Value of a data-driven product manager
- May 29, 2020
Product managers are visionaries and critical contributors to the success of their products. As industry leaders shift toward agile development methods, there’s a need and an opportunity for product managers to align and provide value. Understanding the value of data analytics is key to success and reduces the chances the product becomes obsolete.
Product managers are widely recognized as “CEOs” of their products. The day-to-day responsibilities for a product manager encompass managing customers (external) and company (internal) needs, all while setting a vision and making sure a world-class product is built. Engaging across the customer landscape as well as cross-functional dependencies among peers is no easy feat. This is especially true when one is asked to build a product that’s innovative and profitable. But the buck doesn’t stop there. The product must be innovative and profitable, sustain competition, evolve in its lifecycle and continuously provide value.
As the competitive landscape evolves, products fail when they become obsolete before launch. Take gaming as an example. Countless games are canceled or delayed as products miss the mark. As games are published on a multitude of platforms at a low cost, large developers are struggling to keep up with the customer’s desires.
So how do product managers ensure this doesn’t happen to their products? The answer to this question is the utilization of data.
While traditional product managers focus on customer feedback, they often under-account for customer data, which is the unspoken voice of the customer. Customer feedback shouldn’t be ignored. Rather, it should be used in conjunction with data.
This article will cover a couple of use cases to identify:
- Traditional product management vs data-driven product management
- The value gained from using data for product managers
Traditional product manager vs data-driven product manager
Imagine being a traditional product manager where your day-to-day focus is listening to the customer, reviewing survey results and partaking in focus group discussions. You’re neck-deep in qualitative research. With another sprint completion, you must gauge its value, but there isn’t enough time. Below we’ll identify some of the pitfalls of traditional product management and provide insight and solutions to the issue by introducing a data-driven product manager. To clarify, our intention isn’t to say that traditional product managers are not effective at their jobs. It’s mainly to point out that they typically rely less on data.
Use case #1 (“whack-a-mole” vs long-term approach)
Imagine getting daily calls from outspoken customers identifying issues with features.
- You tend to them and provide comfort and assurance.
- You somehow manage to buy yourself another day or two before the customer calls and complains again about the same problem
- The cycle continues.
How can you prepare yourself and provide feedback to the customer and guide them in the right direction?
Enter a traditional product manager
- This is a common challenge for traditional product managers when they’re asked to be tactical rather than strategic. In the long run, this results in product failures.
- More often than not, the customers making the calls are outspoken and account for a small sample size.
- Product managers are so engrossed in the “whack-a-mole” approach to problem-solving that they never come above the surface to get a breath of fresh air. They fall behind evaluating the bigger picture by focusing on qualitative measures such as customer calls and surveys.
- Traditional product managers usually don’t have an elegant prioritization approach. They’re focused on hearing the most outspoken customer even if it impacts a small sample size and establishes a cognitive bias.
Enter a data-driven product manager
- A data-driven product manager allocates time to qualitative and quantitative measures. While qualitative measures answer the “whys,” quantitative measures answer the “whats.” Evaluating both measures provides the context and helps understand the full spectrum of the problem at hand.
- In our use case, a data-driven product manager would identify the impact and the magnitude of the problem, prioritize the issue at hand and let the customer know when it’ll be solved. (Value Add)
- A data-driven product manager approach uses data as the great equalizer. With a repertoire of KPIs at his disposal, he can scan the vast deposits of data to identify opportunities for innovation and take proactive measures. (Value Add)
- A data-driven product manager is often a part of the agile team. With the creation of the product owner role who helps with product execution, the product managers have increased bandwidth to address strategic needs. (Value Add)
- Ideally, after every sprint, a data-driven product manager would gain some insight to make proactive decisions. With continuous feedback, a data-driven product manager can engage customers and seek their buy-in. (Value Add)
Insight
- Traditional product managers and data-driven product managers both use quantitative and qualitative measures, but the right mix is essential. The context of the problem should be understood to provide value to the customer, prioritize upcoming changes and innovate.
- Traditional product managers struggle to be forward thinking in analyzing data. They tend to solve the issue that is the hottest.
- Understanding methodologies is essential for a product manager. A data-driven product manager is a perfect fit within an agile team, but it may not be effective for a traditional product manager to be part of the agile team.
- According to a 2020 study by Alpha:
- 79% of product managers don’t have enough time to talk to customers
- 28% of product managers copy ideas from competitors
- 71% of product managers don’t have enough time to run experiments
- The survey solidifies the idea that it pays to be more data-driven to understand the big picture and innovate.
- The table below shows a summary of the differences between quantitative and qualitative measures and their characteristics:
Use case #2 (data vs data insights)
Let’s assume there are two competitor applications (App A and App B) in the marketplace that drive most of their revenues from advertising. App A has a traditional product manager at the helm leading the charge, while a data-driven product manager is leading App B. In this case, an important KPI for both parties is measuring active users at daily, weekly and monthly levels. It’s December and the number of users at every level has gone down for both Apps.
Commonalities
We aren’t arguing that traditional product managers don’t use data. Data has been at the core of the product management practice for decades. It’s the method and the extent to which data points are used that makes a difference.
Enter a traditional product manager
- A traditional product manager may look at historical data to understand the usage of the App.
- Since December is usually a holiday season, the product manager may conclude that this is an expected variance and look for numbers to start rebounding next month.
- A traditional product manager will level set with the teams internally and communicate that revenue numbers are going to be down, but better times should be on the way.
Enter a data-driven product manager
- A data-driven product manager will reach the same conclusion but dig deeper. They’ll try to use other data analytic techniques to understand the decline and try to drive growth even in these times.
- A data-driven product manager may differentiate and segment the users not logging in via an analytic method called clustering. This allows them to find a segment of the population that encompasses a higher percentage not logging in. (Value Add)
- A data-driven product manager will engage the marketing team with this data and work on different models to attract traffic to the app during the holiday season. (Value Add)
- An innovative, forward-thinking approach is needed to constantly evolve a product and provide value to all stakeholders. Being data-driven with a keen eye for insights is key to success.
Insight
- Resorting to a common solution is one way of thinking, but taking a deeper dive using data is essential for innovation and extending the product lifecycle.
- It’s also important to ensure data quality is high before driving insights.
- Data is raw and unprocessed facts, while data insights are an understanding of data with further analysis.
Data-driven product managers can help you increase engagement/usage, drive continuous improvement and innovation and derive sustainable value from your products. Organizations that empower and encourage product managers to thoughtfully apply data and insights throughout the product lifecycle are likely to outperform their competitors.
— By Bilal Mukati