A guide to retail data systems

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Retail Data Analytics Solutions

What are retail data analytics solutions?

Each day, retail businesses see hundreds, thousands, or even tens of thousands of people come into their stores, shop around, browse items they like, ask questions, make decisions, and purchase products. Even with all this interaction, however, it can often be hard for these businesses to connect with their customers—to understand what their interests are and what they want and expect from a shopping experience.

As the pandemic recedes, people are going to be working more regular hours, earning more, attending more social functions, and generally shopping more both online and in person. Taking advantage of these coming changes and providing improving the customer experience in retail stores and digital channels is going to be vital for the success of all businesses going forward.

Luckily, there is a quick and easy way to connect with customers in order to craft and deliver the kinds of products and experiences they want. It’s called retail data analytics, and it allows businesses to use all the customer data created by interactions with their brands to improve their relationships with shoppers, grow customer lifetime value, reduce churn, and more.

Using data analytics in retail industry settings is becoming more and more common as the technology behind it gets better and online shopping and browsing become larger parts of people’s lives. Retail data analytics solutions can work for businesses of all sizes in any segment of the market, and as a result, these tools are becoming ubiquitous and vital to staying competitive and profitable.

We want businesses to understand these tools and be able to use them to do better business. In this article, we’ll go over how customer data analytics works, how it can be applied to both in-store and online shopping, give some examples of real-world use cases, talk about some of the major players, and give an idea of the benefits of collecting and analyzing customer data can have for both you and your customers.

Retail analytics tools

Analytics in retail industry settings focuses on obtaining and analyzing all the data created through each interaction between a customer and your business either online or in person. Generally, it works something like this:

First, specialized software applications connected to your POS system, web store, and advertising platforms will gather information about shoppers. How often they shop, what kinds of products they buy, how much they spend, and which ads they respond to will all be tracked. This data will then be sent to a customer data platform or CDP. The CDP stores all this info and sorts it into individual customer profiles, also known as a single customer view, for easy access and efficiency.

From there, many CDPs use machine learning models to analyze the data and make it usable by your marketing, retail, and service teams. Retail dashboard programs will condense all the data into simple and straightforward graphics and charts so that you can keep track of any important shifts in customer behaviors or satisfaction. Analytics software will monitor data trends and generate models to predict what customers are going to do and want in the future. Using these predictive analytics, business owners can better understand their consumer base and work toward building a better, more personalized customer experience for them. At the same time, these retail analytics tools can also help upper management quickly respond to and remedy any complaints or sources of dissatisfaction.

Retail store analytics

While both physical and online retail store data are important to crafting better shopping experiences, different types of data are produced at each location.

When customers shop at an online store, it is easy to keep track of every item they look at, how long they view it, and how long they take to decide whether or not to purchase it. You can also find out exactly what kinds of products they are in need of by looking at the things they search. As well, you can use ad and affiliate link data to determine what sites they are most likely to see and interact with your ads on as well as what their search engine of choice is. Finally, more basic data like the frequency, recency, and monetary value of customers’ purchases will be readily accessible. In particular, one of the top benefits of retail store analytics is the ability to measure customer lifetime value, a key indicator of overall business health.

You can also gather basic purchase data in-store from your POS system. What sets a physical store aside from online shopping is the possibility of face-to-face interaction between customers and employees. Online reviews can let businesses know buyer opinions on specific products, but it is only in a store that they can learn how a customer feels about your business as a whole—why they shop there, how they feel about the service and product selection, and what could be improved. This kind of qualitative data can be recorded by employees in retail store reports and is useful in crafting a better shopping experience.

Some of your collected data is bound to overlap, but collecting as much as possible from all your business touchpoints is the best way to ensure your retail store analytics generate actionable insights that lead to tangible results. By using a CDP to combine retail data with omnichannel metrics, you can gain a better understanding of the entire customer experience you offer.

Retail analytics examples

To better understand how all these kinds of data can be used in the real world, it is beneficial to look at some retail analytics use cases.

Imagine that you run a store for outdoor and sports gear. To find out how you could better serve customers while shopping in your brick-and-mortar locations, you instruct employees to ask people about their shopping experiences at checkout. You also tell them to prompt buyers to fill out an online survey using a clienteling tool. These data are analyzed, and the results show that customers would like more employee assistance while shopping, as many of them are unsure of the components they need or how to fix problems they are having. You hire a few more salespeople and train them to walk the sales floor, offering assistance. As a result, sales rise.

