The future of retail includes technologies, such as footfall counting, and platforms that make footfall predictions. However, while highly sufficient for the retail industry, these solutions can be applied just about anywhere. You can use footfall counters and predictive data in healthcare, education, airports, and in other spaces where needed.
This valuable data and insight help organizations identify new opportunities, understand their visitors (and customers), and much more. For instance, you can even use footfall counters and predictive analytics in public restrooms to automatically trigger maintenance operations.
What Is Footfall Analytics?
Footfall counters measure how many people enter the facility, and footfall analytics derives valuable insight from that data. This approach helps the organization understand visitor or customer habits, behaviors, demographics, and other relevant information.
Some key performance indicators (KPIs) for customer behavior include:
● The total number of visitors
● Proximity traffic
● Path analytics
● Capture rate
● Sales conversion
For decades, companies physically placed staff at the entrance to count the number of people walking through their door manually. Employees would use a clicker to keep count and report it at the end of their shift. However, this approach was not only prone to human error, it was also extremely time-consuming, and it kept staff away from more important tasks. Today, enterprises leverage multiple footfall counting technologies, such as door counting sensors, video counters, thermal imaging, and WiFi people counters, to fix the issues of the past.
What Is Footfall Prediction?
Footfall predictions are forecasts made by analyzing past and present data. This approach helps accurately predict trends based on data like footfall traffic, weather, sales, and more.
This information is collected, cleaned, and analyzed with the help of the smart sensors, such as the Internet of Things (IoT), or artificial intelligence (AI), machine learning (ML), and data analytics. Once you have collected enough data over weeks, months, and years, you can leverage this information to make footfall predictions.
Predictive data helps you manage inventory, place products that people tend to buy together, and make accurate sales forecasts. Footfall traffic and customer spend are closely connected. Therefore, footfall predictions are highly precise in anticipating future sales performance.
How Do You Use Predictive Data and Analytics?
When you leverage predictive analytics, you have a real opportunity to drive profit. When used correctly, you can optimize and enhance customer-facing and operational functions.
For instance, as a retailer, this type of forecast can tell you what a customer did last summer and what they will most likely do again during this summer. Additionally, you can understand how customers browse through the store, their mood when entering and leaving the store, dwell times, and more.
So, what else can we get from AI-powered footfall predictive analytics? Below you will find some of the most prominent qualities of predictive analytics.
Improved Inventory Management
You do not have to wonder about what inventory you need to store and what you do not. Based on footfall, sales, and revenue metrics, retailers can quickly visualize and understand when to replenish the stock of a product by accurately predicting demand.
However, this alone is not enough. You can also leverage footfall and other data to understand where to offer a new product to boost sales and revenue. When you do this, you ensure that the products, customers are looking for, are available and supported by dynamic pricing. When businesses do this successfully, their inventory costs go down while sales and profits rise.
Dwell Areas and Times
With the help of IoT sensors and AI-powered footfall analytics, you can identify dwell areas and times and target those locations with promotions and advertisement. By analyzing footfall data with information gathered from several touchpoints (both online and offline), companies can anticipate the customer’s needs and when to target those.
For example, you can set up a pop-up store or an advertisement in a dwelling area and target specific customers with personalized promotions on their smartphones or on digital screens nearby.
Enables Smart Staffing
You can use footfall predictions to get the most value from your staff. For example, you leverage people counting data to avert over- or understaffing. This approach helps reduce labor costs, boost conversions, and even enhance employee experiences.
Compare and Target Different Segments
Footfall traffic comes in many forms, and you need to figure out which type of data you want to utilize. For example, you can analyze footfall traffic at a specific time of day to understand customer behaviors and cater to these appropriately.
Some people counting data that companies divert their attention to include:
● The average time customers spend in your store: Solely counting the number of people walking into your store is not always enough. You can determine how much time the average visitor spends inside your store and at what location they spend most of the time. This approach can help you understand which areas are not visited and strategize how to change that.
● Bounce rate: This is essential because these are the visitors who walked in and out of your store within a few minutes without buying anything. This data can help you understand why you are losing potential customers amongst those you have already attracted. This insight can, then, be used to strategize and make predictions on how you can engage them in more efficient ways.
● Loyal versus new customers: Some footfall counting- and analytics tools help you track returning customers. This information can tell you a lot about the overall health of your business. Whenever retailers focus on this type of traffic, they also gain deeper understanding of the company’s long-term potential. For instance, if you are not attracting returning customers, you are most likely failing to build brand loyalty, which could potentially result in a slow demise of your business.
● The number of by-passers: The people who walk by matter as you want them to come into your store. By counting the number of people who pass by and combining such data with demographic data, you can make changes to attract them.
As demonstrated, footfall prediction- and analytics are much more than the sole concept of people counting. You can use this information to optimize different aspects of your operation to improve efficiency, increase sales, and deliver enhanced customer experiences.
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