Retail analytics help retailers make data-driven operational decisions. The focus is not only on the back end with supply chains, procurement and warehousing, but above all on activities on the sales floor. This enables a holistic view of the individual retail environment. Data on customer structure, customer behaviour, sales figures and stock levels are therefore the important foundation for the continuous optimisation of a location. What are the current retail analytics trends?
Collect data on customer behaviour internally
Online retailers have always used the data they collect about visitors and customers very successfully. This data helps them understand their customers better and develop personalised customer strategies. Data-driven customer strategies are also beneficial for brick-and-mortar retailers. To further develop their business model, retailers should not rely solely on external data from third-party providers. Internally collected data is essential for revealing business potential and opportunities and for monitoring changes. A combination of external and internal data is the starting point for competitive business development. This trend is actually quite obvious. But too few retailers are paying enough attention to it.
Apply dynamic pricing strategies
Retailers take many factors into account when setting prices. These include demand, seasonality, competitor prices, stock levels, promotions and profitability. Price setting is already highly dynamic. However, retail analytics provide retailers with even more information about how customers interact with individual products or product groups on the sales floor and the demographic structure of the interested customer base. Retailers can then use this additional knowledge to adjust prices to specific target groups with the help of dynamic pricing algorithms. In conjunction with digital price labelling, price adjustments are even made automatically without any effort.
Integrate omnichannel data
The individual branch is not the only touchpoint customers have with a brand. Many touchpoints in the online, media and mobile world provide specific data that retailers can use to create an exceptional shopping experience in a store. Whether it's a third-party marketplace, shopping app, social platform, search engine, web shop or physical store, retailers are forced to converge their channels. The trend is therefore moving towards a holistic omnichannel approach. This enables a better understanding of customer behaviour and optimises assortment design, advertising strategies and product presentation.
Optimise shop layout
Optimal product placement is one of the most important tasks for management in order to stimulate impulse purchases and increase sales per customer. That is why more and more retailers are turning to data protection-compliant video recordings to analyse customer behaviour in detail. This allows them to record how customers move around the sales area and where they particularly like to spend time. In addition, they can also analyse which products customers are interested in. To design an optimal shop layout, companies can then test different variants and compare them with each other. Retail analytics provides the necessary data for this.
Utilise the Internet of Things
The networking of products, cash register systems, shopping trolleys, shop fittings and security systems via the internet enables new business processes and a better shopping experience. This is because sensors can be used to monitor and track all processes on the sales floor, as well as in the warehouse and logistics. For example, apps make it easier for customers to find the products they want in large sales areas. In addition, retailers are then able to advertise additional offers via digital displays based on the products in the shopping basket. Furthermore, networking the sensors in shelves and storage rooms can optimise the efficiency of inventory management.
Predicting customer preferences
More and more retailers are also using big data to predict future customer behaviour. This is because the large amount of historical and real-time data provides a detailed insight into customer preferences and expected customer behaviour. Machine learning and algorithms help with the evaluation. This enables retailers to make better short-term and long-term decisions. For example, they can use retail analytics to identify signs that customers are turning away from a brand. In other practical cases, a shorter dwell time for certain age groups may indicate that the available goods are less suitable for this target group. Predictive analytics also provides important support for optimised staff scheduling in large retail spaces.
Shaping the future of retail
In today's retail landscape, data is king. AI technologies help to consolidate and evaluate large amounts of data from different sources and derive new insights. The transformation is already in full swing, and individual retailers are already addressing the trending topics with varying degrees of intensity. Large corporations and innovative retailers in particular are doing pioneering work. Nevertheless, too many retailers are not yet exploiting the potential. Contact us to find out how you can remain competitive and meet customer needs with retail analytics.
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