Analytics-Driven Product Prioritization: Value vs Effort

Editor’s note: the following was written by a guest blogger. If you would like to contribute to the blog, please review the Product Blog contribution guidelines and contact [email protected]

What Is Product Prioritization?

Shortlisting the features which need to be built and ensuring the resources are being utilized to provide maximum value to the customer in the shortest possible time is product prioritization. The Product Manager is responsible for deciding what gets to the top of the list of the features to be built and what needs to be in the backlog. Product prioritization is very important and needs to be done before any team starts building a product because it provides the team a direction. Without product prioritization, the team could go on building whatever requests/features come their way without giving any thought. This can lead to a product that nobody wants to use. Every organization has limited resources and to make sure the resources and time are utilized efficiently, product prioritization plays a fundamental role. 

You might also be interested in: Product Templates: Feature Prioritization

How Do PMs Prioritize?

So where do Product Managers start for prioritization? There are different sources to gather a list of possible features to be built in the product like customer feedback, product data analytics, competitor features, suggestions from sales, customer support, and leadership. The most difficult part of a Product Manager’s job is to say no to the stakeholders for features that do not make sense to be built at that time. Although this activity of prioritization is very interesting and everyone wants to be part of it, it is the most challenging as well.

What Is Product Analytics?

Product analytics is all about tracking how users are engaging with your app. What features do they like to spend time on within the app and what is causing them to leave the app? Organizations capture all the user behavior data, user journey across the app and analyze it to optimize the product further.

What are the benefits of Product analytics?

There are many benefits of analytics in Product Management like below:

  • More confident decision making: Whether it’s for product prioritization or finding out the problems faced by users, if it’s backed by analytics then you can confidently present your decisions. It helps you ensure that your decision is right as it is based on the data and the facts collected from the users.
  • Understanding user engagement: Analytics provides insight into how users are engaging with your app. It helps you understand if users are actually able to use your app as expected. You can also gather information about when users are most active and what activity or feature is most engaging for your users.
  • Problems faced by users: If you monitor product data frequently, you would be able to notice trends in the behavior of the users over time. For example, users might be using a particular feature daily but then suddenly they stop using it. Data captured indicating that users are suddenly exiting the app can help you guide towards uncovering a major problem faced by the users while using a feature.
  • Data-driven mindset: Monitoring product analytics and analyzing the data helps to keep a data-driven mindset. This can be fostered among the team to take unbiased decisions related to the product.
  • Validate new features: After release, monitoring the engagement of users with the new features can help you gauge the adoption rate for each new feature introduced to the users.

How to Prioritize Your Product Based on Analytics?

white printer paper on white wall

You know now about product analytics and how features are prioritized by a Product Manager but do you actually drive product prioritization decisions based on analytics, or is it based more on a gut feeling, CEO preference, or customer feedback? Although Product Managers love the data, unfortunately during product prioritization they do not always rely on product analytics.

Next imminent question is, how do you apply analytics while prioritizing features? 

Lets go through a 5 step process below to figure out analytics-based product prioritization. If you follow these 5 steps, then it will help you get into an analytics-driven mindset and ensure that your decisions are not biased or influenced by stakeholders.

  • Integrate analytics tool with your app: The first step towards using analytics to optimize your product is to integrate analytics tools with your application. If you are new to analytics then depending on your needs, you can choose a product like Amplitude, Mixpanel, or Adobe analytics. This step is to ensure that you are receiving a fair amount of user interaction data in the dashboard. Integrating analytics tools might not be a good idea if you don’t have a large user base. Here is a quick guide to some of the best available analytics tools along with their pros and cons to help you choose a tool suitable for your product.
  • Define the KPIs for your product: You must define the most suitable KPIs for your product in alignment with all the stakeholders in your organization. Revenue metrics, usage metrics, and user engagement metrics are the major category of KPIs that every Product Manager needs to measure. These KPIs must resonate with the goal of your organization. Setting these KPIs will help to keep your head in the right direction by keeping you focused on improving these and ultimately achieving the goal of your organization. To get started, you can check out this list of KPIs which are fundamental for every product. Also, once these KPIs are set, make sure that your team and concerned stakeholders are aware of these KPIs.
  • Analyze the data and identify key trends: As a Product Manager, it is very crucial that you spend some time analyzing the user data captured over a period of time. You can validate new features released recently by observing the feature adoption rate. Identify the key trends for user behavior based on the data like a sudden drop in feature usage, increased crashes, users exiting the app on specific event triggers, etc. Example: Let us suppose you have a mobile app with video streaming as a core feature and you are observing a downtrend in the media time spent over a period of time, this indicates the users are either not interested in the content anymore or are facing streaming issues. To further narrow down the root cause, you need to observe the crashes or error events during streaming over the same period of time. You can also analyze the user flow or user’s app journey using analytics to help understand what is triggering the users to exit the app.
  • Validate the key trends with qualitative data: Now, it’s great that you are capturing and analyzing the user data. But to make sure that you are inferring it correctly and to further validate your conclusion, it is important to gather qualitative data as well. To get this data, you can conduct surveys among your user base, go through app store ratings, and also capture feedback from customer success or support teams. By analyzing this data, you should be able to figure out the top complaints from the users and also the top few delighters from your application. Once you have this information, take a step back and bring in parallel, the key trends/observations from your quantitative data (analytics dashboard).  For example: for the same video streaming mobile app mentioned above, you also observed the app ratings from the store. Most of the users are complaining about the errors that are interrupting the video streaming. This indicates that for sure, the video streaming errors or crashes have increased and you need to fix it. Qualitative data when complemented with quantitative data helps in further validating the assumptions and steering the team in the right direction.
  • Use prioritization frameworks along with the results from product analytics: There are well-known frameworks that are used by every Product Manager to evaluate the backlog and prioritize what needs to get built into the product. You might be aware of most of these frameworks, but do you actually utilize product data while using these frameworks? I am going to take the Value vs Effort framework and then demonstrate how to apply product analytics while using this framework for product prioritization.
person picking dart pins on board

