Data analytics and product management go hand in hand.

Analytics in product management is necessary to learn more about your target audience, how they use your product, and whether they are satisfied with it. 

Product management analytics is the process of identifying, monitoring, and extracting actionable insights from relevant metrics that reflect your product’s performance in the market.

In simpler terms, data analytics in product management helps the product team verify their hypotheses through numbers.

In this article, let’s understand the importance of product management analytics, the crucial metrics to track, and how to act on them.

Importance of product management analytics

Product management data analytics generates insights by monitoring existing users which has the following advantages:

1. Directs product development and design: Analytics in product management helps you find what your audience wants and needs. You can rely on this information to make your product faster, leaner, and more economical by removing what your users don’t use with certainty.
2. Builds better customer relationships: Analytics help you create a personalized experience for your customers. For example, you can use analytics in product management to send notifications to your customers according to their usage patterns.
3. Assists the product management team: Integration of product analytics and management guides the product team accurately while developing a product. This reduces risks, develops the product faster, and delights customers.
4. Minimizes risk to save resources: Product management analytics, for instance, will help you know which features are at the highest risk of abandonment. You can also see session replays to accurately identify areas of friction and make targeted improvements. This will make your efforts more targeted and minimize the loss of resources.

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Thanks to different kinds of product management analytics tools, teams can track just about any metric to understand more about the pain points and expectations of their customers and deliver a solution that works for them.

There are four broad categories of product metrics that are necessary for efficient product management and analytics: engagement, retention, monetization, and satisfaction.

I. Engagement metrics

Engagement metrics let you know how much value your audience is getting from your product. There are three important engagement metrics that will help you with this:

1. Daily active users (DAU)

DAU is the number of unique users that use your app on a daily basis. A higher DAU means more users are signing up and using your product regularly. On the other hand, if your DAU falls, it is a sign that your product is not impacting the lives of your target audience as expected.

While measuring DAU it is important to define what an “active user” is for your product. In the case of mobile games, for example, an active user is someone who opens the app and plays the game, rather than just signing in and out.

Another metric that is related to DAU is MAU (monthly active users). Here are the differences between both:

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2. Session duration

Session duration is the length of time your user is actively using your app. The definition of “actively using” can vary depending on the nature of your product. Music streaming apps, for example, calculate session duration differently than a messaging app.

Keep in mind that a higher session duration doesn’t necessarily mean higher revenues. For example, someone might just browse through the catalog of an eCommerce store for hours without purchasing anything.

3. Sessions per user

It is the number of times your users start unique sessions in a given time period. You can rely on this metric to find out whether your customers are getting the values they are looking for in each instance to improve the overall usability of your product.

Tools like Google Analytics, Mixpanel, and Pendo are quite effective in measuring the above metrics for product management analytics. You can also use tools like Hotjar and Microsoft Clarity for recording user sessions.

II. Retention metrics

Customer retention metrics show you how long a user stays with you. Apart from learning whether your existing customers like your product, you can also know whether you are targeting the right audience. There are two crucial retention metrics:

1. Retention/churn rate

Retention rate is the percentage of users who are continuing to use your product after a given time. Conversely, the churn rate is the percentage of users who dropped off after a certain period.

A higher retention rate often leads to increased revenue and customer satisfaction. You can segment your users on the basis of various parameters such as location and age to get deeper insights.

2. Feature adoption rate

Feature adoption rate refers to how quickly a new feature is used by your customers. Analytics-based product management relies on this metric to verify whether they understood the customer’s problem accurately and delivered an appropriate solution.

You can monitor retention or churn rate through the same tools you use to measure customer engagement. Solutions like Appcues and Product Fruits are effective in tracking feature adoption and identifying areas of friction during analytics in product management.

III. Monetization metrics

Monetization metrics focus on the financial performance of the product and the business as a whole by tracking revenues. There are four monetization metrics you need to track for efficient product management analytics:

1. Customer acquisition cost (CAC)

Customer acquisition cost is the money you spend to get a new customer. Although a lower CAC is desirable, it doesn’t mean that a higher CAC translates to an unprofitable business. It boils down to whether you have attracted the right users who will make a purchase.

If you acquire a new user for $10.00 but only one in a hundred makes a purchase worth $50.00, you are losing money. In that case, you are better off acquiring a user for $20.00 where one of ten makes a purchase of $500.00.

Whether a CAC is sustainable for your business depends on other monetization metrics which are listed below.

2. Customer lifetime value (CLV)

CLV is the total amount of money a customer spends on a business over the course of their relationship with the company. It will help you quantify the value of customer relationships and can guide you to improve your customer retention strategies.

Due to the nature of this monetization metric, measuring it accurately for analytics in product management can be difficult.

3. Monthly recurring revenue (MRR)

MRR is how much your business earns in a month. It is an important metric for product management and is often used to track business performance over time and to set goals, especially for subscription-based products.

Furthermore, you can use this metric to compare your product’s performance with the industry standard and allocate resources rationally within your team.

