How to optimise your retail sales data
Retail Sales Data Analysis
How Lexer can help you optimize your retail sales data
A sale is, in the most basic sense, revenue for the company that makes it. What businesses have been learning in recent years, however, is that each item purchased also holds the key to generating the next sale and many more in the future.
This is made possible by new advancements in business software and a system called retail sales data analysis. These programs work by investigating points of contact between customers and businesses, gathering data from these interactions to better understand shoppers. By running this information through a complex system of retail analytics tools, business leaders are provided with key insights into the wants, needs, and preferences of their customer bases. This enables them to deploy new strategies and make improvements to their business models to drive satisfaction and loyalty. With happier customers, companies realize higher sales and effectively foster consumer loyalty.
E-commerce has been especially central to this, since online stores are able to generate huge amounts of customer and sales data and are also becoming the most common place to shop. With personalization now a hallmark of online shopping—customers are expecting the businesses they patronize to know more about them and respond to their needs almost intuitively. This is only possible through large-scale data crunching, and companies without these capabilities are quickly feeling the strain of the competition.
Physical businesses must also pay attention to these trends, however, since social media has now accelerated trend-cycles in fashion, gaming, entertainment, and numerous other sectors. Companies must quickly and instinctively know how to shift their brand image and product lines to meet emerging interests and demand.
Both on- and offline, this new software is quickly becoming the norm, and having an effective data platform is going to be crucial to success in coming years as both the technology and the expectations of customers develop and change. This article will explain the basics of retail data analysis and what it takes to turn information into sales.
Retail store analysis report
The end goal of sales analytics is to create a retail store analysis report which management can use to improve how their business operates. Retail store analytics is a complex process, and there are several steps the raw data has to go through before retail store reports can be created.
The first step is to collect all the sales data and other information generated at the various touch points of your business. These can include checkout, your online store, ad spots, and more.
Next, all this raw data has to be funneled into a customer data platform (CDP), a huge database containing individual profiles for each customer and prospect. Every new piece of data is automatically sorted into one of the profiles based on where the data came from, creating a detailed picture of your entire customer base.
At the next step, all this cleaned and consolidated data is run through analytics programs. These tools perform a variety of different functions, looking for meaningful patterns and connections in the data. These conclusions can relate to demographics, emerging trends, ad engagement, and much more.
Finally, these insights are compiled in retail store manager reports. Here, business leaders can look at all the data in simplified form and get a bird’s eye view of what their customers are doing. From there, they can identify opportunities to enhance customer experiences and target areas in need of improvement.
Retail analytics use cases
The data analysis process is flexible and can be applied in a number of retail analytics use cases.
Demographic insights are an especially important product of retail analytics solutions. Understanding who your customers are is essential to good business and also helps to determine how they will respond to your business model. Analytics programs can tell you whether your base is mostly women or men, teens or adults, frugal or wealthy, and more. All these factors will affect what kinds of products they buy, when they buy them, and how much they spend.
Analytics can also be used to monitor shopper behavior overtime. The software can take data from hundreds of locations and produce information on seasonal sales fluctuations, churn rate, expanding and contracting market segments, and more.
Forecasting is of high value in all business contexts, and one of the biggest uses for analytics programs is in predicting how data (and customers) will behave in the future. Knowing what people are going to need and be interested in tomorrow, next month, or next year is essential to staying competitive and allocating funds and resources intelligently.
The internet is filled with retail analytics case studies with practically endless examples of what these tools can be made to do. Though sales data is what makes the whole system work, business leaders can’t take meaningful action without analytic insights.
Retail analytics examples
To make better sense of the capabilities of retail analytics techniques, some real-world examples may be helpful.
A sportswear company examines its sales data from the past year and finds that more customers tend to come in during the summer, but sales of most brands tend to stay even throughout the year. Using its analytics software, the company determines that the summer months see a large influx of younger buyers in the age range 14-25. Management deduces that while there is a larger market for exercise gear, swimwear, and other summer essentials, the prospects who want them can’t afford the better brand lines they usually carry. Management decides to begin stocking several lower-priced lines during the warmer months, and overall revenue during summer increases.
An electronics retailer examines its sales data from its physical locations and uses its software to integrate it with purchase and search data from their online site. It finds that purchases of used equipment and devices are much higher in-store, while most new items are bought online. By analyzing the content of online reviews left by buyers, management realizes that people prefer to buy used gear in-store where they can appraise its condition and functionality. Likewise, purchasing new items online is quicker and avoids the in-store rush on recently released items with limited inventory. The company decides to only sell used products online, making more room in-store for new inventory. Product shortages decrease, and sales rise.
These retail analytics examples only skim the surface of what is possible, and many problems and challenges encountered in the sales sector can be remedied through better data engagement.
Data analysis in retail industry
Big data analytics in retail market settings is becoming the new norm, but retail itself is an extremely diverse sector. Each different business has its own unique customers and creates different kinds of data in the process of serving them.
The impact of big data on the retail industry has given rise to multitudes of software companies producing specialized tools to fit every use-case. Many companies elect to pick and choose a number of tools to fit their specific needs, combining them into a single system with extended functionality.
This method can be helpful for companies with specific needs or unorthodox business models, but it also comes with drawbacks. Integrating dozens or scores of proprietary software programs can take a great deal of time and resources and requires frequent troubleshooting.
Lexer takes a different approach, offering a single, comprehensive system covering everything from data capture to organization and cleaning to analytics and dashboards. Having everything in a single pre-built package eliminates the need for compatibility testing while providing enough customizability to fit the needs of any company.
Data analysis in retail industry settings will only become more common, and Lexer’s system is the perfect solution for any companies looking to enter into the world of sales data analysis.
Types of retail analytics
Finally, before beginning to set up your own system, it’s important to understand the types of retail analytics possible for businesses with and without an online presence.
In a traditional brick-and-mortar location, most business analytics in retail industry are obtained through the POS system. Here, information is collected on what kinds of things customers buy, when they shop, and how much they spend. Depending on how they pay, personal information can be linked with these sales and later combined in the CDP. Other data can be obtained by tracking inventory and, in some cases, through personal interactions between customers and employees. This latter kind of information is often hard to quantify but can be manually input into the system to produce qualitative insights to complement POS data.
Online, similar information about purchase size, frequency, and content is also available. However, with the search function and individual customer account info, businesses can look through and analyze all the items customers have searched and viewed in the past. Companies which place ads on other sites can also track customer engagement with these promotions, helping them decide which sites to target in the future.
Regardless of which types of data a company has access to, integrating sales data analysis into your business model can produce key insights, and businesses of all kinds should be familiar with the kinds of data they’ll be able to leverage.
Retail analytics reports
The end goal of sales data analysis isn’t the retail analytics reports themselves, but the action they make possible for upper management. Marketing and retail analytics are, in the end, only an effective means of better understanding the customer.
When management sees what customers are doing, thinking, and above all buying, they have the chance to make big changes. They can roll out new promotions, introduce special memberships, develop new ad campaigns, and alter their inventory and brand image—all to enhance and improve the experience of the customer.
The software and retail analytics companies are currently using will only become more widespread in the future, changing what it means to be both competitive and successful. Lexer’s data analytics system offers an easy and intuitive entry into the new data landscape, enabling better business through the same sales that already undergird all retail companies.
Click here to learn the top 15 reasons customers choose Lexer as their preferred CDP partner and vendor.
📄 Customer Data Platform (CDP)
📄 Customer Intelligence Platform
📄 Retail Data Analytics Solutions
📄 Customer Experience in Retail