Are you interested in getting more out of your data analytics for eCommerce? You’re not the only one. Analytics can be intimidating: there are a lot of moving parts, a lot of strange terms, and a huge volume of writing. It can be tough to know how to improve your strategy.
This is not a comprehensive guide. Instead, it’s a look at a few key things to consider when you’re really trying to dial in your analytics.
Data analytics are best used towards an end. To get the most out of them, you need to know what you’re trying to achieve. You’ll want to set goals for specific campaigns, products, or channels.
The goals could be a certain conversion rate, retention of a particular segment of your audience, or a return on investment for a particular product or advertising campaign. What’s important is that you’re using the information you collect to build towards something in particular. Having precise goals will help you to focus on the data that matters to you.
The conversion conversation
A goal is little use without a conversion rate. The conversion rate is a ratio: take the number of sales which meet your goal criteria, divide it by the total number of visitor sessions, and multiply it by 100.
Conversion rate is crucial to analytics. It’s not automatic, however: in order to get it up and running, you have to add in a bit of code to your back end. If you’re unsure of how to do that, or if you don’t know exactly which metric you’d like to target, feel free to reach out to Arcada Labs. We’d be happy to help you out.
The trick with a conversion rate is this: in a sense, it’s less about the visitors who are converted to customers and more about those who aren’t. In the strictest sense, it’s a measure of your success, but the important part of the conversion rate is the other number: the visits that aren’t converted into successful sales. Pay attention to those visitors and you can make changes to shrink their number and bring your conversion rate up.
Once you’re tracking conversions, have a look at user behavior. What you’re seeking are potential bottlenecks or blockers. Are you seeing a lower conversion rate from users who are visiting using a certain kind of phone? It may be that your site is poorly formatted in mobile for their particular device. Are certain users abandoning their carts at checkout? It may be the case that they’re not seeing the option to complete their order.
Data (plural, not singular)
Finding bottlenecks in your conversion rate requires data. There are a whole host of sources that you can get customer analytics data from. These include the big names: Google Analytics, which you can enable with a small widget on the website, or data from Facebook. You can also get info from tools that are specifically designed for analytics: Hotjar, Taboola, and Outbrain. You can even look to email providers or direct research to learn more about the customers who are visiting.
Generally, more is more. You don’t want to be picking one of these sources to the exclusion of others. Instead, you want to be looking at all of them so that you can build out the most complete dataset possible. With so many moving parts, however, there’s a real risk of becoming overwhelmed. As you add more data, the signal can become lost in the noise.
There are different ways of handling this. One way to do this is to build an analytics dashboard. This will allow you to see the data from your various sources in one place. You can use a pre-made product, like Google Analytics Dashboard. Alternatively, if you have the know-how, you can do it yourself. Having a centralized place to view and parse your data is key to staying on top of the information that you’re collecting and getting more out of your eCommerce analytics.
There is an important exception to the “more is more” rule: if you set up too many trackers on your site, you can hinder your page feed, damaging your overall SEO. You can read more about that here.
Once you’ve got your data, you need to think about how you’re parsing it. There are two broad approaches you can take when making decisions about how to set up your online store. The first is to look at info about site sessions. These are people who’ve already visited your website, where they came from, and how they behave.
Alternatively, you could think instead about people who haven’t yet visited but might. This is the realm of predictive analytics. You’re still taking customer data and using it to make inferences. Rather than looking at how they’ve already behaved on your website, however, predictive analytics uses customer profiles scraped from various sources to predict how they might behave.
One way to get a sense of how people are behaving is through a behavior flow chart. It shows you the paths that different customers take through your website. Behavior flow is a great way to visualize the ways in which people navigate through your store: you can use it to identify what’s working and what isn’t in a highly detailed way.
It’s important to remember that customer behavior isn’t static. Good granular data analysis will account for trends and seasonal changes. If there’s a pet rock craze, make sure you’re pushing pet rocks. When it’s Christmas or Valentine’s day, adjust your behavior accordingly. Keep your ear to the ground and a wet finger to the wind.
Theory into practice
As you develop a more complete picture of your visitors and customers, you may want to try certain things to get more out of your eCommerce analytics. Think that you could get a higher conversion rate with a better page design? There are few different ways to test out your theory in order to get more out of your eCommerce analytics.
- A/B testing, in which you try out features on different groups of users, is a good way to test out different theories. There is a catch, however. If you don’t have enough traffic to your site, you may not have a large enough sample for reliable data.
- If you’re lacking customers, go back and tweak your goal conversions. By setting different goals you can see which customers are taking specific actions. It allows you insight into particular metrics — how many purchases are completed, for example. Even if you aren’t getting enough traffic to do A/B testing, you can use goal conversions in order to make inferences about what’s working and what isn’t.
- Rule-based alerts are a third option to help you separate the signal from the noise. The volume of analytics data you’re taking in can be dizzying. Google Analytics and some other platforms will alert you when you hit certain metrics. You could know when you’ve hit your target conversion rate for adults 65+, for example, or if shopping cart abandonment improves with a specific product.