Value Vs Effort

It is the most common method used by many Product Managers. Basically, with this framework, you evaluate the benefits or value of a potential feature with the amount of effort involved to build it. Let’s take an example of an established mobile app Uber and the KPI that aligns with Uber’s goal is to increase average revenue per user (ARPU). In my backlog, I have some features which I need to prioritize. The next step is to calculate value vs effort for each backlog feature and then based on the extent the feature will help to meet the KPI, assign KPI values on a scale of 1 to 5, with 5 being the highest. To assign a KPI value, use various facts to support your hypothesis. 

Let’s get into a little bit more detail and assume I have below three potential features in the backlog:

  1. Introduce car rentals
  2. Introduce special services i.e. offer rides to meet the needs of people like a car seat for babies
  3. Send notification to your emergency contact with details of the ride 

Further, I am assuming the user base is 1000 per day. Now, to support feature A, let us say from the current user base if 5 % of users book car rentals and on average, each car rental is charged to user at 100 $ then the revenue will increase by 5%* total user base * 100 i.e.the monthly revenue increase is (1000*30*5*100)/100 which is 150000$.

To support feature B, let’s say there are 10 single parents/mothers per day who are booking a ride with their baby and would prefer to pay for a car seat extra with a ride. If Uber charges 5$ extra for a car seat then the revenue increase per month is (10*5*30) i.e. 1500$.

Feature C is to help users feel safe and is not going to increase ARPU.

Considering feature A is contributing most towards the KPI as compared to feature B and C, therefore out of 5, I will assign 5 to feature A as KPI value, and 2 to feature B because the revenue increase is very less as compared to feature B. Feature C gets a 0 out of 5 because it is not contributing to increasing ARPU at all. This does not mean that the Value vs Effort framework is not useful because you still need to use this framework to make the first pass from the backlog features and then reorder them with your KPI in the picture.

In the table below you can see the value vs effort calculated values and further I have also listed the KPI value corresponding to each feature. Now, multiply the KPI value with the Value vs Effort for each feature. The final result column can be used to prioritize backlog features.

If you just use the Value vs Effort framework, then B and C will take preference over A because value vs effort is higher for B and C. But you can reprioritize your features by considering which one would actually help in meeting the KPI using this method.

FeatureValueEffortValue Vs EffortWill ARPU increase?KPI ValueFinal Result
A, Introduce car rentals531.6Rentals can be charged at more rate to the users, thus generating more revenue (150000$)58 (5*1.6)
B. Introduce special services i.e.offer rides to meet the needs of people like a car seat for babies422Single mothers without a car would be willing to pay extra for a car seat rather than bringing their own car seat for a ride (1500$)24(2*2)
C.Send notification to your emergency contact with details of the ride 212It is a safety feature and will not generate any additional revenue00 (0*2)

With this approach, you can utilize analytics to prioritize your features and hence make sure that the prioritization decisions you make as a Product Manager are based on data and not biased or influenced by a stakeholder. This will truly help in eliminating the problems faced by the users and providing features that users enjoy while simultaneously achieving your organization’s goal.

Meet the Author

Monica has close to a decade of experience in business analysis and Product Management. She started her career as a Business Analyst with Infosys and then moved into Accenture as a Product Manager. During this time, she worked with industry bigwigs such as Google, Cisco, Target. 

Today, she is building amazing mobile and OTT applications at Clearbridgemobile Inc. (an Amdocs Company). She practices agile development and loves to solve business problems with technology to bring value to the end-users. Check out her blog, where she writes about Product Management.

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