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Shopping Behavior Analysis

Everything about Shopping Behavior Analysis

     

What is Shopping Behavior Analysis?

Shopping behavior analysis refers to the process of gathering data on the actions of buyers in a retail environment, and then using that data to identify their buying preferences and patterns. Some of the factors that are considered during the analysis include:

  • The shopping environment and how shoppers navigate it
  • The number of buyers present in the shop at a given moment or during a certain time frame
  • The nature of the products
  • The cost of the products
  • The times at which people shop

 

This type of analysis enables marketing, sales, and logistics staff to predict market trends, which is useful when making buying decisions, setting up promotions, and designing store layout, for example.

What is Shopper Behavior?

Shopper behavior refers to the actions and emotional responses of buyers during the shopping process. In a physical shopping environment, aspects of shopping behavior that may be useful to observe include:

  • Which products draw the shoppers’ attention?
  • Which products or which elements of the store layout cause confusion?
  • How do shoppers navigate the store?
  • At which times do customers prefer to shop? And is there a noticeable demographic trend? E.g. do working-age people prefer to shop in the evenings? Or in the mornings on the way to work?
  • What emotional responses do people show throughout their shopping experience? What is the predominant emotion among shoppers at various points throughout the day and on different days of the week?

 

What is online Shopping Behavior?

Shopper behavior refers to the actions and emotional responses of buyers during the shopping process. In a physical shopping environment, aspects of shopping behavior that may be useful to observe include:

  • Which products draw the shoppers’ attention?
  • Which products or which elements of the store layout cause confusion?
  • How do shoppers navigate the store?
  • At which times do customers prefer to shop? And is there a noticeable demographic trend? E.g. do working-age people prefer to shop in the evenings? Or in the mornings on the way to work?
  • What emotional responses do people show throughout their shopping experience? What is the predominant emotion among shoppers at various points throughout the day and on different days of the week?

 

Also read: Online Shopping Behavior

Why Analyze Shopper Behavior?

There are many different aspects that affect shopper behavior, and some of these can be influenced by the retailer. Examples include: store layout, product placement, promotions, background music, length of the checkout lines, and availability (and helpfulness) of store assistants.

To find out how to optimize these aspects – and increase sales and enhance the customer experience – it is necessary to analyze shopper behavior to determine what produces the best results within a specific store.

There are various tools you can use to do this, and we will be exploring them below.

Tools for Analyzing Shopper Behavior

When it comes to analyzing shopping behavior in a physical store environment, crowd analysis tools that rely on face detection and face analysis are a good choice. These AI-based tools enable you to, for example:

  • Count the number of people entering a store
  • Record the times at which people enter a store
  • Identify the demographics, such as age and gender, of the people in a store
  • Track the movement of people within a store
  • Analyze the emotions of people as they shop

 

Sightcorp’s deep learning-based facial analysis tools enable you to do all this and more, while making sure that privacy is a priority.

Both CrowdSight Toolkit (an easy-to-use plug-and play solution) and DeepSight (SDK) come with privacy by default, allowing you to blur the recorded faces without affecting the quality of the data that you capture.

By analyzing shopper behavior, you gain access to real-time insights and long-term trends that help you make data-driven decisions that benefit both you and your customers.

Read More About Our Facial Analysis Technology

Other articles you might find interesting:

Online Shopping Behavior
Customer Analytics
Programmatic Digital Advertising

 

 

   

Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.