How to increase foot traffic in 2022 with Big Data?

Foot traffic is a key factor for the success of any retail store, the main step to increase it is to measure it. Only by counting and tracking it is it possible to identify the factors that affect and improve a retailer’s store footfall.

What is foot traffic?

Foot traffic is the number of customers entering or passing through a retail outlet. Generally, higher foot traffic leads to higher revenue. Thanks to Big Data, foot traffic can be measured using advanced analytics techniques and spatial data from anonymized mobile devices.

Who needs foot traffic?

Retail businesses rely on foot traffic for most or all of their sales, while businesses offering services such as maintenance or repairs tend to benefit more from the visibility provided by a high-traffic location.

Why measure foot traffic?

Accurately measuring foot traffic can be a difficult task. However, it’s an important task, as the results inform intelligent decisions regarding business location and store optimization. Foot traffic tracking methods range from very basic approaches to complete location analysis systems. Here are some additional advantages of foot traffic tracking:

  • Enable quantitative and qualitative information about customers by better understanding who the customers are and what their shopping patterns are.
  • Identify where most customers are located and where they walk.
  • Observe the days and times when outlets tend to have the most visits.
  • Measure the duration of visits and the time it takes to make a purchase.
  • Identify the most requested products or services.
  • Measure sales conversion rates.
  • Improve business decision making.

How to increase foot traffic?

More retailers are blurring the lines between physical and digital shopping experiences to increase sales and foot traffic. The more data is collected, the more feasible it becomes to detect patterns, measure POS performance and make labor planning and marketing strategies more cost-effective.

Once customer behavior is learned, strategies are generated to make business decisions and maximize revenue and visits at each point of sale, such as:

  • Staffing: Optimize the number of employees during the most visited hours of the points of sale, avoiding overstaffing and reducing unnecessary operating costs.
  • Product placement: Generate heat maps that help understand how customers move in and out of retail outlets, identifying high-traffic areas and optimizing space and improving strategic product placement.
  • Inventory planning: Helps project sales and predict the amount of stock needed to avoid loss, overstocking and out-of-stocks of products.
  • Sales: Allows planning sales and special offers during peak periods.
  • Expansion models: Generates strategies based on accurate data and predictive models for new store openings, allowing you to get a clear idea of how you can replicate success in a second or third location.
  • Marketing strategies: Allows you to measure and forecast the success of marketing strategies.

Also Read: “Case Study: Site selection Model for retail stores

One of the biggest failures of retailers is not keeping up to date with new technologies. If businesses would understand that it’s already possible to measure foot traffic, improve access and visibility of a store, they would obtain thousands of benefits from a powerful tool that generates competitive advantages.

At PREDIK Data-Driven we help retailers to make intelligent decisions regarding inventory, store layout, return on investment (ROI) of marketing campaigns, target market, shopping behaviors, visitation patterns and seasonality of their outlets.

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