We are swimming in a sea of data. There is data that tells you how many people searched for red feather boas for Halloween costumes. You can determine how many people searched “wedding during a pandemic” between March and October. Even learning how many page views you got from 11 a.m. Saturday morning through 10 p.m. the following Saturday is as simple as a click of a button with the right analytics tool. But what good is having access to things like eCommerce data if you don’t know how to use it, how to interpret it, or create a plan to manipulate it?
ECommerce data analytics is a valuable strategy that can help you make better business decisions. However, if you are just looking at random data, you won’t ever get anywhere with these numbers that you keep observing. With that in mind, below are the top nine mistakes we see entrepreneurs making with their eCommerce analytics. Hopefully, it will inspire you to take a look at your own methods of analysis and improve your strategies so that you have a chance to stay afloat (and grow) rather than drown in that data filled sea!
1. Looking at Vanity Metrics vs Actionable Metrics
Vanity metrics are those that look good on paper, but may not have any importance to your business. Actionable metrics are those statistics that are directly linked to specific things you can improve on in your business. Examples of vanity metrics include things like the number of
- Hits on your website
- Page views to specific pages
- Visits to your website
- Unique visitors
- Followers/friends/likes on social media
- Minutes spent on the page
- Pages seen
- Emails collected
Regardless of these numbers, while increasing them “feels good,” what really matters is action. How many people signed up for your email list? How many added something to their cart? How many people bought something? These are actionable metrics that when they go up, your revenue is likely to do so as well.
2. Assuming Data is Clean and Not Checking It
An interesting thing happened about a decade ago when people were asked for their phone number at the checkout in grocery stores and pharmacies. Many people started giving their area code and the number 867-5309. Can you guess why? They didn’t want to give their actual phone number, so they gave the one they could easily remember from the popular song of the same name.
This is a great example of data that isn’t clean. If a company were to have used the data and called that phone number, they would have been left with no prospects. What a waste of data! That’s why you want to clean your data and check that it is unique. Merge duplicate values, and confirm that everything you’re receiving is accurate.
3. Excluding Outliers
Let’s assume for a moment you have about 100 or so people that buy from your eCommerce store more than others. They purchase large quantities of your product when most people only purchase one item. That makes these 100 people outliers. Don’t ignore them as they could contain valuable information in the form of qualitative data, meaning interviews and survey responses.
4. Including Outliers
Just as bad as excluding outliers completely is the mistake of including outliers or rather, relying on the outlier data too much to build a general model. In this case, your best bet is to investigate these outliers and find out if they are errors or helpful data.
Ultimately, quantitative data is great for testing hypotheses, but it’s lousy for generating new ones unless combined with human introspection.
5. Ignoring Seasonal Trends
There is often a surge in the purchase of flowers for Mother's Day. In fact, it’s the number one day of the year that florists receive the most orders. To ignore this information could make budgeting and other business decisions problematic. When examining your ecommerce data analytics, consider what is coming up on the calendar and how it could impact your sales. This will give you a better picture of the causes behind your data so that you can make better choices for all aspects of your online store.
6. Ignoring Size When Reporting Growth
When you start your company, every new customer is considered a huge amount of growth. For example, if you just launched and today you had one customer and tomorrow you had two, you doubled your customer base. This looks exciting on paper, but it would be overzealous to say that you doubled your growth in your first month of business if you don’t get any additional customers after that.
The better way to look at your numbers is to put them in perspective. If you’re still small, growing by a new customer each day may be good. However, after you’ve grown significantly, one new customer each day is not as exciting. While consistent growth is important, it’s the amount of growth compared with your current stage that matters more.
7. Looking At Too Many Numbers
This is often referred to as data vomit. When you’re trying to grow your business, looking at all the eCommerce KPIs available to you will just leave you confused and floundering. As a result of looking at too much data, you won’t actually know where to begin with making changes that will actually move the needle. That’s why it’s recommended you focus on the one metric that matters most to your eCommerce instead of 10 or 20 of them. Draw a line in the sand that you measure your most important metric against and hone in on how to actually make changes that will have a helpful impact.
8. Metrics That Cry Wolf
Like the boy who cried wolf, if you have too many alerts coming in, you’ll quickly start to ignore your metrics when you are getting pinged every time there is a change. You don’t need to be alerted every time there is a new sign up or a new purchase. If you’re alerted too frequently, you’ll become immune to alerts, and could miss something that is important to take notice of.
9. Not Focusing on the Bigger Picture
When you focus too much on the smaller details and all the noise online, you miss the things that help the bigger picture. In other words, avoid the urge to latch on to every new trend you read about to grow your business. Keep your larger vision in mind, and focus only on the eCommerce data you committed to monitoring to achieve tangible, repeatable results.