Unlocking the Customer’s Mind With Retail Analytics

It was the same with big data analytics — retail was an early adopter. One of the primary uses is maximizing the value of their customer relationships. With big data, companies can improve outcomes for:

• Customer retention
• Order values
• Overall revenues

Big Data Analytics in the Retail World

When it comes to retail, data can come from anywhere. You name it, it can be measured: warehouse inventory, traffic patterns from brick-and-mortar stores, point-of-sales systems, customer surveys, sales receipts, the list goes on. Combining this data and drawing meaningful insights was once extremely time consuming and sometimes flat-out impossible.

But without this data, it’s difficult to understand online customer behavior. You might have the data, but without a way to analyze it, you might as well be blind.

Companies need to prioritize uncovering correlations and then connecting their customers with more targeted outreach efforts. By studying their customer behavior more closely, companies can develop an understanding of how customers think and how they can be convinced to make more purchases and higher value purchases.

Netflix: How a Simple Big Data Algorithm Changed an Industry

Let’s look at an example of how big data can yield competitive advantages. Back when Netflix was still known mostly for sending DVDs via mail, the company faced with a serious challenge: most customers only wanted to rent the latest releases, so Netflix had to spend huge sums acquiring recently released DVDs. After a few months, those DVDs would essentially lose most of their value as people stopped renting them and moved on to newer releases.

Netflix facilitated the creation of a recommendation algorithm that crunched vast quantities of data to match customers’ movie preferences with older movies they were likely to enjoy. By compiling preferences, correlations between ratings and other factors, Netflix built a highly accurate recommendation program that put older movies customers would likely enjoy right in front of their eyes.

Customers quickly started renting older movies, helping Netflix keep its library costs lower and squeeze the maximum number of dollars out of it. This, in turn, allowed Netflix to dominate the home DVD rental market, and still plays a vital role in the company’s ongoing home entertainment efforts.

As Netflix shows, big data and retail are made for each other.

Customer Behavior Analytics: Unlocking the Retail Customer’s Mind

Netflix’s algorithm, which we now all but take for granted, was a major breakthrough for the time. Yet the algorithm only predicts one behavior, the types of movies people like. Big data analytics in retail can provide numerous other insights regarding customer behavior. Customer behavior analytics allows you to understand how customers act across all of your digital and non-digital channels and interaction points.

By building an understanding based on such breadth and depth, you can better target your customers. This means you’ll craft better outreach efforts that are highly targeted for specific audiences, which in turn will improve results.

Fact is, customers are bombarded day in and day out with ads, messages, emails and other contacts. Over time, many customers have become all but immune to non-targeted messages, able to shrug them off without second thought.

By understanding customers’ behavior on the deepest possible level using vast quantities of data, business leaders can figure out the best way to interact with them. Interactions might include ads, emails or perhaps rewards programs, or a specific coupon sent at just the right time. With big data, you can start to map out customers and their journeys, and then you can craft strategies to control or at least influence this journey.

For example, when targeting customers, often it’s the timing that’s wrong, rather than the message. A coupon or offer sent at the right time can convince customers to open their wallets. Send that offer at the wrong time? It’ll probably end up in a digital or physical trash bin. In the past, marketers had to blindly throw darts at the wall and hope that something would stick. With big data, markets can analyze countless interactions, touch points and other pieces of data to uncover trends and correlations. Then they can figure out the optimal timing to produce results.

Not only can you convert more customers, but you can often improve the overall value and profitability of each sale. There are numerous ways to increase the monetary value of a customer’s purchase, such as upselling or customer retention programs. Big data can help you figure out which potential programs will produce the highest number of extra purchases.

Surfdome: Using Big Data to Make Retail Decisions

Consider Surfdome, one of Europe’s leading online retailers. The company focuses on delivering action sports products across Europe, and was looking for ways to increase the overall lifetime value of customers. They turned to big data to conduct more meaningful behavior analytics. By using big data, the company hoped to engage in highly targeted marketing, increase cross-selling and increase repeat purchases.

Before using a BI Platform (in this case, Datameer), the company was trying to use simple Excel sheets and other basic methods. The workload was simply too much, compiling everything was exceptionally difficult and time consuming, IT was being overwhelmed, and often, data was left in silos. By engaging with a big data BI platform, Surfdome was able to execute a shift both in the way data was collected, and also in how it was used.

 

This shift was actually driven by business intelligence and marketing personnel, instead of IT. Not only did this result in a reduction in implementation time, but it also meant the data itself and the way it was used was directly integrated within the business decision making process. Big data also allowed Surfdome to more accurately segment their customers, learn more about the customer journey and improve customer acquisition through highly targeted marketing.

Conclusion: Big Data Analytics and Retail Are Made For Each Other

Big data and retail are a perfect fit. Modern, big data-enabled BI platforms can help retailers understand their customers and their entire journey. Other tools, like Google Analytics, can provide some insights, but they can’t tell the whole story.

By using big data analytics in retail, companies can discover never-before considered correlations and integrate data previously stuck in silos. Big data is especially important for retail because competition for consumer dollars is fierce, and competitive advantages can mean the difference between success and failure. Further, with powerful BI platforms, business leaders no longer have to make blind decisions or decisions based on gut instincts. Instead, they can use data to make the most sound, logical and informed decisions possible.