The business environment for brick-and-mortar retailers is particularly competitive. Therefore, understanding one's own financial performance is extremely important. By continuously analysing sales data, retailers can uncover changes in sales, recognise seasonal patterns and identify new or long-term trends. This is the only way to tailor the product range to specific target groups, use resources effectively and make the right strategic decisions. For more details and greater significance, Retail Analytics provides even more data to analyse and increase sales.
Analyse sales data
Retailers use sales analysis to determine important financial data related to sales. Of course, it is not only absolute sales that are considered. Instead, other statistical company data is used to correlate it with the sales data. Important key figures include, for example, sales growth rate, product sales or average receipt value.
Total sales: The most important key figure is the total sales for a period. For an annual sales analysis, the annual sales are decisive. It is also possible to analyse sales data in terms of months, days of the week or times of day.
Sales growth rate: This key figure indicates the decline or increase in sales for a specific period. It allows retailers to keep track of overall performance when comparing different periods. It also gives them an overview of sales growth or declines in individual stores.
Average receipt value: Retailers can use sales data to determine the average value of goods or receipt value of a purchase. They can then compare this figure with previous years. The goal is always to increase the value of goods per customer purchase.
Product sales: Sales analysis also includes determining sales for individual product categories and products. If retailers also know which product categories have the highest profit margins, they can focus more on these product categories. An unprofitable product category with high sales but low margins tends to be scaled back.
Consult additional visitor data
Retail analytics enables even more detailed sales analysis. This allows retailers to find out how visitor traffic is distributed over different periods. This gives them the option of comparing the respective sales data with the number of visitors and actual customers. In addition, they can find out more about the age and gender of visitors. Customer segmentation based on this information helps to optimise product range design.
Retail analytics also provides data on average dwell time, shopper group size and walking routes. For example, dwell time can also be compared to sales figures. Walking routes, in turn, provide an indication of where there is still untapped potential on the sales floor and where it makes sense to place cross-selling and upselling products.
Increasing sales in brick-and-mortar retail
Cross-selling: Cross-selling involves offering customers additional products that are closely related to the product they initially selected. Sales data provides information about which products are often purchased together. Knowledge of visitors' demographic characteristics can be used to promote additional sales specifically targeted at particular groups.
Upselling: When upselling, retailers aim to offer customers a higher-value product. The products should always be placed in such a way that the higher-value products are also presented in a more attractive manner. Retailers use a prior walkway analysis to identify the optimal placement locations in order to increase sales.
Product presentation: To increase sales, fast-moving goods should be placed at the forefront of product presentation. In addition, high-margin goods are also particularly important in product presentation. Data-supported tests of different shop layouts allow conclusions to be drawn about the perfect location for such product presentation.
Personal advice: Sales analysis and personnel costs make it easier for retailers to see whether and how staffing levels affect sales. The more advice-intensive the goods are, the clearer the link between staffing levels and sales figures. Regular sales training is therefore a key factor in increasing sales.
Assortment design: The range and depth of an assortment depends on the target group and on the sales figures for individual products and product categories. An analysis of sales data and visitor numbers, structure and behaviour forms the basis for making data-driven decisions about the product range. By changing their product range, retailers can respond to demand behaviour, close gaps, improve the shopping experience for the target group or attract additional target groups – always with the aim of increasing sales.
Modern technologies support your store optimisation
Continuous analysis of sales data provides valuable insights into financial health and operational potential. Sales analysis also helps in the development of growth strategies and forms the basis for optimising operational efficiency. Retail analytics helps retailers to better understand and retain their target groups. It also makes it easier to identify potential new target groups. We would be happy to advise you on which technologies you can use to analyse your store visitors even better in order to achieve sales increases.
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