The Definitive Guide to Product Analytics

Retail ecommerce sales hit almost $5 trillion in 2021 and are expected to reach over $7 trillion by 2025. Ecommerce is booming, and more and more ecommerce businesses are created daily.

If you want to stay competitive, understanding why and how your customers interact with your products is key. Translation: You need reliable product analytics.

What are product analytics?

Product analytics are metrics that focus on user engagement and the behavioral data of customers, making them incredibly important.

Everyone from leadership to marketing and design teams can use this information to improve the overall customer experience. When used correctly, product analytics can help convert more sales leads, increase customer retention, and maximize revenue, as they provide rich insights into what users actually do (instead of what they say they do.)

Here’s everything you need to know about how to put them to good use.
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Product analytics: Key metrics to focus on

The first step in using product analytics successfully is knowing which metrics to focus on. If you don’t know what you’re looking for, what the data means, or how to use it, product analytics can feel overwhelming and a little bit like information overload.

“Data is like garbage. You’d better know what you are going to do with it before you collect it.”

-Mark Twain

Here are three metrics to keep a close eye on within the spectrum of product analytics.

1. Engagement

Most marketing teams probably already track metrics like pageviews, CTRs, and maybe even NPS. However, these analytics (on their own) don’t provide the full picture of the customer journey.

For example: Maybe you may have thousands of pageviews and CTRs, but not many conversions and sales. Or, better yet—you have plenty of sales, but for some reason, there aren’t many return customers.

Product analytics not only help identify superficial engagement metric data but also provide a more holistic picture of the journey a customer takes through the sales funnel. This will help indicate which steps your customers do tend to take (i.e. do they sign up for your newsletter and use a coupon code you provide there?) and where the customer encounters friction points.

These insights help create better products, as well as increase engagement that keeps customers coming back for more.

2. Retention

A Bain & Company study found a 5% increase in customer retention can increase a company’s revenue by 25% or higher. Not bad, right? Increasing retention is arguably the best way to increase revenue for your business.

That’s where product analytics come in handy. They help indicate:

• Return patterns or common customer support requests around specific products
• Track user flows of long-time customers
• Where in the sales funnel customers tend to drop off

Product analytics help zero in on where you may need to pivot marketing strategies, fix technical issues, and how you can improve the customer experience (all based on customer behavioral patterns.)

3. Customer LTV (Lifetime Value)

Not only do repeat customers spend more over time, but it also costs less to keep them long-term. If you want to increase the revenue of your business, understanding why repeat customers are repeat customers is a must.

Studying product analytics data around long-time customers will show which features they favor and what products are their favorites, so you can leverage that insight and increase the LTV for all customers.

Analysis of product analytics

In the world of product analytics there is a lot of data to collect…but then what? How do you dissect all those facts and figures? The next step is putting product analytics data to good use through various forms of analysis.

Here are a few examples of analyses you can conduct with product analytics:

• Cohort Analysis: Groups your customers together who share common characteristics.
• Trends Analysis: Outlines trend-based behaviors around products.
• Churn Analysis: shows you how many people are sticking with or abandoning your product and at what rate (ex: are they abandoning right away, or slowly over time).
• Retention Analysis: Helps identify the number of customers returning to products and how often (i.e. after day one, week three, month seven, etc.).
• Journey Analysis: Analyzes historical and operational data to identify patterns in customers’ journeys.
• Attribution Analysis: Focuses on user flow data and on the users who have completed the sales journey. It then analyzes all the different touchpoints in reverse.
• Funnel Analysis: Shows how customers are progressing through the sales funnel and which areas of the funnel can improve.
• Conversion Analysis: Gives a more detailed look into the funnel and what all converted customers have in common.
• Milestone Analysis: Helps identify the moment when loyal customers fell in love with your brand or products.
• Customer Experience: Analyzes the entire customer journey and shows which personalization tactics got customers to take high-value actions with your product. (This is a good place to use A/B testing to find a strategy to get other customers to take the same actions.)
• Personalize marketing experience: Provides behavioral data which can be used to create more personalized experiences for customers (ex: personalized ads, emails, in-app experiences, etc.)

Based on what you need to discern from your data, you can use these various types of analysis to get a clear picture of what steps to take next.

How to use products analytics insights successfully

Once you’ve collected oodles of customer data, it’s time to put it to good use. Here are some ways to maximize the efficacy of your product analytics.

1. Use the data for improved team collaboration. For example, product analytics can help your marketing team explain to web designers why changes need to be made to a landing page.
2. Focus on accuracy. You can’t make smart, data-backed decisions if you’re working from false insights.
3. Figure out what you want to track about your products. If you’re part of a team, make sure to ask other team members (in different departments) what their concerns or questions are so you all can be on the same page. From there, make sure everyone on your team has access to the same data.
4. Integrate your analytics programs with all of your platforms (ex: CRM, marketplace/store/website, specialized marketing platforms, etc.) so data isn’t siloed.

You can have all the data in the world, but if it’s not readily available to team members, isn’t accurate, or can’t be shared across departments, you’ll limit what it can do for your organization.

Choose the product analytics tool right for you

Yes, there are a lot of product analytics tools out there. That said, experimentation is the only way to figure out what will work best for your team. It may take a bit of trial and error, but when you find the right fit, it’ll be time well spent.

As you start your search, be sure to define which analyses you want to focus on and choose the tool that best fits those needs. For example, Triple Whale has a great cohort analysis and LTV calculations feature. It’ll even allow you to segment each cohort by when they first purchased a product from you, used a discount code, etc.

If you’re looking to understand the different types of customers you have, the features they respond to, and why they return, Triple Whale may be a good fit.