Not all visitors are created equal. Neither are all customers (purchasers). What you see when you login to most analytics tools is the average view of all visitors or all customers. The aggregate view isn’t as interesting as the segments you create, especially when you start segmenting customers. So firstly segment out your customers and compare them to the rest of your visitors. Segmenting customers In your analytics tool find the part of the tool that allows you to segment the visitors. Set-up a condition that says revenue per user is more than €1. You now have a customer segment because the tool will look for any user whom has spent more than €1 on the website and define it as a “customer”. You could also use goals or something else that is valuable to you as a business to segment in the same way. You now should have a view that compares customers to standard visitors. In the case we looked at to write this article there were some 500,000 visitors per month of which 3800 were customers. But while that already showed some huge differences to the visitors it doesn’t really go far enough. We wanted to know from those 3800 users that bought something what the differences were between them. So we dug deeper into the average order value. Average order value (AOV) The average order value is the total value of the purchases divided by the amount of customers. In our particular case this was €228. As an average this doesn’t say very much apart from in our particular case that was quite high as the company in question was largely selling beverages. So we needed to look at different segments of our customers based on the average order value. We split these into 3 sub sets of our customers. Dolphins, Whales and Blue Whales.

Not all visitors are created equal. Neither are all customers (purchasers). What you see when you login to most analytics tools is the average view of all visitors or all customers. The aggregate view isn’t as interesting as the segments you create, especially when you start segmenting customers.

So firstly segment out your customers and compare them to the rest of your visitors.

Segmenting customers

In your analytics tool find the part of the tool that allows you to segment the visitors. Set-up a condition that says revenue per user is more than €1. You now have a customer segment because the tool will look for any user whom has spent more than €1 on the website and define it as a “customer”. You could also use goals or something else that is valuable to you as a business to segment in the same way.

You now should have a view that compares customers to standard visitors. In the case we looked at to write this article there were some 500,000 visitors per month of which 3800 were customers. But while that already showed some huge differences to the visitors it doesn’t really go far enough. We wanted to know from those 3800 users that bought something what the differences were between them. So we dug deeper into the average order value.

Average order value (AOV)

The average order value is the total value of the purchases divided by the amount of customers. In our particular case this was €228. As an average this doesn’t say very much apart from in our particular case that was quite high as the company in question was largely selling beverages. So we needed to look at different segments of our customers based on the average order value. We split these into 3 sub sets of our customers. Dolphins, Whales and Blue Whales.

Dolphins

We started with segmenting customers that spent more than €1 but less than the average €228 – we called this subset of our  customers Dolphins. We found that from our 3800 customers dolphins made up about 2800 of them and on average spent €62. The purchases were 1 or 2 products per time. The most interesting thing about them was that they made up nearly 75% of our customers but only spent 20% of the revenue. They also tended to use a mobile phone more than the Whales and the Blue Whales.

Whales

Any customer that spent between €229 to €999 we called Whales. There were about 570 whales but they made up nearly 30% of the revenue. They tended to buy cheaper products in bulk suggesting that they were buying beverages for larger establishments such as small stores or cafe’s. 85% of the customers used a desktop environment (IE a PC) to buy their products.

Blue Whales

Finally there were the blue whales. They were customers who spent more than €1000 (4x the AOV). The interesting thing about these guys was that there were only 150 of them and yet they brought in 50% of all the revenue to the site. We’d found the most loyal segment. These people probably represented chains that were selling lots of beverages every month. They bought all of their products (99%) on the desktop.

Devices & Other Insights

In other articles we’ve discussed the importance of segmenting by device used. 70% of all the visitors coming to this particular site were on a mobile or tablet. When we broke that down further we found that the Apple iPad outperformed the Apple iPhone by 4 times on conversion (probably due to a wider screen). The insight being that if we designed a better user experience for the iPhone we may improve conversion rates.

As we previously noted Whales & Blue Whales converted primarily from desktop (probably in an office environment) but the dolphins were being severely underserved and were more likely to be on the move than the higher spending segments. Again a better user experience on lower screen sizes would help the Dolphins.

However the whales might be helped by being able to speed up purchases by identifying who they are by logging in and viewing “previous purchases” to simply re-order the same size batch of products.

This kind of segmentation gives great starting points but it’s just scratching the surface of what can be done.

Action Points

  1. Learn how to segment in your analytics tool. Probably one of the easiest and most valuable things you can learn. Google is your friend here if our articles aren’t enough.
  2. Segment your customers into AOV based segments (below AOV, above it to 4 times the AOV and greater than 4 times AOV). You don’t have to name them after aquatic mammals but you can if you want. A label makes things easier to talk about.
  3. Once you’ve segmented them compare and contrast against devices, traffic sources, cities and metropolitan areas, try to uncover patterns in when they visit (day of the week, time of the day), add weather and other sources of data to your numbers to see if you can determine other insights.