The use of technology has become an integral part of any business. At retail stores, the number of employees is being reduced by unmanned stores and IT, and recently, “in-store analysis” using AI is drawing attention. Here, we will also introduce the reasons why in-store analysis is necessary and the methods and terms.
What is “in-store analysis”? It is intended to analyze and optimize mechanisms and approaches for maximizing sales based on store customer flowlines and purchasing behavior. This is an indispensable element that becomes the lifeline of store management.
Up until now, in-store analysis has also been an issue that store managers have conducted based on their own experience and intuition. However, there are some parts that are overlooked by the human eye, and it is not possible to make objective judgments, so improving the accuracy of countermeasures was a problem that could not be compensated by efforts alone.
In addition, it is difficult to spend time and effort on numerical analysis during store operation. The clerk is in an environment where customers are constantly coming and going and responding to sudden requests. Among them, there is a limit to the in-store analysis that needs to be considered deeply when performing inventory / equipment management, customer service, staff support, cash register support, telephone support, display replenishment, etc.
Therefore, what is attracting attention is in-store analysis using AI. With AI, you can always see only the numbers and facts from the bird’s-eye view, and you can continue the analysis automatically 24 hours a day, unattended.
Reasons why in-store analysis is necessary
So why do you need in-store analysis? There are three main reasons for this.
Maximize sales
You can analyze your customers’ buying behavior based on numbers. Analyze buying trends from a variety of factors, including seasons, trends, regionality, and peak hours. You can also infer a personalized approach if you have personal data such as customer gender, age, and behavioral patterns.
Develop more effective measures
The flow line from entering the store to opening the store, the flow from the customer to the purchase, the products you often pick up, the signboards and POPs you stop by. Each of these has hidden tips for increasing store sales. By incorporating AI analysis, it is possible to scoop up elements that were previously overlooked by the human eye.
To solve the labor shortage
There is a big problem of labor shortage in store management. However, if there are few staff, there is concern about crime prevention, and since you cannot see the entire store, you may not be able to follow up and miss the opportunity to purchase. Also, if you can analyze the peak and off-season of the store, you can shift the optimal number of people and labor costs will not be wasted.
In this way, the introduction of store analysis is expected to solve store problems.
In-store analysis method
There are multiple ways to analyze. Here are some of them.
Dynamic analysis
When the term is used in a store, it refers to an analysis performed by logging actions taken by customers in the store, such as flow of people, actions such as customer purchases, and points where people gather. ..
Retention analysis
It’s also an element of dynamic analysis, but customers can analyze where and what elements work and stay. When customers “stay” in a store, they have many opportunities to make purchases, which should be emphasized in store analysis.
Attribute analysis
You can analyze buying behavior based on basic customer data and characteristics. For example, you can get a detailed picture of your characteristics, such as “20s, female, calm facial expression, long hairstyle”. You can also use digital signage to display advertisements that match the attributes on the spot.
3D camera / sensor
In-store analysis using cameras and sensors is essential for introducing IT into retail sales. It can accurately count the number of visitors and the number of guests, and is also used for accidents and crime prevention measures at unmanned stores.
In this way, you can analyze the inside of the store based on various factors, so you need to consider which factor you need.
Terms related to in-store analysis
Data mining
It is a technology that can analyze patterns and trends using artificial intelligence and statistics based on a large amount of information collected in a database. It is primarily used in marketing and can be used for customer purchasing analysis.
POS (Point of Sale)
The POS system can record purchase information such as sales performance and unit price at the time of sale. From the accumulated information, it is possible to analyze purchase trends by store or region and popular products by attribute of the purchaser, so it is used as a marketing tool in many stores.
Customer attributes
As the name implies, it refers to attribute classification by customer status and profile, but strictly speaking, there are two types: “static attributes” and “dynamic attributes”. A static attribute is a profile that never changes, such as the customer’s date of birth (= age) or place of birth. Dynamic attributes refer to changes in customer information such as current address, income, occupation, and purchasing habits.
Power hour
I think retail stores are busy all year round and it’s off-season, but even a short break of the day can be “crowded” or “low visits”. Power Hour is the former and is the busiest time of the day. You can also do store analysis to find out when to allocate more staff.
