Product development metrics give companies insight into how efficiently they build products. Product metrics typically fall into two categories: Strategic, which measure the output of the organization as a whole over a long time period, and tactical, which measure teams, individuals, systems, and projects over a short time period. In order to measure a product, both are important.
Companies can control the quality of their products as well as the rate at which they produce them with the help of product development metrics, or key performance indicators (KPIs). A company's ability to compete in the market as well as its long-term financial success are both affected by these factors. Metrics are like a car's speedometer for product teams: They let them know when to accelerate, slow down, and reinvest resources. Let's see how this work by looking at Peter’s situation as an example.
Peter is a product manager at a SaaS company in charge of building features to increase product revenue and business performance. For the ideation of new features, Peter always turns to his product management metrics to get a better insight and back up his hypothesis and suggestions. This time looking through the metrics Peter finds that the Net Promotor Score has drooped from 66 to 40. This is a serious drop and it means customers are not happy enough with the product to recommend it to others. Peter has to track the cause of the drop and fix it fast.
Looking deeper peter finds that in the same period of the NPS drop, the average session time has dropped in half along with the adoption rate going down. This means that customers are using their products less than they did before. The major culprits are the multiple new customers that were signed last month. In the beginning, their product usage was great but has continued to decline week over week. Peter asks the customer service team if they have had any issues arise from these clients before giving them a personal call. During these calls, Peter realizes that the drop in session and adoption is due to the lack of analytics. The new users are extremely analytics just like Peter and want to understand the impact of the product. But without any analytic features, it is becoming hard to track that and they have to use an outside solution to do it making work harder.
Peter immediately gets in touch with his analytics provider Toucan to expand their offering from just internal BI by adding embedded analytics to the product. Within 3 weeks of the initial conversion, Peter has the analytics feature incorporated into his company's product. Now customers would not need to exit the application for the analytics and can justify the spend with the impact they can monitor. Within months the adoption rates and average session were better than they had ever been. The net promoter score jumped to a crazy 79.
Important Product Development KPIs to Monitor for Product Managers
1. NET PROMOTER SCORE (NPS)
Net promoter score (NPS) helps you to find the number of customers that might promote your product. You will start by finding the number of detractors. Detractors are those customers that don’t promote your product. If your net promoter score is high this indicated that customers are extremely happy with your offering and will remain loyal while also helping bring in new customers with testimonials and word of mouth.
Calculate NPS as follows:
- Request your customers to rate your product from 1 to 10.
- Establish a standard to identify promoters and detractors. You can consider customers rating your product 8 or above as promoters. The other customers are detractors.
- Calculate the % of promoters and detractors.
- Calculate the net promoter score as (% of promoters – % of detractors).
Make appropriate product decisions to turn detractors into promoters.
2. ROADMAP SCORING
You might have multiple enhancement/feature requests in your product roadmap. The product roadmap scoring is a product management KPI to prioritize the enhancement/feature requests. This prioritization helps you to align your product roadmap with your strategic objectives. The best way to understand and keep track of your product Roadmap is by using the RICE Socaing method. Learn more about RICE.
3. THE NUMBER OF SUPPORT TICKETS CREATED
The number of support tickets created indicates the amount of customer support your clients need. The higher the number, the more customer support is needed showing the product isn't easy to use or has too many bugs. You can count all support tickets created in a specified period and gather other information like severity, resolution time, etc. to understand how users view your product landscape.
4. WORK POINTS RETIRED
Product development teams using the Scrum framework use this product development KPI. It can also be used for any normal project that you have road mapped. This is one of the important metrics to measure performance. A work point refers to a unit of work, and one shouldn’t confuse it with the number of hours. The number of work points retired indicates the amount of work completed by a team.
You can measure the following key metrics:
- The number of work points retired in a sprint, i.e., iteration;
- The number of work points retired per team member.
