Retail stores welcome millions of visitors every year-end season, and a store's primary objective is to attract those visitors and turn them into sales. However, this constant flow of visitors can also be harnessed to create another source of value for retailers. You can collect footfall data and extract key retail analytics from it. This gives you a better understanding of customer behaviour and preferences.
By identifying common trends, you are better placed to maximise store footfall.
Take the following example: you run a furniture showroom and your store analytics show that 65% of your visitors are women. The data also shows that around 70% of your prospects visit the store between 12 pm and 3 pm on Saturdays.
Based on this information alone, you can create numerous sales opportunities and optimise store operations. Some retailers have permanent access to such details because they actively collect and analyse store footfall data.
Counting and analysing visitor traffic
The first step is to calculate and analyse your visitor traffic data.
This gives you an overview of how many people visit your store, your busiest periods and other relevant information about your visitors.
Peak and off-peak periods
This is a crucial metric because it provides insight into customer behaviour. If you know when to expect a rise in visitor numbers, you can prepare accordingly and make the most of the extra footfall.
Visitor counting technologies are needed to identify a store's busiest periods.
Demographic analysis
anavid also offers retailers a demographic analysis solution. With this solution, store visitors can be categorised by age and gender. This information can be used to optimise your product range. Imagine you run a sportswear shop and your traffic analytics show that young men aged 18 to 23 are your main customers. You should stock products that appeal to this age group to secure sales and achieve better conversion rates.
In-store traffic analysis
The second part of store traffic analysis focuses on customer behaviour inside the store. By identifying customers' behavioural patterns and preferences, retailers can boost sales by optimising the shopping journey. You can also leverage in-store analytics to create new sales opportunities.
Visitor flow
The heatmap solution lets you analyse your customers' journey through a point of sale. The software's analytics show how visitors move around a store. They highlight high-traffic areas (hot zones) and help retailers identify bottlenecks in the customer journey. Bottlenecks should be resolved, and slow-moving products can be placed in high-traffic areas alongside popular products to boost sales.
Zone traffic
Heatmap analysis can also be used to identify high-traffic zones, i.e. the sections that receive the most visitors. These sections have greater sales potential than any others, making them ideal locations for promoting new products. You can also deploy more staff in these areas during peak periods to improve service and increase sales.
Conclusion
With retail analytics, there are many metrics to track, and optimising just one of them can significantly improve business performance. It is far more effective to use an optimisation platform and accurate people-counting sensors to make collecting and analysing traffic data easier.