In a data-driven world, it’s becoming ever more important for product managers to be able to interpret and use data in order to make the right decisions for their products. In fact, it’s estimated that 75% of software providers now rely on data from embedded software analytics to guide product decisions and track customer health.
This increasing reliance on data may feel overwhelming for product managers without a background in data, but the reality is that you don’t need to be a data scientist to use data effectively.
By knowing what data to look for and how to use it, product managers can easily start to make better decisions for their products by leveraging data efficiently, thereby enabling them to scale their product more efficiently too.
Why do product managers need to be data-driven?
Data is vital for decision-making as it enables evidence-backed insights into how a product is performing and how it can be improved. Opinion and intuition alone are unreliable when it comes to product decisions, but data provides a foundation of evidence on which to build a better product.
By using data effectively, product managers can gain insights into:
- Which potential new features would be most valuable to customers
- Problems with a product’s user experience
- How customers use a product and its features
- The results of prior product decisions on customer acquisition, retention, and satisfaction
All of this helps to pin down the reasoning behind the product decisions you need to make. If, for example, data shows that certain features are being neglected, then you can conclude that these features need more promotion and visibility. It’s therefore essential that product managers learn to work with and understand data on a basic level.
You don’t need to become an expert overnight – there are many automated data analysis tools you can use to make your work a lot easier, and you can always lean on data experts within your team if necessary. But data-driven decisions are a powerful way to scale your product, so knowing how to leverage data is an important step to product management success.
How can product managers use data to drive good product decisions?
If you put the right emphasis on data as a product manager, then you can begin to make more effective product decisions as a result. It starts with learning which types of data you need to collect and what trends or patterns to look out for.
Different data types allow you to gather specific insights about your products and how they’re used. Using a broad range of data, you can pinpoint specific issues with your products, identify areas for improvement, and opportunities for growth. Moreover, data gives you the evidence to explain why taking action in specific ways is likely to result in a better product and user experience, and how that translates into business objectives such as sales and customer retention.
User data is particularly important. Usage data, feedback, and customer satisfaction surveys all provide valuable insight into your customers’ needs and behaviours. In collecting this data, you can build a better understanding of your customers and therefore guide products to fulfilling their needs more effectively.
As a product manager, it’s also important that you don’t just use data before you make decisions – you should make use of it afterward, too. Tracking user data after adding new features or improvements to your product can gauge how effective the new addition was. Positive data can validate the decision, while negative data can expose flaws in your design thinking and in turn enable you to avoid making the same mistakes in the future.
What metrics can product managers use to leverage data and scale their products?
There are a variety of different data metrics that product managers can use to inform better decision-making – in fact, the sheer variety of data at your disposal can be part of why data analysis can feel overwhelming. As such, it’s a good idea to look at data metrics in terms of specific categories to focus better on how they’re used.
Adoption metrics help product managers to understand the product journey of new users by examining how they’re using the product. In doing so, you can gain valuable insights into how well new users are attuning to a product and which features are their biggest focus.
Important adoption metrics include:
- Adoption rate – the percentage of new users who have started using a given feature
- Time to first action – the average time it takes for new users to utilize a given feature; higher times can indicate problems in your product’s onboarding process or suggest that features need to be made more visible
Adoption metrics can also be used to examine the behaviour of existing users in relation to new features, gauging how effectively their rollout has been and whether customers have found value in them.
Engagement insights measure how well a product is fulfilling users’ needs, largely by looking at usage data and user activity. By using engagement metrics, product managers can pinpoint areas of the user experience that may need improving, and thereby keep customers invested in the product to enhance customer retention.
Important engagement metrics include:
- Active users – generally speaking, the more active users your product has, the more engagement it enjoys; in other words, if it’s in use then it’s proving useful.
- Usage frequency – measuring how frequently customers are using your product against expected usage levels can indicate whether your product is fulfilling their needs sufficiently.
- Time spent – high usage times can be a good sign that users are engaged, but make sure to measure this against projected use times, as times that are too high can indicate inefficiencies within your product.
It’s also vital to monitor customer feedback to measure engagement. While it’s less numerical in nature and thus harder to analyze en masse, feedback still provides vital insights into what customers want from your product and therefore how to keep them engaged.
Consider using smart feedback-gathering tools that can collate feedback from different channels and automatically identify repeated requests, complaints, or praise to see which features of your product are working and which aren’t.
Finally, consider using Net Promoter Scores (NPS) as a wider indicator of your product’s engagement levels. An NPS is calculated by surveying customers on a scale of 1-10 in terms of how likely they would be to recommend your product.
Scores of 1-6 are classed as detractors, while 9-10 indicates promoters. Simply subtract the percentage of detractors among your respondents from the percentage of promoters and you’ll find a score between -100 and 100. The higher your score, the more engaged your userbase is, and the more easily your product will scale.
Data is vital to a product manager’s job. It informs the best possible decisions for your products and the wider business, and therefore enables you to grow and scale your product by identifying how it can best become an effective solution to customer needs.
However, you don’t need to be a data god to achieve this – you just need to know what data to collect and have the skills to identify trends and patterns that can guide your decision-making. You also don’t have to do it alone; you work in a team, so use the expertise of your data specialists to keep you on track when necessary.