Customer Counting: How To Analyze Retail Store Traffic
Retail stores receive millions of visitors every year. The primary objective of a store is to attract visitors and make sales. However, this constant stream of traffic can be leveraged to create an alternate source of value for retailers. You can collect traffic data and extract key retail analytics from it. This provides insight into customer behavior and preferences, and by identifying common trends, you are better placed to maximize store traffic.
Consider this: you manage a furniture showroom, and store analytics indicate that 75% of your visitors are women. The data also shows that most of your prospects (~60%) visit between 10 a.m. and 3 p.m. on Saturdays. Based on this information alone, you can create a lot of sales opportunities and optimize store operations. Some retailers have constant access to details like these because they actively collect and analyze store traffic data.
Surprisingly, the National Retail Federation (NRF) estimates that only about 47% of retail businesses leverage customer analytics. If you manage a retail store and you’re wondering how you can analyze and leverage store traffic, the guide below should help:
Counting and Analyzing Visitor Traffic
The first step is to calculate and analyze visitor traffic data. This provides an overview of how many people visit your store, your busiest periods, and other pertinent information about your visitors.
- Peak/off-peak periods
This is a crucial metric because it provides insight into customer behavior. If you know what times to expect increased visitor traffic, you can prepare accordingly and make the most of the extra footfall.
Customer counting technology is required to identify a store’s busiest periods. The software analyzes historical data from traffic counters to determine the hours, days, or seasons that prospects are likelier to visit. V-Count is the world’s foremost provider of this software.
- Capture rate
With data from Street and Customer Counters, you can determine your store’s capture rate. This is calculated by comparing pedestrian traffic to walk-ins. If 1000 people walk past a store and only ten people enter, that’s a capture rate of 1 percent—quite low.
This may indicate that the store is not attractive to pedestrians. The manager may address this by devising strategies to boost walk-in traffic. Perhaps improve external/window displays or offer discounts. A higher capture rate means more visitors in the store.
- Customer demographic
V-Count also offers retailers a Demographic Analysis software. With this, store visitors may be separated into categories based on their ages and genders. This information can be used to optimize product listing.
Say you manage a footwear store, and traffic analytics show that young men between the ages of 18 and 23 are your prevalent customers. You need to list products that appeal to that demographic to ensure sales and better traffic conversion.
Analyzing In-Store Traffic
The second part of retail traffic analysis focuses on how customers behave in the store. By identifying behavioral patterns and customer preferences, retailers may boost sales by simplifying the buying process. You may also leverage in-store analytics to create new selling opportunities.
- Customer path
Heatmap technology makes it possible to analyze customers’ paths through a retail location. Analytics from the software provides insight into how customers move around in the store. It highlights busy walkways and helps retailers identify bottlenecks in customers’ journeys. Bottlenecks need to be resolved, and adverts may be placed along busy paths to boost visibility and engagement.
- Traffic per store section
Heatmap analytics can also be used to identify hot zones—the sections that see the most visitor traffic. These sections hold more sales potential than any other, making it ideal for promoting new products. You may also need to deploy more staff to the sections during peak periods to maximize traffic and increase sales.
- Average dwell-time
Retailers can get in-store customer tracking sensors from V-count. Data from these sensors can be analyzed to measure average dwell time, a measure of how long customers spend in a store.
Studies have shown that the longer the time prospects spend in a store, the higher the likelihood that they’ll make a purchase. There is also a higher likelihood that they’ll spend more. If analytics show that customers don’t linger in your store, you must implement strategies to increase the average dwell time.
The beauty of retail analytics is that there are a lot of metrics to track. Additionally, optimizing just one metric has the potential to boost business performance significantly. For example, doubling average dwell time has been shown to increase sales by up to 30 percent. With so many metrics to measure and optimize, it is much more effective to use retail technologies to facilitate the collection and analysis of traffic data.
V-Count provides a suite of these technological solutions, and retailers from all over the world are already taking advantage of their software. To learn more about retail analytics solutions, visit V-Count.com.