When you take that first deep dive into analytics for your online store, things can get overwhelming fast. There’s so much data to be explored, so many lessons to be learned, that you can hardly be blamed for not knowing what to do first.

One of the biggest challenges you’ll face is knowing what data is important (and what’s not). You can only focus your time and attention on a few main goals at a time, so the data you review and act on should be chosen carefully.

But how can you tell which metrics match up to what you’re doing? Or, if your store is new, how can you know which metrics you should be tracking — and focusing your efforts on — first?

Today, you’ll learn which metrics you should track for your specific store. Ready to dive into the data? Let’s go.

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By now, you probably know that customers love a good sale. Discounts, coupon codes, and special offers are enticing, and often good for business, considering how much attention they can drum up.

When it comes to promotions created by owners of small or new online stores, though, finding the time to walk through every step of the process can be tough. The temptation is often there to “wing it” — to simply set up what sounds appealing and hope it works out for everyone.

Winging it might work for a little while, but the decisions you make should be based on data, not guesswork. After your promotions end, you should be reviewing the results and learning what to do differently next time. Otherwise, both you and your customers could be missing out.

Let’s take a look at what you should review after each sale concludes, plus what you should do to ensure each subsequent promotion is a perfect fit for your shoppers.

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The holidays are coming to a close, and with them your online store’s discounts and deals. Things are quieting down and sales are slowing, which means it’s back to business as usual for you, yes?

Perhaps not. You might be surprised to learn that one of the very best things you can do at the end of the holiday season is, well, another deep dive into the holiday season. But we’re not talking about running another big sale. We’re talking about diving in and reviewing some data.

With more customer and order data flowing in than usual, now’s the perfect chance to learn what you’re doing right — or wrong — and how you can improve your store. If you study your sales, returns, feedback, and so on, you stand to learn quite a bit from the holiday experience.

Ready to find out what you should take away from the holidays? Let’s proceed.

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In the past year, while remodeling my house, I’ve spent approximately 80 percent of my time and 140 percent of my patience dealing with a home improvement store. As one example of this journey, when I called to check the status on my windows order, I was told they weren’t sure where in the US it was.

During the two week process of locating the order, I had to call and re-explain the situation to nearly everyone in the windows department. I even tweeted their corporate office, who told me to email them, and they then provided incorrect information via email. The process convinced me that this business truly did not care about my experience with their brand.

But the truth is that it’s not really the company’s fault. Here’s how information and data would have had to flow for me to get the experience I wanted:

Just to fulfill my purchase to my expectations, my order information had to travel from the local office systems to the manufacturer’s, the delivery company, and the corporate headquarters. Then feedback about the physical goods and my interactions with different levels of customer support had to flow back through all these systems.

As a digital marketer, I see why this is hard. But as a customer, I still expect the company to know my order and use that knowledge to provide timely service — and your customers likely feel the same way.

That’s where the joys and pains of Big Data comes in.

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With personalized product recommendation engines, eCommerce stores can immediately deliver product suggestions. Rather than providing users with pre-defined related products, highly relevant products can automatically be recommended on any page, from products to the shopping cart.

We wanted to give you a closer look at how machine learning technology tracks, stores, and analyzes data so that you can deliver both product and user recommendations to customers. We also wanted to more closely explore the advantages of personalized recommendations over pre-defined related products. So today we’re going to do just that.

Let’s dive into personalized recommendation engines and explore how they learn from customers to deliver highly accurate product suggestions, and take a look at how one store has used our plugin to meet their specific needs.

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Customers love that personal touch, whether it’s being addressed by name in an email, a shop owner remembering their birthday, or being able to customize a website to their needs. It can make them feel like they’re your single most important customer.

With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations will make every customer feel like your shop was created just for them — and you’ll enjoy a natural boost in conversions and order values.

Let’s take a look at what we mean when we say “personalized” recommendations, explore how implementing these recommendations can help you improve your store, and review how to decide if a recommendation engine or plugin is the right choice for you.

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As an eCommerce store owner, you’re likely focused on driving more traffic to your website from search engines, social media, and so on. But if your website isn’t converting enough of this traffic into sales, you may be feeling frustrated.

If a certain page or component on your website was causing these potential customers to leave, you would definitely want to address it. Well, through the use of data in Google Analytics, you can.

Using data from Google Analytics, you can identify the pages that are the weakest points in your sales process. Once identified, you can make plans to improve them, and convert more of your traffic into money.

Curious to know how you can turn more traffic into sales? Let’s dive into the data and find out what could be causing your customers to leave.

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Google recently announced a program called Enhanced eCommerce for Google Analytics, which utilizes further data for eCommerce site owners, not only helping you understand what types of customers are coming to your website, but how they respond while moving about the site.

It replaces and adds to the current reports you receive for Google Analytics, but it incorporates a funnel system to hone in on the areas that you weren’t able to see before. Now you can view where people drop off in the checkout process and even find products that require more marketing efforts.

Let’s take a look at how to set up Enhanced eCommerce for Google Analytics and walk through an overview of the most powerful reports.

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