Hundreds, thousands, tens of thousands of customers pass through a retail store every day. However, many retailers do not have a complete picture of what is happening on their shop floors. This is because visitors’ movements get lost amongst the shelves, stands and displays. Sales figures per customer, item and product group are calculated, but what happens before a purchase is made remains a mystery. Visitor behaviour is often not yet systematically recorded. Innovative camera and sensor technology makes it possible to eliminate this blind spot and collect valuable data. With Retail Analytics, brick-and-mortar retailers gain an integrated view of visitor flows, trends and overall business operations.
Use retail analytics to identify and analyse customer demographics and behaviour
More and more retail businesses are making professional use of retail analytics. This growing use is a response to the changes taking place in the retail sector. These changes are primarily driven by digitalisation, shifts in consumer behaviour, new competition from innovative retail concepts, high rents and increased pressure on margins. Retail analytics provides retailers with valuable insights into visitor numbers and behaviour within their premises. These insights enable further optimisation of store locations and an increase in profitability.
Key performance indicators for site optimisation
Capture rate, customer journey, dwell time and conversion rate are key metrics for any retail business. This is because they serve as a vital tool for optimising store performance. The following examples illustrate the value of these metrics for store optimisation.
Understanding the customer base to tailor the product range
Modern sensor technologies can be used to determine age and gender in compliance with data protection regulations. It is also technically possible to distinguish between adults and children. Furthermore, the software also identifies the size of individual groups. The detailed analysis of customer demographics provided by Retail Analytics enables the product range to be tailored even more effectively to target groups. Detailed insights into customer demographics also allow for more targeted advertising campaigns, leading to higher sales.
Queue detection for greater customer satisfaction
Furthermore, store optimisation can be achieved by reducing long queues at the entrance, at the checkout or at service counters. This is because long queues have a negative impact on customer satisfaction and can also lead to abandoned purchases. Using retail analytics, queues can be analysed in real time, enabling retailers to deploy staff when queues exceed predefined limits.
Insights into visitor numbers for better staff scheduling
Staff numbers must be adjusted according to footfall. Having too many staff when footfall is low results in unnecessary costs. Conversely, if there are too few staff when footfall is high, service quality suffers. Retail analytics also enables predictions to be made about footfall. If retailers are aware of peak and off-peak times, they can deploy their staff more effectively. Retail analytics is therefore a key component in the site optimisation of a retail business.
Overview of dwell times and customer flow paths for efficient product placement
If product placement is not to be left to chance, retail analytics can be a great help. Analysing customers’ movement patterns and dwell times provides the perfect basis for optimising the layout of a particular store. The optimal arrangement and placement of products is an ongoing process that can be systematically managed using retail analytics.
Use retail analytics systematically and on an ongoing basis
In order to optimise store locations using retail analytics, data must be recorded over an extended period. This is because the data collected should be free from natural fluctuations and random variations. It therefore makes sense to develop a plan for implementing retail analytics and carrying out optimisation measures.
Setting goals: First, retail companies must set specific goals regarding which areas and metrics they wish to influence. Suitable areas and metrics include, for example, the conversion rate or the time spent on site.
Recording a baseline value: It is important to carry out a baseline measurement over several weeks in order to obtain a meaningful baseline value. Only then is it possible to compare new figures against this baseline once optimisation measures have been implemented. When selecting a period for the baseline measurement, retailers should choose a time that is not influenced by factors such as the Christmas shopping season or major advertising campaigns.
Implementing optimisation measures: In the next step, individual parameters are adjusted in one or more branches. For example, changes can be made to the shop fittings, the layout, the product range or the window display, and specific promotional activities can be carried out.
Evaluating results: Once the optimisation measures have been implemented, the new figures are again recorded on an ongoing basis. The reference figures can then be compared with the new figures. Ideally, customer behaviour will have improved and sales will have increased. Analysing the data is always an ongoing process, meaning that site optimisation is also a continuous, long-term endeavour.
Are you ready for the journey of digital transformation and retail analytics?
Let’s turn your vision into reality. Contact us today to work with us on setting your brand on the path to data-driven management of your visitor spaces.
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