Annual revenue rate (ARR) is also tracked with MRR. However, both metrics serve different purposes in product management analytics: 

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4. Average revenue per user (ARPU)

ARPU is the money spent by an average user in a month or a specific time period. 

A common approach for analytics to product management operations involves tracking ARPU for different customer segments to identify areas of improvement.

You can calculate ARPU by dividing MRR by the number of paying users.

Consider all four monetization metrics together while assessing your business performance. Focussing on only one could mislead you. 

For example, an increasing MRR alone could indicate that your product is performing well. But you should also consider ARPU simultaneously to ensure that.

There are a variety of sales tools that can be used to track your product’s revenue such as Pipedrive and Nutshell. Some of these tools come with predictive analytics for product management which can help you forecast sales based on past performance.

IV. Satisfaction metrics

Satisfaction metrics help you gauge whether your product is meeting the expectations of your users. Technically, every metric we discussed above can be used to determine customer satisfaction but two, in particular, are crucial for product management data analytics:

1. Customer satisfaction score (CSS)

Customer satisfaction score is a direct indicator of how happy your customers are with your product. Your customers rate your product on a numbered scale (usually from 1 to 5) and could share additional details about why they gave a particular score.

You can ask your customers to rate various aspects of your product or business to accurately identify areas of improvement.

2. Net promoter score (NPS)

Net promoter score is a satisfaction metric that shows the likelihood of a customer recommending your product to others on a scale of 1 to 10.

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Customers that give a score of 9-10 are considered “promoters”, 7-8 are “passives”, and 0-6 are “detractors”. Apart from measuring customer satisfaction, an NPS score also reflects customer loyalty.

Customer survey tools like Jotform and SurveyMonkey can help you learn about your customers’ perceptions of your product. Apart from CSS and NPS, you can also create subjective surveys with these tools.

Collecting customer data is the first step of analytics in product management. The next step is to understand the metrics so you can plan your next steps. Let’s look at how you can do just that.

How to act on product management analytics

Product management and data analytics teams can analyze user behavior and survey data in three ways to determine their next steps:

1. Cohort analysis

Cohorts are groups of customers with certain common characteristics. It helps product managers identify usage patterns to target more qualified niches and add relevant functionalities to the product.

For example, if your product is a daily planning app, students will use it differently than busy professionals. Analyzing both cohorts separately will help you learn more about how each kind of customer uses your product.

2. Retention analysis

Retention analysis helps product teams know what helps them retain a customer. It involves understanding the usage patterns of loyal and churned customers to get to the bottom of their behavior.

You can also learn about the functionalities that are used more by loyal users and the ones responsible for churn.

3. Funnel analysis

Funnel analysis observes the journey of your customers through various stages from awareness to purchase. Generally, funnel analysis is done by product marketing managers to improve the existing marketing and sales strategies, but it is of immense value for product managers as well.

For instance, if a lead is dropping off at the “Interest” phase, perhaps it means the relevant functionalities are not offered by the product.

Summing up

Product management analytics is a data-based approach for monitoring a product’s performance in the market and extracting actionable insights to improve it.

Engagement, retention, monetization, and satisfaction are four crucial categories of metrics that product teams keep an eye on for better product analytics and management. 

Unfortunately, product managers have to rely on multiple tools, as we mentioned in each section above, to track these metrics which makes their processes inefficient and error-prone.

Zeda.io helps teams stream product management analytics by bringing data from multiple sources to one place via integrations and enabling product managers to monitor various product management processes.

Start your free trial today.

FAQs

1. What analytics do product managers use?

Product managers track various engagement, retention, monetization, and satisfaction metrics to measure the performance of their products and find new development opportunities.

2. What is product management analysis?

Product management analysis is the process of learning how users interact with a product to map out the next development steps.

3. Is data analytics part of product management?

Yes. Data analytics helps product management to extract actionable insights from user behavioral data.

4. What is product data analytics?

It is the process of monitoring key metrics that helps product managers understand how customers engage with a product.

5. What does a product analyst do?

Product analysts conduct market research and customer surveys, monitor competitors and track user analytics to measure product performance.

6. Who earns more data analysts or product managers?

Data analysts earn between $60,000-$80,000 and product managers earn between $110,000-$130,000. However, it depends on the organization who earns more.

7. Should product managers learn data analytics?

Learning data analytics can be very valuable for product managers while learning from customer data.

8. Is Python needed for product management?

Python is a programming language with a wide range of applications. Product management, on the other hand, requires a set of skills such as prioritization, communication, and time management rather than knowledge of any particular technology.

9. What are the 5 types of data analytics?

Prescriptive, Predictive, Diagnostic, Descriptive, and Cognitive are the five types of data analytics.

10. What is the best product analytics tool?

Google Analytics, Amplitude, and Mixpanel are the three best product analytics tools.

11. Is a product analyst the same as a data analyst?

The area of focus of product analysts is the product’s business performance and are concerned with timely reports about it. Data analysts work on complex projects involving large data sets.