How retailers are slaying the big data beast

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Big Data in Retail

Why is big data in retail important?

Businesses have always made a point to get to know their customers. From the advent of the earliest storefronts, owners and employees have distinguished between and remembered details about their various shoppers. Regulars, debtors, and friends have been remembered by name and habits or else marked down to ensure payment, give credit, or personalize the experience the next time they come in to the retail store.

Today, however, memory will not suffice to keep track of the hundreds, thousands, or tens of thousands of customers who shop at the biggest national chains and online stores. There is now too much customer data floating around to be kept in order by single sales associates or marketers. This “big data” has been a challenge for many retailers who want to connect better with their customers, but find there are simply too many of them and too much information to keep track of.

In recent years, however, the problem of big data in retail industry settings has been solved by the development of customer analytics software. With this new technology, every available piece of info on each customer can be collected, organized, accessed, and activated with ease. This has allowed businesses to sell, advertise, and communicate with shoppers in ways never before possible.

The impact of big data on retail has been enormous. The retail industry before data analytics was limited to best-guess marketing and rock bottom prices as means of drawing in and keeping customers. Now, this technology is making it possible to increase customer satisfaction and increase profits at the same time. To learn more about the importance of customer data in retail, click here to read “10 must-know facts and statistics for retail and ecommerce.”

What is even more astounding is that this technology can be applied anywhere in the market. No matter what you sell or how big your business is, these tools are guaranteed to make you more successful. As a result, more and more companies are tackling big data, and developing your customer analytics maturity is becoming an important part of staying competitive and profitable. All retail businesses should now be familiarizing themselves with how this software works, what it can do, and how to invest in it. In the coming decades, big data is going to be larger than life.

Customer data platforms (CDPs) are one of the leading tools helping retailers collect and manage this data effectively. To learn more about the benefits and impact you could see using a CDP, click here to read “How to measure the impact of a customer data platform (CDP).”

Retail store analytics

Even with the rise of ecommerce, brick-and-mortar stores are still vital parts of many companies’ business models. Retail store locations also offer types and quantities of data which can’t be accessed anywhere else.

Perhaps the most important place in the store where retail data can be captured is at the register. Here, programs attached to your POS system can determine what kinds of products are being bought most frequently, how often customers are making purchases, and how much they’re spending each time. Retail store analytics can also figure out who the customers are that shop the most, how often coupons or other discounts are applied, how many people have memberships, and how often they’re used.

On the sales floor, more quantitative and non-numeric data can be obtained by employees. They can talk to customers, offer help locating and choosing products to buy, but also learn more about how individuals relate to and feel about your business. They can find out the kinds of products shoppers like, why they prefer to shop with you over your competitors, and what they feel could be improved about the shopping experience. All these pieces of information can be fed in manually to programs and systems built to handle more complex data, such as retail clienteling tools.

Whatever info cannot be obtained through checkout or person-to-person interactions can be captured by stock programs. These monitor how much of each item is in stock at any given time and can tell business leaders which products are hot at which times of year and which are staying on the shelves.

Together, all this data can be crunched and sorted through to give your team insights about what your company is doing right, and what could be made better. This is how big data problems are solved in retail sector locations, and with analytics software, businesses are able to find quicker and better solutions which make customers happy, reducing churn and increasing customer lifetime value.

Big data in retail examples

So how do you get from collecting data to increasing customer satisfaction and sales? To understand the process a little bit better, some big data examples may be necessary.

You run a store which specializes in high end outdoor and sporting goods. Many of your customers make quite large purchases, but visit infrequently. You install a program such as a CDP which tracks the recency, frequency, and monetary value of customers’ purchases. Another program then sifts through this data and finds out who the highest-value customers are. Your employees are then instructed to offer these shoppers a special membership which includes a store discount and a small percentage cash back on each item bought. The result is that the biggest spenders come in more often and run up even larger bills than before. The increase in purchases easily offsets the discounts and other perks and drives up profits.

You run a kitchenware and cooking supply store. You use your customer data system to keep track of how many times each customer shops in your store. This enables you to differentiate between regular shoppers and people who are visiting you for the first time. You then set up a system that gives coupons to new shoppers attached to the end of their receipts. You also train employees to remind new shoppers of these coupons. You tell them that they also get a further discount when they refer a friend to your business and they come in and shop. This pushes first-time customers to become regulars while also encouraging them to bring in even more new shoppers.