5. TEAM VELOCITY POINTS
One of the key metrics to understand overall team health, this product development KPI measures how many work points can be retired in a sprint. You have estimated your project. You know how many work points are there. You can find out how many sprints you need if you know the team's velocity points. If team velocity points are lower than estimated your team might be getting overworked.
The formula for team velocity points is:
- Team velocity points = the average number of work points retired by a team per sprint
7. THE COUNTS OF DAILY ACTIVE USERS (DAUS) AND MONTHLY ACTIVE USERS (MAUS)
Determine whether your product engages users sufficiently by measuring product management KPIs like DAUs and MAUs. Account for the following parameters, the number of interactions and the time period of the interaction.
Users that perform the above-mentioned interactions during the specific time period are active users.
- DAU is the number of active users in a day
- MAU is the number of active users in a month.
8. DAU / MAU ratio
DAU/MAU Ratio (Daily Active Users to Monthly Active Users ratio) measures how active monthly users are on a daily basis. In other words, this engagement metric measures the number of days in each month that users performed an activity that qualifies them as active users. A higher DAU/MAU Ratio generally indicates high adoption, meaning users consistently return to the app.
According to product benchmark reports, the average DAU/MAU Ratio benchmark for SaaS B2B and B2C apps is 13%. The significance of tracking the DAU/MAU ratio, as opposed to the DAU or MAU individually, arises from the fact that the ratio measures growth alongside adoption.
The formula for DAU/MAU ratio is:
- DAU/MAU ratio = Daily active users (DAUs) / Monthly active users (MAUs).
9. Session duration
Session duration refers to the period during which users are actively interacting on a website. A session expires when there has been no activity from the user for a predetermined amount of time (30 minutes by default). Session duration measures how long a person spends on a website or application. The time-on-page for the last page, from where the visitor exits, will be zero.
Average session duration is the total duration of all sessions divided by the total number of sessions. This means that all of our 0-second sessions that were the result of bounced sessions will weigh down the average session duration across all of our sessions. As a result, the higher your bounce rate is, the lower your average session duration will be.
The formula for average session rate is:
- Average session rate = total duration of all sessions / total number of sessions
10. Time to first key action
A new user's average time to try a feature or an existing user's average time to try a new feature. During that time, you may have understood the value of the feature, become curious about it due to its name or promise, or find it attractive due to context. For example: when a new user tries out your application the time to first click a navigation item from when a user opened the homepage could be 2.7 seconds.
It would be best to identify key actions with the product or service first and not measure this metric for every single tiny action. Here's another way to look at the first-time experience. What percentage of users first performed a particular action within a given period? For example, “46% of new users clicked on FAQs in the first 6 minutes of using the application.”
11. Feature Usage
Determine the total number of unique users. This is an important measurement since it gives you further insight into how many actual customers are using your feature, as well as their general usage behavior. Next, we get more detailed with the data by finding the percentage of feature users out of product users. Now it starts getting interesting. How widespread is feature usage among all of your product’s users?
The formula for feature usage rate is:
- Feature usage rate= (number of unique users of a feature / total number of unique users) x 100
Once we understand the percentage of feature usage, the next aspect is frequency. We can find this out using average feature uses per day. the average feature uses per day metric gives you insight into the frequency of daily feature usage among your consumers.
The formula for average feature uses per day is:
- Average feature uses per day = total number of times a feature was used per day / number of unique users of a feature per day
Developing a digital product is a never-ending process for many companies. To make the interface even more useful to customers, teams are constantly improving and iterating on it. As soon as the product is released to beta testers or real users, the development process enters a new phase. As a result of real user feedback, the team can add new KPIs to the product: engagement metrics. These measurements, which include user growth, average revenue per user (ARPU), and lifetime value (LTV), offer product teams an even more complete view of their users and give them even more tools to build a successful product. The best way to monitor these metrics and stay on top of them is with an analytics solution like Toucan. Check out how much easier Product development can be.