Big Data in Ecommerce Personalization, Explained

Big data is everywhere. All the information that you have ever provided online, all the saved cookies in your browser, and all the online banking apps that you have ever used — all of these store tons of details about you. That’s why when you search for something online, you’ll find ads for the same product following you everywhere you go.

It’s not necessarily a bad thing. While some oppose collecting big data in general, there’s still plenty of room to theorize and practice big data for business purposes. One of the most promising domains for this is marketing, as user data is a major ingredient in a healthy marketing mix. Here, personalization becomes one of the most frequently exploited use cases for big data applications that proves to have high ROI.

The reason behind it is simple. Marketing and sales are all about understanding customers’ pain points and desires. What used to take years of marketing research, now takes days or even hours with the data collected from personal profiles of online shoppers and made actionable through ecommerce development.

Let’s take a closer look at several prominent applications of big data for marketing, specifically where it drives product and service personalization.

Create Recommendations

It’s probably one of the most commonly known big data applications in ecommerce. We constantly deal with personalized recommendations based on our previous purchases while visiting online stores.

Big data helps to uncover product dependencies that aren’t that easy to spot with a human eye. Recommendations particularly shine when applied to potential customers — unknown users or visitors who haven’t purchased before.

By analyzing their behavior in real time, businesses can push them toward a purchase by updating ‘recommended’ items based on the already viewed products.

Leverage Customer Care

Providing a high level of customer support is another great, though overlooked, way to exploit big data and increase brand loyalty.

In order to successfully utilize big data in customer care, you need to democratize access to that data. All your support representatives should have access to customers’ information, their purchasing history, and advanced analytical capabilities — the data sets that are usually a privilege of sales, marketing, and C-suite.

These big data layers have to be presented in a digestible format, as customer care representatives have limited time to act upon customers’ issues.

For example, customer care agents can personalize customer experience in the following way:

• Use a customer’s purchasing history to offer relevant discounts and promotions.
• Use other customers’ purchases in the same demographic group to upsell with related products.
• Analyze purchasing behavior and single out products or customer categories that experience the highest number of issues. This piece of intelligence will help to preemptively address such customers’ concerns.

Build Up Loyalty

How do you become friends with somebody? You create a relationship. They learn something about you. You learn something about them.

Customer loyalty is similar. Friendly relationships with your ecommerce customers are important. New customers mean business growth, but loyal customers are who keep businesses alive. Big data helps to build a rich profile for every one of your customers and then use it to develop relationships with them.

You can personalize promotions by analyzing the product types your customers purchase, including special promotions based on the size, color, and type of the products they buy.

For example, if a person has bought a blue t-shirt of a specific size and type (let’s say, V-neck), then you could offer them a prompt discount for this particular type of clothing, taking into account their common frequency of purchasing these items in your store. Such recommendations create a sense that you know the customer, and they’ll be more likely to purchase as long as you’ve offered them exactly what they’re interested in.

Another great way of building up loyalty is to offer localized discounts or promotions using customers’ billing or delivery addresses. This localization could be anything, from a promotion based on their local holiday getaway to a special delivery discount built around their locale.

Dive into Unstructured Data

Structured data is stored in categories in a database and includes all the information about customers required for a business to operate. Unstructured data can’t be categorized in the same way. There’s no specific row or column that you can find in your database containing that information.

Usually, when we talk about unstructured data, we talk about information that’s stored outside of your site. Social media publications about your brand are an example of unstructured data.

However, you can collect this information using monitoring tools. For example, certain digital services can collect mentions of your brand across the web. This data can help you analyze the sentiment around your brand name, hint at what people are looking for when trying to interact with you, and even point to the products that might interest them. You can use this data to build content around specific products and customer needs.

Use Time to Your Advantage

Some big data initiatives have to be built around timely execution. Shoppers have a short attention span. That’s why it’s crucial to grab their attention as soon as possible by offering the products they need at the moment.

Such real-time personalization is possible with big data. The principle here remains the same — take all the information you have on your user and build a personalized shopping experience.

These efforts have to be intertwined with an advanced analytical framework, such as one built on data science and machine learning. These technologies can help you adjust your tactics on the go and make an impact within a single user session.

There are a number of services, such as Evergage, Marketo, or PureClarity, that can personalize recommended products in real time based on visitors’ location. For example, these services can see that people from a specific location tend to purchase specific products. They then display those products in recommendations or trigger the connected web store to offer a special discount for those products.

Final Words

Even the smallest ecommerce companies have a plethora of data beyond the usual analytics offered by Google and other well-known providers of business intelligence. If you don’t act on the data that you already have, you might be fighting an uphill battle with your competitors already using such data to their advantage.

It’s not hard to start building personalized experiences for your customers. Start with enriching their profiles with personal information. Then try to use this information for delivering personalized recommendations. Once you see the ROI growing, switch to collecting unstructured data and applying AI-powered services to sieve valuable insights.