Product managers are the visionaries and critical contributors to the success of their products. As industry leaders shift towards agile development methods, there is a need and an opportunity for product managers to align and provide value at every increment. To gain an understanding of the value provided thru data analytics is key to success and reduces the chances of the product becoming obsolete.
Product managers are widely recognized as “CEOs” of their products. The day to day responsibilities for a product manager encompasses managing customers (external) and company (internal) needs, all while setting a vision and ensuring a world-class product is built. Having engaged across the customer landscape as well as cross-functional dependencies amongst peers is no easy feat. This is especially true when one is asked to build a product that is innovative and profitable. But the buck does not stop there. The product must be innovative and profitable, sustain competition, evolve in its life cycle, and continuously provide value.
As the competitive landscape evolves, products fail as they become obsolete before launch. Take the gaming as an example, where 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 does a product manager ensure that this does not happen to their product? The answer to this question is utilization of data.
While traditional product managers focus on customer feedback and represent the voice of the customer, they often under-account for customer data, which is the unspoken voice of the customer. By no means should customer feedback 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 are neck deep in qualitative research. With another sprint completion, you must gauge its value but there is just not enough time. Below we will identify some of the pitfalls of traditional product management and provide insight and solutions to this issue by introducing a Data-Driven Product Manager. To clarify, our intention is not to portray that Traditional Product Managers are not effective at their jobs, but it is 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 a Traditional Product Manager where they are asked to be tactical rather than strategic, which in the long run results in product failures.
- More times than not, the customers making the calls are outspoken and account for a small sample size.
- Product managers are so engrossed in “whack a mole” approach to problem solving, that they never come above the surface to get a breath of fresh air. They lag or fall behind in evaluating the bigger picture by focusing on qualitative measures such as customer calls and surveys.
- Traditional Product Managers usually do not have an elegant prioritization approach. They are focused on hearing the most outspoken customer even it impacts a small sample size thus establishing cognitive bias.
Enter a Data-Driven Product Manager:
- A Data-Driven Product Manager allocates time to both 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 above, a Data-Driven Product Manager would be able to identify the impact and the magnitude of the problem, thus prioritizing the issue at hand and providing feedback to the customer as to when it will be solved. (Value Add)
- A Data-Driven Product Manager approach uses data as the great equalizer; always having a repertoire of KPIs at his disposal, scanning the vast deposits of data to identify opportunities for innovation, and taking proactive measures. (Value Add)
- A Data-Driven Product Manager usually is 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 post every sprint, a Data-Driven Product Manager would gain some insight either at a product and/or team level to make proactive decisions. Due to the continuous feedback a Data-Driven Product Manager can engage customers and seek their buy-in. (Value Add)
- Both quantitative and qualitative measures are used by Traditional Product Managers and Data-Driven Product Managers but the need to have 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 for time to be forward thinking to analyze data. They tend to solve an issue which is the hottest.
- Understanding methodologies is essential for a product manager. A Data-Driven Product Manager is a perfect fit within an agile team while 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 do not have enough time to talk to customers
- 28% of product managers copy ideas from competitors
- 71% of product managers do not have enough time to run experiments
- The above survey solidifies the idea that it pays to be more data-driven to understand the big picture and innovate.
- The table below shows a summarized look at the difference between Quantitative and Qualitative measures and their characteristics:
Use Case 2 (Data vs Data insights): Let us assume that there are two competitor applications (App A and App B) in the marketplace which drive most of its 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 the active users daily, weekly, and monthly levels. It is December and the number of users at every level has gone down for both Apps.
- We are not making the argument that Traditional Product Managers do not use data. Data has been at the core of the product management practice for decades. It is the method and the extent to which data points are used, which makes a real difference.
Enter a Traditional Product Manager:
- A Traditional Product Manager may look at historical data to understand the usage of the App.
- As December is usually a holiday season, the product manager may conclude that this is an expected variance and look forward to numbers rebounding starting 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 yet dig deeper. They will 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 will enable them to find a segment of the population that encompasses a higher percentage not logging in. (Value add)
- A Data-Driven Product Manager will then 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 as well as provide value to all stakeholders. Being data-driven with a keen eye for insights is key to success.
- 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 life cycle.
- It is 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 by further analyzing it.
How can Aspirent help?
In the digital age, data is ubiquitous; being captured upon every event that would be of interest. The data captured by social media platforms and IoT integration are good examples. Our point of view is to emphasize Data Driven Product Management to derive insights. Our resources have depth in product management and data analytics to help transform your product. Being data-driven can help you become more innovative and extend your product’s life cycle.
“2020 Product Management Insights Report.” Alpha, 24 Feb. 2020, resources.alphahq.com/research-reports/2020-product-management-insights-report.