Maximize Growth with Product Analytics Strategies

It is only normal that during a time of disruption, when things seem to be out of control, we would want to control as much as we possibly can and that could include how we build products. However, the COVID-19 crisis showed us how crucial embracing product-led growth (PLG) is.

Product-led growth is a business method where the product itself drives user acquisition, expansion, conversion, and retention. This is pretty much saying that being “product-led” means every department, that is, sales, marketing, and engineering focuses on building better, stickier products. Thus, the product itself fuels its growth and not just marketing spend or sales initiatives.

It isn’t enough to just say that your company is big on product-led growth but to live it out and deliver the best user experience, robust product analytics are needed.

What Is Product Analytics?

Product analytics are those tools that companies make use of to collect and analyze their product’s user behavior data. When used effectively and in the right way, it helps to reveal when and how often a feature is used and by whom. It also shows what actions your users took that led to their conversion (or non-conversion).

With product analytics, you can take a walk through your users' experience with your product, by reviewing their actions. This perspective would enable you to make informed decisions based on your users’ actual behavior.

Product Analytics vs. Marketing Analytics

Product analytics and marketing analytics both provide valuable insights into your users’ engagement with your product, however, they focus on different areas. While Product analytics as we have discussed earlier focuses on the product’s user behavior data, Marketing analytics focuses on how users interact with marketing initiatives before engaging with the product.

Marketing analytics shows the early part of the user’s journey, while product analytics examines how users engage with your product at a granular level. They help reveal if your users are getting stuck and help to find out why.

How Product Managers Use Product Analytics To Fuel Company Growth

Although it is very clear how product analytics can benefit a business, actually getting it to work can be quite challenging. Products experts have helped to drop some insights as to how organizations can fully maximize analytics tools while building products. Check them out below:

Clement Kao: Segment by Engagement Levels

In a case study, product manager Clement Kao described how segmenting users by engagement level provided insights into user behaviors. This segmentation helped to reduce churn for a mobile app built for real estate agents.

At first, Clement analyzed the overall user behavior but didn’t grasp the high churn until after segmenting users by engagement. He was then able to compare behaviors and make interface changes which then led to increased engagement and retention for the mobile app.

Emil Ivov: Use Product Analytics To Investigate Hypotheses

In yet another case study, head of product for video collaboration, Emil Ivov, showed how product analytics helped test an engineering hypothesis. There appeared to be a problem that showed that users were using two video products simultaneously which would have been an inefficient use of bandwidth.

However, through the use of product analytics tools, by tracking specific events and separating users into cohorts, they discovered that their concern was unfounded and the problem was not really much of a problem. They were able to apply a simpler adjustment instead of jumping to fix a supposed problem, thereby saving time and resources.

John Cutler: Integrate Instrumentation Into Day-to-Day Product Work

John Cutler, head of education at a top tech firm, notes that he often meets product managers who view analytics instrumentation work as linear. However, he insists that the relationship between instrumentation and insight isn’t linear and that a quick pass at instrumentation unlocks valuable insights.

He emphasized that integrating instrumentation into routine work can yield even greater insights. This means that even a small investment in setting up product analytics instrumentation can help teams get the most out of their product analytics and fuel growth effectively.

Listed below are some product analytics metrics to track.

8 Product Analytics Metrics To Track

Company alignment is the most important component driving product-led growth. The product itself can propel growth when every one is concentrated on creating a better one. One strategy is to agree on main product-led benchmarks.

1. Time To Value (TTV)

Time to value is how long it takes users to reach their “aha” moment while using your product. That is, the moment they understand your product’s value. The shorter the TTV, the quicker users realize this value.

You can define your product’s “aha” moment by studying the patterns in your user behavior data. It will help you measure how you can shorten the time to value units.

2. Product-Qualified Leads (PQL)

Product-qualified leads are users who have completed an activation event in the use of your product. They understand what your product can offer them and are more likely to become paying, as well as, referring customers.

You can only track your PQLs when you know your product's activation event. When once users pass this point, they move from evaluating to now understanding the value of your product.

3. Expansion Revenue

Your expansion revenue is a key indicator that your business is growing healthily and it is also more cost-effective than acquiring new customers. It comes from users already paying for your product through upsells and add-ons.

When users have seen the value your products have to offer them, it usually becomes easier for them to invest in additional features. This is a reflection of the value your product provides.

4. Average Revenue per User (ARPU)

The average money you make per user is called your ARPU. It’s calculated by dividing monthly revenue by user count and it is important for assessing any SaaS company’s health and growth. Your ARPU can be calculated using the formula below:

MRR/number of users = ARPU

5. Customer Lifetime Value (CLV or LTV)

CLV is a measure of the total revenue that you would expect from a user over their product subscription. It helps you to assess the user value now and in the future. It can be calculated using the formula below:

(customer revenue x customer lifetime) - cost of acquisition and maintenance = CLV

You can use your CLV to identify valuable user segments. This will help you know how to adjust your product so as to boost adoption among the lower-performing segments.

6. Net Revenue Churn

Tracking net revenue churn rather than gross churn gives you an overall view of your company’s health while focusing on revenue churn rather than customer churn provides a clearer financial picture. This is because not all users contribute equally.

To calculate your net revenue churn, you can use the formula below:

(revenue lost in a period - new and expansion revenue)/ revenue at the beginning of the period = net revenue churn

Monitoring churn will help you uncover valuable data as to why your users are dropping off. It will also help you to remove friction and enhance your user experience with your product.

7. Virality

Virality is a very important product-led growth metric. It measures how each user brings new users to your product leading to exponential growth. The more likely a product is to be shared, the higher its virality.

You can calculate Virality using this formula:

C(0) * k = number of customers at end of time period.

Where:

C(0) = number of customers at the beginning of the period you are measuring

k = the number of new users each user brings to your product = i*c

i = the number of product invitations each customer shares

c = the conversion rate of those invitations

A product is considered viral if the viral coefficient is greater than 1. This indicates a significant potential for growth through user sharing.

8. Network Effects

Just like virality, network effects also depend on user sharing. They occur when a lot of users use your product and cause an increase in the value of a product, thereby encouraging the use of your products by many more users.

For example, Slack becomes increasingly valuable as more users adopt it within a company. Network effects encourage sharing and promote product-led growth.

Supplement Your Quantitative Data With Qualitative Insights

Remember that quantitative data is not enough to reduce churn. It does not tell you the full story and can be limiting if you can only see that your user numbers are reducing drastically but do not know why.

Product analytics is crucial for understanding and growing your product and with the right insights, it can help you reduce churn and help users find the value in your product. However, quantitative data alone cannot reveal the full story, hence combining it with qualitative research is very important.

Make use of your quantitative findings to guide interviews that will uncover the why behind user behaviors.