Measuring consumer behavior analytics on websites and apps offers ecommerce business leaders a front row seat to understanding your customers. While every business is different, the benefits of tracking visitor behavior are universal. This type of clickstream analysis can shed light on effective methods to drive revenue, increase customer retention, and inform business strategies and website personalization.
In this article, we explore some of the most important consumer behavior analytics to track. Together, they’ll help you understand why your customers are doing what they’re doing — and determine whether you’re optimizing for their needs.
Measure These Consumer Behavior Analytics to Drive Ecommerce Revenue
From a consumer perspective, one of the best things an ecommerce website can offer them is a personalized shopping experience—and according to Twilio’s 2022 State of Personalization report, nearly half of consumers become repeat buyers after a personalized retail experience.
From an internal perspective, the data points your team unveils from analyzing customer behaviors can be leveraged to better impact merchandising strategies.
Here are common ecommerce KPIs that, if measured, can paint a better picture of your user’s journey and allow you to adjust strategies that not only increase their satisfaction, but ultimately your bottom line.
Add-to-carts vs. purchases
Consider your customers’ purchasing patterns.
Are they loading up their carts, only to abandon them altogether because you’re forcing them to set up yet another account? Or do they consistently bounce on their way to payment, thanks to a time-consuming progression to the final confirmation page?
With 26% of shoppers abandoning their carts due to complicated checkout processes, there is a lot to be learned from what customers are doing (or not doing) when it comes to making purchases on your site.
A high cart abandonment rate may signal friction in the checkout experience. If customers are regularly ghosting their potential purchases, take a look at where that friction is consistently showing up to help identify areas for improvement.
Some of the most common reasons for cart abandonment — like forced account creation, unexpected shipping fees, or slow page loading speed — are some of the easiest to fix. Knowing these friction points exist for your customers is the foundation to improving the checkout experience.
(Important note: Watch out for product discovery platforms that don’t distinguish between add-to-cart and purchase and count both as conversions. Those who work in ecommerce know there’s a big difference when it comes to behavioral data.)
Search history
Product discovery platforms powered by artificial intelligence (AI) and machine learning (ML) personalize a shopper’s experience in real time and in future sessions, thanks to clickstream data.
Think of clickstream data like a trail of cookie crumbs. By following these bits of customer activity on your website, you can uncover information and trends that can be leveraged to improve the shopping experience.
This clickstream data can also inform your merchandising and marketing strategies, allowing you to set sophisticated rules based on user behaviors so the right products are returned in search results and promoted in category pages.
For example, imagine a customer searches for organic milk and adds it to their cart. Later, they view several organic cheese products, only to then exit the site (and effectively abandon their cart). If they return to your site, and search for more organic products, those they viewed would be returned higher up in their search results.
And since clickstream data on your site is first-party (i.e., it comes directly from your website visitors) and doesn’t reveal personally identifiable information, you don’t have to worry about privacy policies or regulations enforced by third-party cookies.
If it sounds too good to be true – uncomplicated customer data! – it’s not.
Metrics like search terms, browsing history, what products a customer clicked, and how long they’ve spent on specific pages can help shine a light on where opportunities for personalization exist on your site. And customer behavior analysis can lead to significant increases in revenue and profit.
Refinement rate
When a consumer searches a grocery site for “water” but is returned tuna cans packed in water, they’re likely not thinking they did anything wrong. They’re guessing your store doesn’t stock their item. If they have the patience, they’ll refine their initial search and try again. But if they don’t—and 40-65% of consumers don’t—they’ll simply exit.
Shoppers that aren’t returned relevant products in their initial search results have a higher chance of site exit, which means lost revenue and lower customer lifetime value. This challenge isn’t entirely new, but it’s often difficult to solve.
Zero-result searches present a similar problem. When a search returns no results, the risk of site abandonment goes up immensely.
Cognitive Embeddings Search and natural language processing, features of an AI search solution, can dramatically reduce the rate of zero-results and refined searches for ecommerce sites by solving for things like spelling correction, contextual awareness, and more.
Clicks
A high number of clicks looks great on paper, but how do they explain business outcomes or customer satisfaction?
Looking at clicks alone won’t tell you much, and it’s not the most reliable form of data generated in the clickstream. But if you sprinkle some context in there, you’ll start to see a customer journey forming.
For example, you can measure click rates alongside consumer behaviors like conversion rates, scroll depth, time on a page, and bounce rate to fill in the blanks around what products are being bought at scale and whether the right products are being returned for search queries.
Click-through rate (CTR)
Click-through-rates (CTRs) can reveal a lot about the search effectiveness of your website. Effective, snappy search = happy customer. And happy customers like to purchase.
Low CTRs can often be a flag that users are struggling to find the products they want. For example, a budget-conscious consumer searching for “blue pants” has a higher likelihood of exiting a site if they’re served pages full of overpriced, ripped jeans.
Typically, you’ll see high CTRs when customers are returned relevant, attractive products and recommendations, while low CTRs can point to a wide range of issues, from search relevancy to product pricing.
Conversion rate
If you’re seeing high conversion rates in combination with customer searches, you may assume that you’re returning the right products to customers in real-time.
But for ecommerce, it’s never that simple. (And depending on your product discovery platform, the definition of a true conversion can get muddled.)
Just because a product is relevant doesn’t mean it’s the most attractive option, or even a guaranteed conversion. And since consumers can convert on less expensive items, high conversion rates don’t always lead to an increase in revenue.
When it comes to optimizing your site for business metrics like conversion rates, take into consideration revenue per visitor (RPV) as well to better align business goals and customer behaviors.
The Hidden Costs of Inaction
The habits consumers exhibit while shopping on your site paint a full picture of their journey, and it’s up to you to notice and measure these behaviors. Ignoring the data is a huge loss for business planning and strategizing in addition to — most importantly —your customer experience.
But data can be overwhelming. We found that revenue and conversion metrics are the most common benchmarks for evaluating ecommerce team performance, and that’s a lot to carry. Luckily, user-friendly product discovery platforms can lighten the load (pun intended).
AI solutions with behavioral analytics tools can help surface customer insights in real-time. They can help automatically optimize for the data (identifying and implementing synonyms and reranking) or suggest site-wide optimizations, like boosting or slotting popular items.
This means your team can concentrate more of their time on building merchandising strategies instead of manually analyzing data from thousands of site visitors and setting rules for thousands of SKUs. And your ecommerce business will drive more revenue and profit while personalizing the customer experience.