AI cameras for data acquisition and generation; AI for data utilization.

Based on the customer visit data obtained,
A store management support service that makes sales improvement reproducible across all stores.

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Successful store management is achieved through "data".

What is ABEJA Insight for Retail?

Cameras and sensors are used to visualize "the number of customers, their attributes (gender, age), and foot traffic in front of the store."
This is a store analysis and operations support service that enables data-driven decision-making.

Our specialized success team provides comprehensive support, from data visualization to translating that data into concrete strategies.
Even those working with data for the first time can easily implement data-driven store management without any difficulty.

Do you have any of these problems?

They only see the "sales" results.

It is impossible to determine whether the effect of the measures is "increased customer traffic" or "improved purchase rate," and the results are not reproducible.

It is impossible to determine if the store's "report" is accurate.

The on-site reports lack supporting evidence, making it impossible to issue appropriate instructions.

Unable to break free from experience and intuition

The operation relies on years of experience and intuition (KKD), making it impossible to create a system for utilizing data.

The "priorities" for improvement are unclear.

We don't know where the problems lie—in terms of customer entry, browsing, or purchases—so our strategies are always reactive.

Policies are "just implemented and left unfinished."

The effectiveness of policies cannot be evaluated, and know-how on successes and failures is not accumulated within the organization.

"Specific measures"
It's not decided.

Because the issues haven't been identified, resources are being wasted on measures that lack a solid foundation.

The reasons for sales are not reflected in POS data.

ABEJA Insight for Retail investigated where in the store customers got lost and why they put items back in front of the shelves.
By visualizing the "gap before purchase" that cannot be seen with POS data, we enable store management that does not rely on intuition.

Created by ABEJA Insight for retail
A data-driven system where the PDCA cycle continues to run.

We create a "PDCA cycle" that allows for continuous improvement and verification, starting with customer behavior occurring in the sales area.

ABEJA Insight for Retail: Quick and Easy Guide

We are offering free access to helpful resources, including service details and the implementation process, to assist you in your consideration.

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For inquiries regarding costs, etc.
Our expert staff will answer your questions carefully.

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Foot traffic in front of the store

In addition to understanding the amount of foot traffic in front of the store by time of day, day of day, and month, by linking it with POS data...

The system can also automatically calculate and visualize the customer occupancy rate for each store.

By understanding the store's occupancy rate, it becomes possible to measure the effectiveness of attracting customers through changes to the store's facade visual merchandising (VMD).

Dashboard

Correlation analysis with other items is also possible.

The dashboard displays measurement results in a tree format of sales components. By linking with POS data, it is possible to automatically calculate and display figures such as store entry rate and purchase rate.

The data analysis screen allows you to easily analyze how changes in the number of customers affect other indicators such as average customer spending, sales, and purchase rates. This makes it possible to evaluate the effectiveness of measures aimed at increasing sales.

PDCA function

It supports budget management that breaks down not only sales figures but also KPI items that make up sales, such as foot traffic in front of the store, entry rate, purchase rate, and average customer spending.

By evaluating the performance of daily KPI items based on actual and planned results, you can constantly identify bottlenecks in achieving your sales budget and implement an improvement cycle.

"Why didn't we sell much today, even though there were so many people?" We'll find the answer using data.

Visualization of forecasts and actuals broken down to KPI items

The dashboard allows you to view projected vs. actual figures broken down into KPI items such as foot traffic in front of the store, entry rate, purchase rate, and average customer spending. You can instantly determine which KPI items that make up sales are currently causing problems.

We also provide support for the implementation, management, and effectiveness evaluation of policies that follow the decision-making process.

The system includes a "Measure Management" function that allows stores to register the measures they implement. This function allows you to verify the effectiveness of measures by comparing them with actual results. By repeatedly implementing measures based on problem identification and verifying their effectiveness, it is possible to run an effective improvement cycle.

Comprehensive customer success support to assist with budget planning.

"I don't know how to design a budget for my KPI items." Even in such cases, you can rest assured. Our customer success team will work alongside you to support you in creating an appropriate budget.

Number of guests

In addition to tracking the number of visitors by time, day, and month, by linking with POS data...

The system can also automatically calculate and visualize the purchase rate for each store.

By understanding the number of visitors, it becomes possible to identify challenges at each store and evaluate the effectiveness of customer acquisition strategies.

Dashboard

Correlation analysis with other items is also possible.

