How Non-Technical Product Managers Can Use Data Effectively

As a Product Manager, you’re responsible for developing and executing strategies that can drive your company’s growth and success.

One way to do this is by using data to make better product decisions. But what if you don’t have a technical background or coding experience? Data can be intimidating and confusing without these skills, so what can you do?

I’m not very technical, and I use data all the time, and if I can do it, so can you. All it takes it some few tips, and a few important things to focus on.

Step 1: Define Your Goals and Key Metrics

Let’s say you’re a Product Manager for a new mobile app that your company is launching. Before you start collecting and analysing data, you need to define what success looks like for your app and what metrics you’ll use to measure that success.

To do this, you might start by asking yourself some questions:

• What is the main goal of the app? Is it to drive revenue, increase customer engagement, or something else?
• Who are your target users? What do they want and need from the app?
• What specific actions do you want users to take in the app? For example, do you want them to make a purchase, sign up for a subscription, or share the app with their friends?
• What are the most important metrics for measuring success? For example, if your main goal is revenue, you might track metrics like average revenue per user (ARPU) or customer lifetime value (CLV).

Based on your answers to these questions, you can define your goals and key metrics. Let’s say your main goal is to drive revenue, and your key metrics are ARPU and CLV. You’ll want to track these metrics over time and see how they change as you make changes to the app or your marketing strategy.

For example, if you launch a new feature that’s designed to increase user engagement and encourage more in-app purchases, you might expect to see an increase in ARPU and CLV. By tracking these metrics and comparing them to your baseline, you can measure the impact of your new feature and determine whether it’s achieving your goals.

Step 2: Understand Your Data

Now you’ve defined your goals and key metrics for your mobile app, and you’re ready to start collecting and analysing data.

This is one of the hardest parts, because without the technical knowledge how can we be sure what to look for, and what is accurate? Start with this:

• Identify your data sources: For an e-commerce website, your data sources might include your website analytics platform, your company’s CRM system, or your email marketing software. It’s important to identify all the sources of data that are relevant to your goals and metrics.
• Clean and prepare your data: Raw data is often messy and unorganized, so you’ll need to clean and prepare it before you can analyze it. For example, you might need to remove duplicates, fill in missing values, or transform data into a usable format. Let’s say you’re looking at your website analytics and notice that some customer purchase data is missing. You can clean your data by filling in those missing values with an estimated value based on historical purchase data.
• Visualise your data: Data visualisation is a powerful tool for understanding and communicating your data. Create charts and graphs that show your key metrics over time, and look for patterns or trends that can provide insights into user behaviour. For example, you might create a line chart that shows the conversion rate over time for your website, with annotations indicating when you made changes to the checkout process. This can help you identify whether those changes had a positive or negative impact on conversion rate.
• Identify outliers and anomalies: Outliers and anomalies can skew your data and make it difficult to draw accurate conclusions. Identify any unusual data points and investigate them further to determine whether they’re legitimate or errors that need to be corrected. For example, let’s say you notice a sudden spike in website traffic on a specific day. You can investigate further to see whether there was a specific event or marketing campaign that drove the traffic, or whether it was a data anomaly that needs to be corrected.
• Use descriptive statistics: Descriptive statistics can help you summarise and understand your data. Calculate basic statistics like mean, median, and standard deviation for your key metrics, and look for insights into how your website is performing. For example, you might calculate the average order value for your website and compare it to industry benchmarks to see how you’re doing.

Once you start gaining a better understanding of your data, you will start to uncover insights that can help you make more informed decisions as a Product Manager. Remember to focus on the data that’s most relevant to your goals and metrics from step 1, and use your insights to drive action and improve the user experience for your customers.

Step 3: Use Basic Data Analysis Techniques

Once you’ve identified your data sources and prepared your data for analysis, it’s time to start analysing your data. Start with these areas, which are easier to get into and quickly get results.

• Segment your data: Segmenting your data means breaking it down into smaller groups based on specific criteria. This can help you identify patterns or trends that might not be visible when looking at the data as a whole. For example, you might segment your website traffic data by geographic region to see whether there are any regional differences in user behaviour. You might also segment your customer data by purchase history to identify patterns in buying behaviour.
• Conduct A/B testing: A/B testing is a common technique used to compare the performance of two different versions of a product or feature. For example, you might test two different versions of your website’s checkout process to see which one results in higher conversion rates. By randomly assigning users to each group, you can isolate the impact of the different versions and make data-driven decisions about which one to use.
• Use correlation analysis: Correlation analysis is a statistical technique that examines the relationship between two variables. By calculating the correlation coefficient between two variables, you can determine whether they are positively or negatively correlated, or whether there is no correlation at all. For example, you might analyse the correlation between the number of product reviews and the conversion rate for a particular product to see whether there is a relationship between the two.
• Conduct cohort analysis: Cohort analysis is a technique used to compare the performance of different groups of users over time. By grouping users based on a specific characteristic (such as sign-up date or purchase history), you can track their behaviour over time and identify trends. For example, you might conduct a cohort analysis to compare the retention rates of users who signed up in different months to see whether there are any differences.
• Create dashboards: Dashboards are visual displays that summarise key metrics and performance indicators. By creating a dashboard for your website or product, you can quickly see how you’re performing against your goals and identify areas that need improvement. Dashboards can include charts, graphs, and other visualisations that make it easy to track your progress over time, and to share with others to let them know what you’re working on.

