You kids today won't believe this, but before PayPal was founded, most ecommerce websites were basically catalogs. You pulled up a brand's page, waited five minutes for it to load, and then called their customer service number to order what you wanted. It was the dark ages. It took less than 25 years, but now more than 20% of global purchases are made online. You're still using your phone a lot of the time, but now it's apps and other cool stuff, and it's all done with databases using ecommerce analytics.
As futuristic as things are now, something like 16% of marketing managers are playing the guessing game for how they market their brands, with little or no insight into what ecommerce analytics has to teach them. If this is you, stop it right now and start making data-driven decisions that will keep your online store alive going forward.
Easier said than done, maybe, but we're here to help you out. Let's go over ecommerce analytics, from the basics to the advanced stuff, and look at tips to make this a regular part of smart brand management online.
What Is Ecommerce Analytics?
Ecommerce analytics is the study of information from various sources that affect your online store to make smart, data-driven decisions about online brand management.
In other words, there are little nuggets of valuable information bouncing off your company from all directions. If you can catch them and understand what they mean, you've gained a superpower and will consistently make the right decisions about how to market your brand online.
Let's say you run a simple Etsy store. You're one of several trillion woodworkers trying to move some end-grain cutting boards on there, and you're stumped about how to stand out. If you've ever noticed that your walnut-and-maple boards are selling well, especially the bigger ones you offer, while the white oak boards just sit unsold, it's marketing 101 to make more of those and less of the other.
That's using customer analytics, in this case, actual sales, to make a data-driven decision about inventory. Even if you just think of it as making what sells, you've turned customer insight into action and will probably do better going forward.
Now, imagine you're helping to manage a $10 billion brand of athletic shoes, and your online store tracks every move visitors make. You notice that there's a high bounce rate after potential customers watch your promotional video. That high bounce rate probably means the video is turning off potential customers and driving them away. So you ax the video and see improved retention. You're using analytics again.
Third example: Your website uses a smart widget to track cursor movements across the screen. About 8% of the people who fill up their cart and move to the checkout screen show their cursors aimlessly wandering around the page, seemingly in search of the Pay Now button. When they can't find it, they give up, bounce from your site, and you've lost the sale.
In this case, your insight is that the checkout page is horribly designed and needs a fix. After the dev team changes the layout, your sales go up by 7% (figure that 1% will never find the button, no matter what). That's an intelligent insight you would never have gotten if you didn't have the extra tool of monitoring customer behavior from the background.
Where Does All This Information Come From?
The data you're using for advanced ecommerce analytics comes from all over.
There's direct sales data, bounce rate, conversion rate, mouse movement trackers, linking data, social media feedback (positive and negative), personal interactions through customer support, angry letters from cranks who don't like your ads, feedback on highly productive sales events like Black Friday—the works.
Basically, every interaction your brand has with the world should generate some informative echoes with little grains of insight you could theoretically use to improve your market position. The trick is what you're doing to capture, process, and apply those bits of wisdom to encourage customers to click add to cart on their next visit.
How Is The Data Processed?
No human being can possibly process all of the information that comes into an ecommerce business. Teams of geniuses could work for centuries and miss more than half of the info your site can potentially gather from every interaction. That's why you need smart software to gather, analyze, and develop insights from the data.
Every app and ecommerce analytics program handles the data in different ways, so there's no single best way to do it. That makes sense since so much of your actionable insight will be coming from non-sales data. Initial processing tends to scrape the top of the pile with a fairly superficial look at data analytics. Set-piece algorithms scan through customer journey data for what are basically commonsense insights, such as more sales = better product, or Shopify generates more traffic than Twitter. That kind of stuff.
These algorithms can get pretty sophisticated, using automation to do a lot of the thinking for you.
Pulling information from direct sales data, web analytics, social media interactions, and other marketing channels, the programs can display data pretty much any way you need it on clear, easy-to-read dashboards.
Switch between the customer behavior near the top of the sales funnel and the way customers act when they're nearing the checkout screen, pull demographic data to find out how you're doing with left-handed ailurophiles (people who love cats), or generate a report about how your social media engagement went up after an ad buy, but your sales are flat.
Anything you need to know is probably a parameter in somebody's ecommerce analytics app somewhere.
What's This All Good For?
So, what's this all good for? It's nice to know how you're doing with a Facebook engagement to Twitter follow ratio, and you can probably do small things here and there to drive higher sales and better KPIs, but what else can you do with good ecommerce analytics? What do the really successful brand managers use this stuff for?
Ecommerce analytics help boost sales
The obvious first benefit of better analytics is better sales. Knowing your customers and their behavior helps you optimize your brand message for them, predict what will drive a good conversion rate, and reach them with acceptable pricing.
Knowing that your customers tend to buy milk at the same time they're buying cereal is a valuable insight, and it can inform your cross-selling efforts all over your online store.
Ecommerce analytics help drive engagement
Sales are the end of the customer journey, and repeat business is always welcome, but there's more to modern ecommerce than just making the sale. A glance at Google Analytics can tell you that much of your sales volume is coming from your brand's social media accounts.
That is, people who are exposed to your brand online might not be ready to buy right away, but they might spend $0 to follow you on YouTube. Days, weeks, or even months later, that innocent follow can turn into a sale, which can turn into a lifelong customer relationship.
Using good ecommerce analytics and carefully crafted email marketing to encourage social media followers is a major benefit of data-based marketing efforts.
Ecommerce analytics help managers make smarter predictions
As a brand manager, you're probably the go-to resource for telling the future for your C-suite. The bosses can get nervous, and they need you to tell them what the brand is doing online, how the marketing campaigns are working, and how good the key metrics look.
With advanced ecommerce analytics in hand, developed into actionable insights that make sense to ordinary humans, you can always be the person in the room who knows what's going on and how to make it better.
Even something as simple as predicting a rise in pumpkin sales leading up to October can build your credibility as a brand expert and encourage trust in the systematic methods of decision-making your analytics enable.
Coming Up With A Plan To Use Analytics In Enhanced Ecommerce
That last point bears going into. A big part of your job is probably forecasting, even if it's just a prediction for how your upcoming Google Ads or email campaigns are likely to turn out. To do your job well, you need credibility and trust with the rest of the brand's management.
If your predictions are saying that your web pages' visits decline when you load in too much media like when there's a video, a waving GIF, tons of text, and lots of third-party ads on your ecommerce site, you're essentially asking the company to trust your insight about user behavior and what drives sales.
Ecommerce Analytics Platforms We Like
We've already written up a list of ecommerce analytics platforms we like, along with explanations of what makes them so great. That article has a lot of fantastic insights into what makes a program useful for an ecommerce business, but there's more to think about.
- What is the angle you're coming at the data from?
- Are you trying to max out your average order value?
- Do you want to make your online shopping cart more user-friendly?
- Improve metrics for time on site, bounce rate, and conversions at your online business?
- Do you want to develop better SEO strategies based on which search engines new customers are using to find you?
- Do you want to try some A/B testing or evaluate your total cart abandonment rate?
- Or are you mostly concerned with new customer acquisition and retention?
How you answer those questions will inform which ecommerce analytics are most helpful for you.
Dig Into Your Ecommerce Analytics To Make Smarter Decisions
If you're still not one of the 75% of brand managers using advanced ecommerce analytics to drive forecasting and smart decisions, you're being left behind.
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