A customer walks into a mom-and-pop convenience store, glances around, spots a shelf of pasta sauce, and moves to the shelf for a closer look. She smiles when she sees her favorite sauce and picks up a jar, but frowns when the shelf label catches her eye. She puts the jar back on its shelf, turns around, and walks out.

 What might the store owner conclude about why the customer put the product back? Price is the obvious culprit. She wanted a sauce to go with the tagliatelle she had bought earlier in the week and thought this jar was too expensive. But maybe that’s not the reason. Maybe the customer is on a low-carb diet and willpower asserted itself. Maybe she remembered that she’d arranged to meet her partner that night to eat dinner at a restaurant.

 The brick-and-mortar store owner has no way to know without asking. eCommerce retailers have an advantage. They have data about the customers who come to their store and the actions they take.

 Effective eCommerce retailers use data to build a picture of their customers. That picture shapes everything the retailer does, from product choices to promotions, to marketing, and beyond. If our pasta sauce story happened in the real world, it’s unlikely the store owner would have been watching. There would have been no data to base a decision on, no knowledge of the customer’s visit or their interest in pasta sauce.

 An eCommerce retailer, on the other hand, knows when a customer looks at a product page, when they put a product in their cart, and when they fail to complete the purchase. Moreover, they likely have data about past purchases, as well as the customer’s email address. That knowledge is valuable, and it can be used to, for example, send the customer a abandoned cart recovery email with a personalized discount on pasta sauce.

 If the shopper didn’t buy the sauce because they thought it was too expensive, the discount might motivate them to change their mind. If they really don’t want to break their low-carb diet, the email won’t work, but the data has been used to increase the likelihood of a sale.

 Let’s have a look at some of the data that is available to eCommerce retailers, and how effective retailers use it to increase sales.

Average Spend

 Purchase amount tracks how much a shopper spends in an average purchase. It is perhaps the most basic metric available to eCommerce retailers, but it is valuable for deciding where to invest marketing and promotional budgets. VIP shoppers, the most loyal customers, spend the most money. To know who these people are and to cultivate their loyalty and engagement. By cross-referencing purchase amounts with some of the other data we’ll discuss, retailers can build a profile of their ideal customer.

Organic Traffic

 According to SEMRush, organic search is responsible for almost 40% of traffic to eCommerce stores. That dwarfs the number of referrals from paid search sources like Google AdWords. As a retailer, you need to know what the people who visit your store search for, and how to apply that knowledge to content optimization.

 Google is, of course, the most useful source of organic search term data. Retailers can find out which search terms are used by shoppers who click on their store in search results. Search term information is available in a couple of places:

- Google Search Console Dashboard

- Within Google Analytics’ Acquisition -> Queries sub-menu

 Now the important thing to talk about is how do effective eCommerce retailers use this information?

To align content strategies with real-world searches. The first rule of SEO is that the content you publish must be relevant to the queries shoppers enter into search engines. High-quality content will do nothing for sales if it doesn’t mesh with searches carried out by users who intend to buy a product you sell.

To generate new content. Organic search term data tells you something about the people who visit your store. It tells you what they are interested in and how they go about finding it. You can use that information to create content that captures their interest.

Demographics and Interests

 Who visits your eCommerce store? Is it mostly women or men? How old are they? What interests them? What can you do to engage with their interests and view of the world? If you owned a brick-and-mortar store, loyalty cards and customer surveys could help to answer these questions, but eCommerce retailers have a richer source of data already.

 Google tracks users around the web, gathering demographic and interest data on any site that serves its advertising cookies. That information is made available in Google Analytics via Demographics and Interests reports.

 At their most basic, Demographics and Interests reports segregate users up into broad categories: gender, age, and so on. These categories are supplemented with conversion rates and information about on-site behavior.

 But the data reveals the interests and affinities of shoppers. Affinity Categories have always been fascinating many entrepreneurs. They break users into "affinity groups,” subsets based on areas of interest. So, your store might receive visitors who are interested in gourmet food or catering for large parties or travel.

 If you understand the interests of your visitors, content can be tailored to attract more of the same. Marketing can be personalized to appeal to a specific group. This type of data helps you to refine marketing strategies, so you’re aiming at a real target, rather than shooting in the dark.

 New eCommerce retailers understand why it is important to understand their market, but few of them appreciate just how much information is available. Their eCommerce applications collect massive amounts of data, and it’s easy to tap into high-quality datasets from internet giants like Google and Facebook. I’ve covered just three ways users can learn more about their audiences, but that’s enough to get you started on your journey to learn more about your customers.