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