The dashboard displays measurement results in a tree format of sales components. By linking with POS data, it is possible to automatically calculate and display figures such as purchase rate and average customer spending.

By understanding trends in the number of visitors and purchase rates by day of the week and time of day, it is possible to identify the optimal timing for implementing countermeasures.

Visitor attributes

We estimate the age group (teens / 20s / 30s / 40s / 50s and over) and gender (male / female) of customers and collect this data as statistical information.

Understanding the demographics of your customers allows us to measure the effectiveness of our marketing efforts, such as whether our product lineup is a good fit for your target audience or whether we are attracting the customers we want to reach.

Dashboard

Correlation analysis with other items is also possible.

The dashboard can display trends in customer demographics over time.
This can be used for targeting specific stores and measuring the effectiveness of marketing strategies.

The data analysis screen allows you to easily analyze increases and decreases in customer visits by attribute over different periods. This makes it possible to verify the effectiveness of measures aimed at increasing sales.

Retention analysis

By visualizing how the number of people lingering in specific areas of the store changes due to shelf changes and VMD (Visual Merchandising) measures, and how these changes in lingering numbers in each area affect sales and purchase rates, it becomes possible to implement a cycle of effectiveness verification and improvement for various measures.

Dashboard

The measurement range can be changed.

The dashboard allows you to visualize the number of people occupying a designated measurement area on an hourly, daily, or weekly basis.

You can analyze correlations not only between the defined areas but also with other indicators such as sales and purchase rates.

The measurement range can be changed as needed from the dashboard.

Without the need for additional camera installation, customers can adjust the measurement range within the camera's field of view to suit their specific needs.

AI automatic analysis (β)

Simply select your desired store and time period, and the AI will automatically analyze the factors influencing sales fluctuations. It will identify bottlenecks in the KPIs that make up sales and output the results in an easy-to-understand text format. Sales analysis, which previously relied on experience and intuition, can now be performed by anyone, immediately, and based on data.

Dashboard

Simply press the analyze button and the AI will immediately begin the analysis. You can then review the analysis results together, comparing them with graphs and KPI data. Furthermore, you can freely conduct additional in-depth analysis via chat, expanding the scope of your analysis.

AI analyzes based on data from physical stores.

The AI automatically analyzes the factors behind sales fluctuations, focusing on [Year-on-Year Comparison] in the data analysis screen and [Budget-to-Act Comparison] in the review screen. It identifies the main factors contributing to sales increases/decreases and budget shortfalls from up to five perspectives, in order of their magnitude, and provides supporting evidence. This supports data-driven problem identification and the consideration of improvement actions.

Why choose ABEJA Insight for Retail?

High accuracy and stability of data acquisition.

Thorough support for effective utilization, not just "implementation and then the end."

Our proprietary product boasts a track record of AI implementation with over 350 companies.

Our proprietary AI algorithm enables highly accurate and stable data acquisition, unaffected by environmental factors.

How to interpret data, draw insights, formulate and implement policies,
We support the establishment of the PDCA cycle, including review.

Our development system boasts superior quality and speed, based on the experience and knowledge gained from developing and supporting AI-integrated products for companies across various industries.

Reasons why data utilization is becoming established in the workplace

We visualize KPIs and break down the sales equation—"Foot traffic in front of the store → Store entry rate → Purchase rate (POS integration)"—for management.
Headquarters and the field can use the same data to guide improvement actions.

By combining "AI-driven analysis" and "support from customer success teams," we identify issues from data and assist in planning and executing concrete improvement actions.

Case Studies

We interviewed companies that have actually implemented the system to find out what results they have achieved since then.

Tutuanna Co., Ltd.

From analyzing store entry and purchase rates to implementing sales improvement strategies: Instilling a data-driven culture in the operation of 250 stores nationwide.

Vinos Yamazaki Co., Ltd.

Scientifically analyzing the effectiveness of policies and store staff training—Vinos Yamazaki Co., Ltd. approaches store improvement with "reliable data"

ICI Ishii Sports Co., Ltd.

By quantifying the customer visit rate, we were able to discover merchandise strategies that lead to new customers.

assignment

POS data makes it difficult to understand customer behavior prior to sales, making it challenging to develop store improvement strategies.

assignment

It's difficult to determine whether fluctuations in sales are due to changes in the number of customers or differences in customer service skills, making evaluation and training challenging.

assignment

I want to move beyond experience and intuition and accumulate know-how in store management.

List of successful companies