Now you can gain deeper insights into your data and make more informed decisions. Same as before, remember to focus on the techniques that are most relevant to your goals and metrics, and always think of what would create a better experience for your customers.

Step 4: Iterate and Refine

So you’ve started some more advanced analysis and you’ve got some interesting data, you need to put it to work. It’s important to use your insights to iterate and refine your product. Here are some ways to do that:

• A/B test your changes: Just as you would use A/B testing to compare two different versions of a product or feature, you can also use it to test changes that you’ve made based on data insights. For example, if you’ve identified a problem with your checkout process and made changes to address it, you can use A/B testing to see whether the changes result in higher conversion rates.
• Prioritise changes based on impact and feasibility: It’s important to prioritise the changes you want to make based on both their potential impact and their feasibility. For example, if you’ve identified that a particular feature is causing a high bounce rate, you might prioritise changes to that feature because they have the potential to significantly improve user engagement. However, you also need to consider whether those changes are feasible given your team’s resources and timeline.
• Use data to guide your decisions: As you iterate and refine your product, it’s important to continue using data to guide your decisions. For example, you might use data to determine whether the changes you’ve made are having the desired impact, or whether you need to make further changes to achieve your goals.
• Continuously monitor your metrics: It’s important to continuously monitor the metrics that are most important to your product and business goals. By monitoring your metrics regularly, you can quickly identify any issues or opportunities and make changes as needed. For example, if you notice a sudden drop in engagement or conversion rates, you can investigate the cause and make changes to address it.
• Involve your team in the process: Iterating and refining your product based on data insights should be a collaborative effort involving your entire team. Encourage your team members to contribute ideas and insights based on their own experiences and expertise, and work together to prioritise and implement changes.

Remember throughout this process to remain flexible and adaptable, and be willing to make changes as needed based on the data. Your data will change, and you should be ready to react to that.

Step 5: Stay Up-to-Date with Industry Trends

As a non-technical Product Manager, or anyone with an interest in the analytics or product industry, it’s important to stay up-to-date with the latest industry trends and best practices. This will help you make informed decisions about your product and ensure that it remains competitive in the market. Here are some ways to do that:

• Follow industry thought leaders and influencers: There are many thought leaders and influencers in the tech industry who regularly share their insights and opinions on current trends and best practices. Following them on social media platforms such as LinkedIn and Twitter can be a great way to stay informed about what’s happening in the industry. There are so many great examples to list here, I’m sure you can find the right people to follow.
• Read industry blogs and publications: There are many blogs and publications that are specifically dedicated to tech and product management. Reading these regularly can help you stay up-to-date with the latest trends and best practices. You’re probably already subscribed to a few, but here are some I like:
• Product Talk: This blog, run by Product Manager Teresa Torres, covers a wide range of product management topics, including customer research, product strategy, and team management.
• Mind the Product: This blog covers a wide range of product management topics and features contributions from a variety of authors.
• First Round Review: This publication features in-depth articles on product management, leadership, and other topics relevant to startup founders and Product Managers.
• Attend industry conferences and events: Attending industry conferences and events can be a great way to network with other professionals and learn about the latest trends and best practices in person. Some popular product management conferences include Mind the Product, ProductCon, and INDUSTRY: The Product Conference. You might be able to signup remotely to these events and attend online if you can’t go in person.
• Join industry groups and communities: There are many online communities and groups that are specifically dedicated to product management. Joining these can be a great way to connect with other professionals, ask questions, and share your own insights and experiences. I would recommend the Product Manager HQ group on LinkedIn and the Product School community.

Stay curious and open-minded, and be willing to try new things and experiment with new approaches. Not everything you hear and learn will be applicable to your current work, but you’re building up a base of knowledge and expertise that will continue to help you over time.

Summary

Not everyone is technical, but it’s becoming more and more important to be data literate and data savvy to get ahead in the product world.

In just a few easy steps, you can make better use of the data available to you and make more informed decisions about your product. These steps include understanding your data, using basic data analysis techniques, setting clear goals and KPIs, iterating and refining your approach, and staying up-to-date with industry trends.

To understand your data, start by identifying the key metrics that are relevant to your product, and track them regularly. Use visualisation tools to help you make sense of your data and identify patterns and trends.

Basic data analysis techniques can help you get more insights from your data, even without any coding skills. These techniques include using spreadsheets, creating simple charts and graphs, and conducting basic statistical analyses.

Setting clear goals and KPIs is essential for any successful product. This helps you track progress and ensure that you’re moving in the right direction. Make sure your goals are SMART (specific, measurable, achievable, relevant, and time-bound) and align with your overall product strategy.

Iterating and refining your approach is also important. This means trying out new things, testing your hypotheses, and making adjustments based on the data. Remember to stay curious and open-minded, and be willing to experiment with new approaches.

Finally, staying up-to-date with industry trends and best practices is essential for any Product Manager. Follow industry thought leaders and influencers, read industry blogs and publications, attend industry conferences and events, and join online communities and groups to stay informed about the latest trends and best practices.

By following these steps, non-technical Product Managers (or hopefully any Product Manager) can improve their use of data and make more informed decisions about their products. Even without any coding skills, it’s possible to become a data-driven Product Manager and make a big impact on your product’s success.