Product Managers are responsible for defining the features or functions of a Product and for overseeing the development of the Product. The role of Product Managers spans many activities from developing Product Strategy to tactical plan and can vary based on the organizational structure of the organization.
Typically, Product Leaders are involved with the entire Product Lifecycle. However, the Product Management’s primary focus is on driving New Product Development. To successfully execute these roles, it’s important for Product Management to collect and synthesize proper, relevant data to make informed Product decisions.
Product Managers need to evaluate 10 categories of Key Performance Indicators (KPIs) to determine the most appropriate KPIs relevant to their work:
- Product Stickiness
- Product Usage
- Feature Adoption
- Feature Retention
- Net Promoter Score (NPS)
- Leading Indicators
- Top Feature Requests
- Product Delivery Predictability
- Product Bugs
- Product Speed and Reliability
Let’s discuss these Product Management KPIs in a bit detail.
KPIs around Product Stickiness determine whether users are re-engaging with our product. If a product is successful, it exhibit “stickiness.” That means users don’t just sign up and forget about it. They continuously live inside the product, such that it becomes part of their daily routine. A good product should not long attract new users, but to also continuously re-engage with its users.
Product Stickiness is often measured by taking the ratio of our Daily Active Users (DAU) to Monthly Active Users (MAU): i.e., DAU/MAU. This metric calculates the percentage of our monthly users who engage with our product on a daily basis.
It is inevitable that not all features of a product will be utilized the same. Some features are more heavily used, whereas others are not. The only way to know what product features are important to users is by measuring how our product is being used. Measuring user engagement across the product allows us to answer what features should we enhance, which ones to eliminate, and which features to promote to increase users awareness of the product functionality.
Product usage is measured by using 3 key metrics—Breadth: refers to the number of active users for a given client within the last 90 days. Depth: Captures whether users are using key features that make the product sticky. Frequency: e.g., number of logins across all devices within the last 90 days.
These KPIs seek to understand and set feature adoption goals. Key question to clarify these KPIs is whether users are adopting the newly released features. Feature adoption data of recent feature launches is critical to determine appropriate feature adoption goals. It is important to look at feature adoption at both the user level and account level. For instance, different customer groups with an account may exhibit different levels of adoption for different feature sets.
The key metric to measure feature adoption is the percentage of users using the feature. This should be evaluated across multiple features on a timescale (typically for at least 30 days following the feature release).
Feature retention KPIs reveal true adoption of features vs. the initial promotion-driven adoption. Feature Adoption seeks to measure initial use of a feature, whereas Feature Retention seeks to measure the long-term, persistent usage of a feature. Measuring feature retention helps us identify at-risk users who have started to disengage from the product after the initial promotion is over. We can then take action to re-engage these users.
Feature retention can be measured across different customer segments, e.g., by pricing (Free vs. Paid), by organization size (Startups vs. Enterprises), by position (Analyst vs. Manager).
Interested in learning more about the other KPIs critical to manage and develop a Product Portfolio? You can download an editable PowerPoint presentation on Product Management KPIs here on the Flevy documents marketplace.
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Enterprises invest in Analytics to improve Decision Making and outcomes across the business. This is from Product Strategy and Innovation to Supply Chain Management, Customer Experience, and Risk Management. Yet, many executives are not yet seeing the results of their Analytics initiatives and investments.
Every organization putting on investment in Analytics has experienced several stumbling blocks. This differentiates the leaders from the laggards. Analytics-driven Organizations have clearly established processes, practices, and organizational conditions to achieve Operational Excellence. Their commitment to Analytics is creating a major payoff from their investments and a competitive edge.
What It Takes to Be Analytics-driven
The Harvard Business Review Analytic Services conducted a survey of 744 business executives around the world and across a variety of industries. Their focus was on the performance gap between companies that have struggled to get a return on their Analytics investment and those that have effectively leveraged their investment.
The survey showed that Analytics-driven Organizations get sufficient return on investment in Analytics. In fact, they have been highly successful in gaining a return on Analytics investment. This is gainfully achieved as organizations use Analytics consistently in strategic decision making. Executives of Analytics-driven Organizations rely on Analytics insights when it contradicted their gut feel.
Essentially, Analytics-driven Organizations have reduced costs and risks, increased Productivity, Revenue, and Innovation, and have successfully executed their Strategy. Yet, in evolving the organization’s Analytics approach, there can be 4 core obstacles that can affect their drive to getting a greater return on investment in Analytics.
The Core Obstacles to Finding Return on Analytics Investment
Let’s briefly take a look at the first 2 obstacles:
- Communication and Decision-making Integration. The lack of Communication and Decision-making Integration limits the integration of Analytics into workflows and decision processes do not reach decision-makers. As a result of these core obstacles, the use of Analytics is limited in specific areas.
- Skills to Interpret and Apply Analytics. A second core obstacle is the inadequate skills of business staff to interpret and use Analytics. In fact, the survey showed that only one-quarter of frontline employees use Analytics with only 7% using Analytics regularly.
The other two core obstacles are siloed and fragmented Analytics and time delay. These are two equally important core obstacles that can hinder the use of Analytics to maximize return on investment. Further, the 4 core obstacles are barriers to analytic success.
Are You Ready to Be an Analytics Leader?
Leaders use Analytics consistently in decision making. In fact, based on the survey, 83% of executives use it in business planning and forecasting. On the other hand, laggards only use it 67% of the time. Even in various aspects of the organization such as Marketing, Operations, Strategy Development, Sales, Supply Chain, Pricing and Revenue Management, and Information Technology, laggards use Analytics only half the time compared to Analytics Leaders.
Analytics Leaders always ensure that they establish the processes and organizational conditions to allow them to successfully deploy Analytics. In fact, to increase return on Analytics, organizations must undertake the use of four interrelated initiatives that will drive greater return on investment Analytics. These are four initiatives essential to building an Analytics-driven Organization.
One is building an organizational culture around Analytics. To achieve this the organization must have clear, strategic, and operational objectives that are set for Analytics. Second is deploying Analytics throughout all core functions of the business.
Starting with an Analytics-driven Culture can greatly facilitate cross-functional deployment of Analytics.
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