You own an online fashion and apparel store. You install a program to locate your biggest spenders and most frequent shoppers. You then offer these shoppers special discounts and target them with personalized ads about upcoming sales. Before long, they start shopping even more and buying more products. They also encourage their friends to shop with you, and this results in a wave of new shoppers.

These retail analytics examples are only a small fraction of the applications that retail analytics solutions can be useful for. This should also give an idea of how quick and easy it is to see measurable results. If you want to see other ways this software can be deployed, more retail analytics case studies can be found all over the web, including in Lexer’s CDP use case library.

Retail analytics companies

With all the potential benefits of data analytics for retail and ecommerce businesses, it should come as no surprise that the number of companies offering marketing and retail analytics solutions is staggering.

Because of the complexity of the process and the wide variety of different business types and models out there, most companies choose to focus their efforts on individual types of retail analytics. Some providers work to create the software which gathers data. They work on special applications that are easily added on to your online and in-store POS systems to capture data about purchases and shopping patterns.

Some companies specialize in storing and displaying this data. These CDP vendors keep all your customer data in one place as a single customer view and dashboards and visualization tools to present this info quickly and clearly to whoever needs to see it. This ease of use allows business users to independently perform the kind of sophisticated analytics that was previously reserved for data scientists. Note that there are 4 main types of CDPs, all with their own strengths and weaknesses. Learn more about measuring the impact of a CDP.

Still others focus on forecasting and prediction—creating predictive models of how customers will behave in the future so that your team is prepared for future trends and challenges. These predictive customer insights help you improve overall marketing effectiveness, improve retention rates, increase your margins, grow customer lifetime value, and more.

Some of the most well known retail analytics companies include Salesforce, Informatica, SAS, and Lexer. Lexer stands apart from the crowd for being one of the few companies to offer solutions for gathering, storing, presenting, analyzing, segmenting, and activating customer data. This easy-to-use, fully integrated platform allows you to gain unprecedented customer insights which inform your approach to improving customer experiences across channels and touchpoints.

Retail store analysis report

The most important part of gathering and analyzing all types of retail data is consistency. Retail data analytics isn’t just about collecting data; it’s about learning to understand what the data means and how to respond to it to improve your results. This can’t be achieved with just a few scattered snapshots from disparate data sources.

Your retail analytics dashboard is one of the most important resources for regular data tracking. Dashboards should be updated in real-time, so you can constantly monitor the data feed and respond to fluctuations in shopping patterns and revenue trends.

Managers can make small adjustments around the store, but one of their most important jobs is to create retail store manager reports. These usually contain a summary of the changes and trends managers have seen in the data from their own stores, but will also include their own opinions on what these things mean, what can be done to make improvements to the customer experience, and any extra information about things they have noticed or ideas they might have to make their stores better.

This info is delivered to regional managers, business owners, and other higher-ups. From there, more sweeping and large-scale changes can be carried out to revise and perfect the customer journey. Without retail analysis reports and consistent data monitoring, however, it’s difficult to discover opportunities for improvement, predict and avoid risks, and track your performance over time.

Retail analytics software

Customer satisfaction is key in retail no matter who your buyers are or what kinds of products you’re selling. This is why retail analytics software is so important. This technology allows you to take advantage of all retail data sources to capture and use the information generated by customer interactions with your business. With this, you can work to build a better and more efficient shopping experience which customers will love and which will drive sales.

These tools and these use-cases just scratch the surface of what retail analytics data can do, particularly when using a fully integrated, end-to-end customer data and experience platform like Lexer. Only you know the challenges your business faces and what it needs in order to see success. With all the software analytics companies and data programs out there, better shopping experiences and better business are within your reach. Learn the 15 reasons customers choose Lexer as their preferred CDP vendor.

Related Articles

📄 Customer Data Platform (CDP)

📄 Retail Data Integration

📄 Customer Intelligence Platform

📄 Customer Experience in Retail

📄 Retail Data Systems

📄 CDP Use Cases

📄 Retail Data Collector

📄 Data-Driven Retail

📄 Customer Data Management Software

📄 Data Enrichment Tools

📄 Customer Experience in Retail

📄 Customer Insight Tools

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