Keep in mind though that data is only as good as the question asked. Therefore, it is important for to first create the right set of questions that you want answered from the data. A quick check is to see if after getting answers to the questions can you tell a short story. If you can’t then you don’t have the right questions and you need to go back to the drawing board before diving into the data.
There is a reason for a very higher failure rate in our startups and VCs, and this article is an example of that reason. There is a romantic interpretation underlies the hidden premise that data will tell us the story. Nothing can be further from the truth as it concerns human beings. We invent the story first and then find the data to make it stick. Any adventure that will look at data before taking full account of the concern at hand is bound to be a misadventure. Before being a Johnny on the dime, it would be better to put customers in the middle and have a deep relationship with what they are saying their pains are. Of course, datasets and the science of co-relations and pattern matching can be helpful, but start with the customer.
With innovative products, the ultimate goal is to build momentum with customers. People have a hard time switching from established solutions to any innovation because they worry about risks and often they're right. Switching is also a multi-stage non-linear process, especially if you run cohort analysis to see how different groups of customers adopt the product while starting at different times. To that end, I found Phil Libin's ‘Answer to everything’ model to make a lot of sense. It focuses on customer behaviour and decisions and directs your data-collecting efforts toward what truly matters. Learn more in Phil's video: https://youtu.be/3XumZ_LT_ZE
Specific metrics provide clarity on what is being measured, while measurable metrics allow for objective evaluation of progress. Achievable metrics ensure that they are realistic and attainable within the given resources and constraints. Relevant metrics align with the product goals and provide meaningful insights. Lastly, time-bound metrics establish a timeframe for evaluation and accountability.
Results-Oriented SaaS Account Executive | 5+ Years of Experience Driving Revenue Growth
Establishing an effective feedback loop is an important step in gaining valuable insights from customers to enhance product offerings allows you to define goals and metrics. Identifying problems and pain points in your product is crucial for its long-term success and growth. To achieve this, you need to establish effective feedback loops to gather and analyze customer feedback. These loops can be created through various channels such as customer support interactions, surveys, user testing sessions, and social media monitoring. By actively seeking feedback from customers, you gain valuable insights into their pain points, feature requests, and overall satisfaction.
Once you have your product goals and metrics, you need to collect and analyze relevant data that can help you track and evaluate them. You need to use both quantitative and qualitative data sources, such as analytics tools, surveys, interviews, user testing, feedback, reviews, etc. You need to analyze your data to find patterns, trends, insights, and opportunities that can inform your product decisions and actions.
Consider different approaches to gathering data. A survey, for example, may not provide deep insight into a customer's problems. But if you observe a customer while they complete tasks, you may develop a deeper understanding of what works and what causes friction. There are a wide variety of options for conducting research so consider your objectives in order to select the best method.
Results-Oriented SaaS Account Executive | 5+ Years of Experience Driving Revenue Growth
First, make a list of all the areas where quantitative and qualitative feedback can be collected. Feedback analytics platforms like Rargus help consolidate all your qualitative feedback from disparate sources like customer support, Slack, email, sales calls, app reviews, social media, and anywhere else customer feedback exists- internal and external. Once you have the data, Rargus triangulates the data to automate the tagging and categorization based on the whole data set. Look at the themes bubbling up from the aggregate of your customer reviews, identify common pain points, and quantify the frequency and impact of repeated issues expressed by your customers. Prioritize product improvements and make informed decisions based on the data.
Based on your data analysis, you need to identify product problems and pain points that are affecting your users, your product performance, or your product goals. Product problems and pain points can be anything that causes friction, frustration, confusion, dissatisfaction, or churn for your users or prevents them from achieving their desired outcomes with your product. You need to prioritize the most critical and impactful problems and pain points that need to be solved.
The first step is clearly defining the problem or pain point you want to solve. This could be based on user feedback, customer complaints, or other sources of data. Everything starts from understanding what we are solving and for whom. Otherwise, any feature can be prioritized and teams end up with a long list of items to build.
This article is not correct - how could you identify pain points without talking to your customers to uncover the pains? It doesn’t make sense how this article is written. Yes data is important but it’s important in a way to use it next to tremendous amounts of user interviews. Need early adapters and if you’re a large company s pm - you got enough users to interview! Kpis are needed to be set to reach milestone but just looking at for example where someone drops off - only looking at data, you will never understand the pain behind it! Analyze smart!
Results-Oriented SaaS Account Executive | 5+ Years of Experience Driving Revenue Growth
Prioritizing customer feedback is essential to focus your efforts on the most impactful improvements. Adopt a structured approach considering factors like feedback frequency, potential impact on user experience, alignment with product vision, and affected user segment size. By applying these criteria, determine which feedback items to address first and ensure your efforts are directed towards solving the most critical problems and pain points.
To solve product problems and pain points, you need to generate and validate hypotheses that can explain the root causes of the problems and pain points and suggest possible solutions. A hypothesis is a testable statement that links a problem, a cause, and a solution. For example, a hypothesis could be: "Users are abandoning the sign-up process because the form is too long and complex. By simplifying the form and reducing the number of fields, we can increase the sign-up rate." You need to validate your hypotheses by conducting experiments, tests, or research that can provide evidence to support or reject them.
Once you have validated your hypotheses, you need to implement and measure the solutions that can solve the product problems and pain points. You need to design, develop, and deploy the solutions in a way that is consistent with your product vision, user expectations, and technical constraints. You also need to measure the impact of the solutions on your product metrics, user behavior, and user feedback. You need to use data and analytics to evaluate the effectiveness and efficiency of the solutions and to identify any new or remaining problems and pain points.
This is very important phase. However, most teams falter in this phase by measuring the wrong metrics. To ensure long term success, you need to make sure you focus on metrics the delivery of customer value. For example, revenue and total users may seem good to target but are usually the secondary metrics. Keep the focus on measuring whether you are delivering the customer value that was identified in the problem identification phase and also measure how easily are customers able to adopt your solution. If these two metrics are performing well, laws of economics and some good marketing will get you the required revenue and user growth.
The final step is to iterate and improve your product based on the results of your data and analytics. You need to use the feedback loop of data collection, analysis, problem identification, hypothesis generation and validation, solution implementation and measurement, and iteration and improvement to continuously monitor, optimize, and enhance your product. You need to use data and analytics to learn from your successes and failures, to discover new opportunities and challenges, and to adapt to changing user needs and market conditions.
It is important to test and refine the solutions to ensure they effectively address the problem or pain point. This could involve A/B testing, user testing, or other types of experiments to evaluate the effectiveness of the proposed solutions.
As a product manager the largest pain point we have that as humans we interpret the data, all user feedback. Why? Because we can be biased and look at something and think - oh that’s a pain, let me solve it. Where’s that ML algorithm that could give us predictions based on the product performance? Well - ask me!