The future of retail includes technologies like footfall counting and platforms that make footfall predictions. However, it’s not just for retail. You can use footfall counters and predictive data in healthcare, education, and just about anywhere.
This valuable data and insights help organizations identify new opportunities, better understand their visitors (and customers), and much more. For example, you can even use footfall counters and predictive analytics in public bathrooms to automatically trigger maintenance operations.
But before we get ahead of ourselves, let’s take a step back and quickly go over some definitions.
What Is Footfall Analytics?
Footfall counters measure how many people enter the facility, and footfall analytics derives valuable insights from that data. This approach helps the organization better understand visitor or customer habits, behaviors, 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 time-intensive, kept staff away from more important tasks, and was at risk of human error.
Today, enterprises leverage multiple footfall counting technologies like door counting sensors, video counters, thermal imaging, and WIFI people counters. Footfall analytics collects all this data to deliver a variety of insights.
What Is Footfall Prediction?
Footfall predictions are basically 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 Internet of Things (IoT) or smart sensors, artificial intelligence (AI), machine learning (ML), and data analytics. Once you have collected enough data over the weeks, months, and years, you can leverage this information (and data from other sources) to make footfall predictions.
This predictive data can help you better manage inventory, place products that people tend to buy together, and make accurate sales forecasts. Footfall traffic and customer spend are closely correlated. As such, 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 example, if you’re a retailer, this type of forecast can tell you what a customer did last summer and what they will do during this summer (surprisingly accurately). You can also take it up a level and better 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? Let’s take a look.
Improved Inventory Management
You don’t have to wonder about what you should store and what you don’t need anymore. 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 also ensure that the products they’re 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 better target those locations with promotions. By analyzing footfall data with information gathered from a number of touchpoints (both online and offline), companies can better anticipate the customer’s needs and the right time to target them.
For example, you can set up a pop-up store in a dwelling area and target specific customers with personalized promotions on their smartphones or on the digital screen in the kiosk.
Enables Smart Staffing
You can also use footfall predictions to get the most value from your staff. For example, you leverage people counting data to avert overstaffing and only increase staff and peak times.
This approach helps reduce labor costs, boost conversions, and even enhance employee experiences. After all, it’s better to work and engage customers than be overstaffed with no one visiting the store.
Compare and Target Different Segments
You can also take this data and do much more with it. Footfall traffic comes in many forms, and you’ll need to figure out which type of data you want to focus on. For example, you can analyze footfall traffic at a specific time of day to better understand customer behaviors and cater to them appropriately.
Some people counting data that companies concentrate on include:
• The average time customers spend in your store: Just counting the number of people walking into your store just isn’t 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 walked out within a few minutes without buying anything. This data can help you understand why you’re losing potential customers (who you have already attracted). Then you can use these insights to strategize and make predictions on how you can engage them better.
• Loyal versus new customers: Some footfall counting and analytics tools help you track repeat 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 a deeper understanding of the company’s long-term potential. For example, if you’re not attracting repeat customers, you’re failing to build brand loyalty. So, your business might not last very long.
• The number of visitors who walk by without entering: 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 analyzing it with other personal and demographic data, you can make changes to attract them.
The days of attracting retail customers with a direct mass mailer are long gone. Digital marketing today is far more sophisticated and essentially a two-way street. For example, the consumer has already shared their preferences, price sensitivity, in-store browsing behavior, and much more. You can use this information to target them better when they return to your store.
As you can see from the above, footfall prediction and analytics are much more than people counting. You can use this information to optimize different aspects of your operation to improve efficiency, increase sales, and deliver enhanced customer experiences.
To learn more about people counting and predictive analytics, reach out for a commitment-free consultation.