We have access to a tremendous amount of customer data these days. Even the free tools like Google Analytics give us incredible insights into where our traffic comes from and what people do on our site.
Yet, 80% of online retailers do not use their analytics sufficiently. Many don’t even track important actions like conversion rates and purchases. If you use analytics only to measure your daily traffic, you’re doing yourself and your customers a disservice.
In this post, we’re going to show you how to dig deeper into your analytics and use that data to create personalized shopping experiences for your customers. By implementing these methods, you’ll leapfrog your competition in attracting and retaining customers.
More importantly, you’ll see significant improvements in your conversion rates and revenues.
1. On-site Promotions
Build.com depends heavily on its affiliate partners to send traffic to its e-commerce store. These partners offer discount coupons that incentivize consumers to shop on Build.com.
However, there was a problem with this. When consumers clicked on a discount coupon, they were taken to the Build.com homepage. This confused the consumers because they didn’t know if the coupon had been applied or if they had to go through checkout to apply it.
To solve this, Build.com started using analytics to track the referring sites for each visitor. They also track which coupon code the visitor clicks on and the amount of the discount.
Using this data, they were able to create personalized pop-ups and banners for each visitor. For example, if a visitor comes from CouponAlbum using a 2% discount coupon, they see this pop-up as soon as they land on the Build.com homepage.
Visitors also see a banner in the header reaffirming their discount as they navigate through the rest of the site. This little tweak to their site has brought them a 6% increase in conversion rates. It’s part of their strategy to use data to personalize their website for each visitor.
If you run promotions on your site, you too can display personalized offers for each new visitor. Tracking where visitors are coming from is just one method. In Google Analytics, you can find referring URLs under the Acquisition tab. Clicking through on each source domain will show you the full URL.
You also have access to data like demographics, interests, location, behavior, and technology. You can create personalized promotions based on different combinations of this data.
Edmunds.com uses behavioral data to identify which customers are most likely to be interested in their price quotes before showing them the quotes. They analyze data like repeat vs. non-repeat customers, time of day and day of week, and geographical location of customers.
You can find this data under the Audience section in Google Analytics. The overview shows you traffic stats on an hourly, daily, weekly, and monthly level. The Geo section allows you to segment traffic by customer location, while the Behavior section (shown below) allows you to segment by New vs. Returning customers.
It gets more interesting when you cross segments to find trends. For example, Edmunds.com found that 13% of mobile visitors were within 600 feet of one of their dealerships.
In Google Analytics, you can cross reference Geo Location with Mobile visitors. However, the location is an approximation of the visitor’s IP address so you can go only as far as city.
This analytical approach helped Edmunds.com determine whether or not individual visitors would respond to a free price quote. By targeting visitors with personalized quotes, they increased their conversion rates by 18%.
Craft a unique web experience for your visitors that will make them feel like your store is perfect for their needs. Your analytics gives you a wide range of data to target visitors with personalized offers that they are more likely to respond to.
2. Email Promotions
A study by Experian shows that personalized email promotions have a 29% higher open rate and 41% higher click-through rate than emails that aren’t personalized.
But personalizing emails is about more than just using the subscribers’ names in the subject line. To really delight customers, you need to tap into their on-site activities using your analytics. The data provided by your email marketing software isn’t enough.
While SwayChic was able to identify the best times and days to send emails with their email marketing software, they realized they needed to dig deeper. In order to maximize revenue from their email promotions, they studied the behavioral patterns of their customers.
They looked at metrics like how many purchases customers made, what they purchased, and what time they were most likely to purchase. They were able to create personalized promotions and time their emails perfectly, leading to a three-fold increase in revenue per campaign.
Now, under Section 7 of the Terms of Service, you can’t store personal information, such as customer name and email, in Google Analytics. So, if you want to get down to the level of the customer, you’ll need a more powerful analytics solution. Then, you can keep track of individual behavior data and tie that back into your email promotions.
Restaurant.com tracks each customer on their site and sends automated emails to them based on certain behavioral triggers. For example, if a customer abandons an item in their shopping cart, they get sent an email. If they purchase an item from a different city, they get sent a different email.
The company also integrates other customer information like birthdays and travel dates into their emails. Customers receive birthday emails and thank you emails with discount certificates. If a customer buys a certificate but doesn’t use it, they get a reminder email.
By dropping the generic emails they used to send and switching to personalized emails based on behavioral data, Restaurant.com increased their revenue per email by 900% in just 12 months.
There’s no doubt that email is still your most effective marketing method in terms of ROI. Studies show that for every $1 you spend on email marketing, the average return is $44.25. That means small improvements in open rates and conversion rates on your emails can result in significant improvements in revenue.
3. Dynamic Pages
What do customers see when they visit your website? Do they all see the same static pages every visit, or are you personalizing their entire shopping experience with dynamic pages?
59% of online shoppers say it is easier to find interesting products on personalized e-commerce stores, and 45% are more likely to shop on a store that offers personalized recommendations.
Buying the right bottle of wine requires some guidance, which can usually be found at the local wine store. When Wine.com started selling wine online, they realized it would be tough to compete with this. Their generic product recommendations were no match for real wine experts.
Like most e-commerce stores, they simply “recommended” the current best-selling product to all visitors. These might have been popular wines, but they were not necessarily relevant to every customer.
To create a more powerful and personalized recommendation engine, Wine.com needed more data. They started analyzing the browsing and purchasing behaviors of their customers and compared that with performance data for each product.
The new Enhanced Ecommerce product performance reports in Google Analytics let you do this. You have insights into data, such as how many times the product has been viewed, added to a cart, and purchased.
What’s more, you also have insights into a product’s performance based on which group or list it shows up in. So, if a product shows up as part of a best-sellers list, Google Analytics will show you how it performed in that list.
By combining customer data and product data, Wine.com found trends that led to better product recommendations. They were able to create recommendation lists like “Customers who bought this item also bought…” and “Customers who viewed this item also viewed…”
Wine.com also takes into consideration the geographic location of the customer. They have different product inventory for different states, so customers are offered only products available in their region. In Google Analytics, you can see where people are coming from under the Geo reports.
This data-driven approach to product recommendations was a masterstroke for Wine.com. Today these recommendations drive 10% of their sales, and conversion rates on them are 52% higher than overall site rates.
Of course, if we’re talking about data-driven web personalization, we need look no further than Amazon. Amazon records every single action visitors take, down to every click and movement of the mouse.
No two visitors have the same experience when they visit Amazon’s homepage. In fact, the experience changes with each visit because it’s dynamically generated, based on metrics like browsing behavior, purchases, product searches, and so on.
By personalizing the entire shopping experience, Amazon is maximizing the chances of a visitor buying something. They are constantly experimenting to find the right products to recommend and the right way to present those products. Every data point they collect is fed back into their algorithms in real time to display the most up-to-date recommendations.
Their analytical approach has led to them making $543 in revenue per user, the highest among online retailers and ten times more than Groupon, the second highest.
We’ve only scratched the surface of how to utilize your data effectively. You can read as much as you want about analytics, but the best way to get the most out of it is to experiment with it.
Start by trying out some of the methods covered in this post. The Google Analytics reports we’ve mentioned are just a few of the many permutations and combinations that are available to you. As you get more comfortable with them, you can start using advanced segments or move on to more powerful tools like KISSmetrics.
How are you using your analytics data to increase conversion rates?
About the Author: Sid is a digital marketer at LemonStand, an eCommerce platform for professional online retailers.