These retail analytics examples are just a glimpse of what can be done with big data when you have the right customer data and experience tools. In reality, there are numerous ways to make these programs work for you and many initiatives like these can run simultaneously to drive even bigger gains.

Benefits of big data in retail

The role of customer analytics in retail is to help businesses better understand customers so they can build better shopping experiences for them. Omnichannel data and metrics are collected about each individual customer, fed into analytic and predictive programs, enriched with third-party data sources such as Experian’s Mosaic, and then presented to managers and other leaders in simplified forms so they can figure out how to better measure and improve their effectiveness across marketing, sales, and service.

The benefits of big data in retail for businesses are many. First and most importantly, customers who are happier and more enthusiastic about your brand are going to shop with you more. This means more sales and greater revenue. The more your product line, prices, and customer service offerings can align with what shoppers really want, the more they are going to choose you over competitors.

Second, the more people shop with you and enjoy their experiences doing so, the more people they’re going to tell about your store. This means more people are going to shop with you than before. As a result, without putting any more energy and resources into marketing, you’re going to draw in new buyers.

Finally, because you are constantly paying attention to the wants and needs of customers, you’ll be able to figure out what they want before they even want it. This can protect your business against changing market landscapes and help keep you competitive against your rivals by reducing returns, improving retention rates, and growing CLV.

The data sources for the retail industry are immense, and there are always more ways to leverage this data. You can also assist these programs by offering retail data analytics courses to employees so they understand how they work and can take part in the process itself. Thus, with an investment of time and resources, you can supercharge your business while also keeping it relevant and competitive.

However, some retail analytics platforms, like Lexer, offer strategic consulting services so you don’t have to invest in external training for your team. By choosing a strategic CDP partner, as opposed to just a CDP vendor, you can maximize the benefits you get from the platform.

Data analytics in retail industry

Data analytics software can be applied to any kind of business from software companies to banking. Retail and ecommerce, however, is where this technology really shines.

Big data in the retail industry is special because of the close interaction that happens between customers and stores. Here, the data is everywhere and easy to make sense of. If customers buy a lot of a certain product, they like it and want more of it. If someone shops with you a lot, they are going to be more receptive to deals and discounts. Big data analytics in retail industry settings is, thus, one of the simplest and quickest ways to boost profits and expand your brand.

We’ve talked so far about big data in physical store settings. However, these programs work equally well or even better online. Direct-to-consumer, ecommerce, and other online marketplaces generate much of the same kind of data as in-person shopping, but with a higher level of detail. You may not be able to ask customers about what your store needs, but with customer analytics software, you can track every item they look at, encourage them to purchase items they didn’t buy the first time, and measure brand engagement with different ad campaigns.

To truly unlock the potential of data analytics in retail industry environments, combining online and in-store data is crucial. Big data means not just real-world collection and monitoring, but making use of the vast amounts of digital information available, too. One way to bridge the gap between online and offline data is by building a CDP in a customer data integration.

If you don’t have the resources to integrate your customer data yourself, consider choosing a CDP vendor like Lexer to do all the heavy lifting for you.

Retail analytics use cases

By now, you should be able to see the vast potential of retail analytics systems such as CDPs. Big data applications in retail settings can benefit all types and sizes of business, and retail analytics use cases are practically endless. With nothing more than a little software setup and training for employees and managers, you’ll start seeing big differences in engagement and profits in no time.

For more information about when you can expect to see benefits from your retail analytics tools, click here to read “Post-deployment timeline: What changes can you expect from a CDP?”

Just as there are countless use cases for this technology, there are a wide variety of software providers and applications out there. Lexer, the Customer Data & Experience Platform for retailers, is a leader in this field and one of the few which offers solutions for data collection, organization, prediction, and visualization. For those just starting out in the world of data or any looking for a more integrated and streamlined system to tackle big data with, Lexer is a perfect choice. The role of big data and predictive analytics in retail is to help you and your customers do better business together. The first step in this process is to find the right tools to do it with, and Lexer has a tool for every task.

Click here to learn the top 15 reasons customers choose Lexer as their preferred CDP vendor.

Related Articles

📄 Customer Data Platform (CDP)

📄 Customer Insights Platform

📄 Retail Data Solutions

📄 Customer Intelligence Platform

📄 Retail Clienteling Software

📄 Retail Data Analytics Solutions

📄 Customer Retention Metrics

📄 Data-Driven Retail

📄 Data Enrichment Tools

📄 Retail Data Systems

📄 Customer Experience in Retail

📄 Customer Insight Tools

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