Product managers try to solve customer problems in ways beneficial to their business. Balancing those sometimes conflicting priorities requires frequent and sometimes difficult decisions. When you make the right decisions, your customers reward you for helping them solve their problems by continuing to use your product and recommending it to others. That results in much sought-after product growth.
When you make the wrong decisions, your customers are ambivalent about your product or leave it in droves. Either way, growth stops, and you potentially lose more customers than you gain.
To get more decisions right than wrong, you’re looking for some information to guide your choice. Several successful product managers swear by data-driven product management.
Here’s a look at data-driven product management and how you can use data to guide your product management efforts in a way that solves customer problems and leads to product growth.
What do we mean by data-driven product management?
Data-driven product management refers to collecting and analyzing data to guide your decisions when you create and evolve a product.
You can use data to gain insights into customer needs and behaviors, as well as the impact the product has on your overall business.
These insights help you make the “right” decisions about features and pricing so that your product aligns with customer needs and succeeds in the marketplace.
You use data to inform your decisions so that you’re not relying solely on gut feel or assumptions.
Rather than going to all the work to roll out a new feature that you “just know” will triple sales, looking for the right bit of data beforehand can save you time, effort, and probably a bit of embarrassment.
Even though we call it data-driven, that doesn’t mean you should only look at data. To make truly informed product decisions, your best bet is to use a bit of intuition and your gut to identify potential opportunities and apply data to help you choose which opportunity is the best idea.
What data to collect
To practice data-driven product management, you need the right data in the right amount. It should be fairly clear that absolutely no data is not helpful. But it’s also possible to have too much data.
The data you collect about your product should answer one of these two questions:
- How does my product resonate with customers?
- How much revenue will my product generate?
You need distinct data points to answer each question, and the answers help you make different decisions.
Customer-oriented metrics
When you want to learn what’s working with your product and what you need to change, look at data generated by current users or prospects. Jim Semick refers to these as customer-oriented metrics and could include:
- Product usage or adoption
- Percentage of prospects or leads who take a specific action in response to your marketing efforts (actions may include signing up for a trial, downloading a white paper, and others)
- Percentage of users who take specific action in the product itself, such as using a specific feature
- Customer retention or churn rate
- Quality (such as the number of bugs reported by users and their trend over time)
When you know what customer-oriented metrics you want to track, you’ll usually need to add tracking to your product.
As an example, ProductPlan tracks a variety of data points about actions our users take with our product. We don’t have access to the roadmaps they work with, but we can see what features people use and don’t use. This helps us to identify parts of the user experience that we may need to refine.
Business-oriented metrics
The customer-oriented metrics provide a direct picture of how your product impacts your customers.
When you want a broader view of how your product is contributing to your business overall, you can look at business-oriented metrics such as:
- Cost to acquire a new customer (CAC)
- Customer lifetime value (LTV)
- Monthly recurring revenue (MRR) generated by your product
- Average revenue per user
- Conversion (e.g., the percentage who convert from free-trial users to customers)
Individual changes you make to your products are not likely to have a direct impact on these metrics, but they will give you a sign over time if you focus your product strategy in the right direction.
How to use the data to drive product growth
When you collect the necessary data points, you can use them to guide your efforts to help customers solve their problems and drive product growth.
Specifically, data-driven product management will inform your product strategy, prioritize features, make design decisions, and make pricing and marketing decisions.
Inform product strategy
Your product strategy describes what your organization hopes to accomplish with your product and how you plan to do so. The product strategy should answer key questions such as who your product serves, how it benefits them, and your organization’s goals for the product throughout its life cycle.
Once you’ve identified your product’s strategic goals, identify the customer-oriented and business-oriented metrics that will give you the best sign that you’re progressing toward your goals.
Customer-oriented metrics act as great leading indicators and will provide rapid feedback on the impact of your product actions. Business-oriented metrics provide a more lagging view of progress toward your goals.
Now you can build this strategy (including the supporting data points) into your product roadmap, which you can then share with your executive stakeholders — demonstrating to them you’ve brought evidence, and not just your intuition, to these strategic decisions.
Prioritize features
Once you have your strategy, you can use it and the supporting data points to guide your prioritization decisions. Generally, you pick the changes to your product that will have the biggest impact on the metrics you tied to your strategy.
For example, if your product strategy is based on increasing customer lifetime value (a business-oriented metric) and your team believes that their biggest impact on LTV is via increasing retention, you’ll prioritize those features that directly impact retention.
You may also look at usage patterns and trends to identify popular existing features you want to tweak instead of introducing entirely new features.
As you consider this data to make priority decisions, you may develop your product roadmap to capture your decisions and communicate them to others in your organization.
Make design decisions
As your team implements features and other changes from your roadmap, there are a variety of decision decisions they’ll need to make.
Your team may look at trends in customer-oriented metrics to identify areas for improvement. Then they may identify several ways to address the issue and use A/B testing to determine which solution performs best.
Both are examples where you use targeted data to help you make decisions that improve your product and ultimately drive growth.
Make marketing decisions
Another area where data comes into play is deciding how to let your target market know about your product.
You can use data about customer preferences and prior buying activity to develop marketing strategies to target the right audience.
As you implement those strategies, you can use data about how potential customers respond to your marketing messages. That data helps you identify potential tweaks to your content and find the most effective channels to attract your target audience.
A big advantage to using data to help you structure your marketing activity is you feel more comfortable with your decisions, and you’re able to make objective arguments to influence your stakeholders.
Data-driven product management means data-informed decisions
Data is an essential tool for product managers. When used properly, it helps you make informed decisions about what problems to solve, what solutions you build, and how you tell prospective customers about that solution.
The key to effectively using data is to pick the right metrics and the right number of data points. You want metrics that apply to the decisions you need to make. You don’t want so much data that it confuses more than clarifies.
And finally, you don’t want to rely solely on data to make your decisions. When you combine data and intuition, your data-informed decisions will make you a much more effective product manager.
