8 Customer metrics tech companies should care about

As a tech company, tracking the wrong customer metrics can cause you to develop strategies that do not align with actual customer needs. The result of this is often increased churn, misdirected resources, and misalignment between teams.

So how do you gain a complete understanding of behavior across channels to scale in the right way? And how do you unite your team with a shared understanding of the customer journey?

These are the questions we consider in this article, providing a complete guide to the customer metrics you should care about as a tech organization. We also look at a product analytics platform you could use to conduct more efficient, effective customer analysis across channels.

What are customer metrics?

Customer metrics are insights gained from customer analysis. They track and assess customer behavior, satisfaction, and engagement with a product and cover every stage of the customer journey, from acquisition through to retention.

Tech companies also increasingly need to focus on activity across channels to gain an understanding of this customer journey. And Gartner research¹ shows that almost 50% of product managers have started collating customer analytics in recent years.

Why are customer metrics important for tech companies?

Customer metrics are crucial to understanding the causes of churn and identifying opportunities for revenue generation. A careful approach will provide nuanced insights into customer preferences and inform strategic decisions across all aspects of business.

For example, let’s say you are analyzing customer metrics for a cloud storage service. Instead of merely tracking the number of files uploaded, you look deeper and find that a segment of users who regularly collaborate on shared documents and folders have higher retention rates compared to individual users who primarily use the service for personal storage.

This would allow you to tailor your marketing and product development strategies to generate more revenue from the collaborative users. You could also provide extra customer support and onboarding to help the second group get more out of the app.

Collecting data from across channels gives you more context about user behavior inside the product while uniting other teams, such as marketing and customer support, towards a more customer-centric strategy.

By using a product analytics platform such as NetSpring, which sits directly on top of your data warehouse, you can bring all of your data together. From here, you can understand, for example, which users are creating a customer support ticket and then leaving the product completely. Having the ability to build reports and conduct exploratory analysis allows you to find answers to questions that previously were difficult to address and retain as many users as possible.

Top 8 customer metrics to track

Here we look at the most important customer metrics to track as a tech company. The table provides an overview and key formulas; after this, you can find a description of each with tips on how to improve on each data point.

Customer Acquisition Cost

This tells you the average amount of money your company spends when acquiring a new customer. By tracking this, you can assess the efficiency of your marketing and optimize resource allocation.

To improve your CAC, you need to build an accurate picture of your customer journey across channels. Using marketing analytics software like NetSpring, you can understand which online channels (e.g. YouTube, your website, LinkedIn) drive users to your product; this can help you allocate your marketing budget more accurately.

Formula: Total cost (marketing + sales) / Number of new customers

Time To Activate

Time To Activate is the period of time between a user signing up and completing their first activation milestone. You’ll decide how this milestone looks; it could be anything from making their first purchase to setting up a profile.

By understanding Time To Activate alongside analyzing the customer journey, you can identify issues that are preventing users from becoming engaged with your product.

For example, if your activation milestone is setting up a profile, a prolonged Time To Activate might indicate issues with the onboarding process. You could then ask your customer support team to gather feedback from users who are experiencing difficulties setting up their profiles and use this insight to reduce the time involved.

Formula: Activation timestamp − sign-up timestamp

DAU/MAU

DAU and MAU look at how many active users you have on a daily or monthly basis. They are the key engagement metrics to show how your user base is growing and how they respond to improvements you make inside the product.

You can boost DAU/MAU by personalizing the user journey with data about a particular segment’s behavior, preferences, and interactions. For example, you could send push notifications to specific user segments based on their past interactions with the product.

Formulas: Total MAU= Unique active users in one month + returning users in one month

Total DAU= Unique active users in one day + returning users in one day

Average MAU/Annual MAU= Sum of each month’s active users / 12

Feature Adoption Rate

Feature adoption analyzes user’s interactions with a specific feature inside your product. This helps guide your product roadmap by showing where users are finding the most value. You can also identify areas where users may be encountering difficulties.

To improve your feature adoption rate, you can then create targeted marketing campaigns, onboarding processes, and help guides.

Formula: Number of customers who adopt feature/number of total customers) x 100

First Contact Resolution Rate

First contact resolution rate is a customer support metric that shows how often customer inquiries or issues are resolved during the initial interaction with a support agent, indicating a decreased likelihood of churn. You can gather this data from ticketing systems, customer surveys, and more.

You can improve your first contact resolution rate by identifying common trends in cases where issues were not resolved alongside looking at product data. From here you can identify issues that might act as bottlenecks that impact an agent’s ability to resolve problems quickly. You can also proactively prevent issues from arrising by targeting similar users to those who raised the support tickets with support guides.

Formula: Number of inquiries resolved during first contact/ total number of inquiries x 100

Net Promoter Score (NPS)

​​Net Promoter Score measures how likely your users are to promote your product to others. It’s measured by asking a question like “On a scale of 0 to 10, how likely are you to recommend this product to a friend?” People who answer with a score of 6 or above are known as promoters while those below 6 are known as detractors.

Understanding your NPS allows you to pinpoint issues with your product journey. If you ask users whether they’d recommend you after onboarding and you get a high number of detractors, for instance, this could indicate issues.

To improve your NPS, you can establish a feedback loop that allows your customer support team to give feedback directly to your product team and vice versa. By collecting insights from customers at various touch points along their journey, you can keep meeting their expectations.

Retention Rate

Retention rate is the essential metric to understand how many users continue to use your product over a certain period of time. Within tech companies, it serves as a key indicator of success for marketing teams, product teams, and customer service teams alike.

Equally, by using cross-channel data from each of these teams, you can boost your retention rate over time. For example, product data can identify characteristics and behaviors of people who typically churn, and then marketing can intervene with a campaign or support initiative.

Formula: Number of users at the end of the time period – number of users acquired during the time period/ total number of users at the beginning of the period

Reactivation Rate

Reactivation rate shows how many of your inactive or churned customers return to use your product within a specific period of time. This shows your ability to re-engage customers and can also give you insights into why customers churned or became inactive in the first place.

You can boost your reactivation rate by identifying the characteristics, behaviors, and preferences of people who typically re-engage before targeting your efforts.

Formula: Reactivated customers / total churned customers * 100

Analyze customer metrics more accurately with Netspring

With a product analytics platform like NetSpring, you can bring your data together from across channels. This allows you to build a complete understanding of the customer journey to align as teams toward growth.

With NetSpring, you get a self-serve platform, where you can access:

• Accurate insights from across channels (because NetSpring works on top of your data warehouse)
• A time-ordered, 360-degree view of user behavior
• The chance to freely answer your own hypotheses with exploratory analysis
• The ability to create custom reports
• Self-serve funnel, path, and cohort analysis