OneCheck
Where Offline Retail Enters Next Phase
Where Offline Retail
Enters Next Phase
Issues
What Offline Retailers
Struggle With
Offline retailers continuously collect data, but due to complex and hard-to-use environments, much of it remains unused and simply accumulates. The lack of customer journey data leads to fragmented information, making it difficult to understand performance drivers or translate insights into effective strategies.
01
Poor Visibility into Customer Behavior
Offline retailers often lack clear visibility into customer entry paths and in-store behavior. Limited insight into segment-level differences and real-time activity results in fragmented customer understanding, making it difficult to develop effective strategies.
02
Data Without Answers
While offline environments capture basic metrics like visit and conversion rates, they rarely explain performance drivers. Data silos and limited causal analysis hinder deeper interpretation, making it difficult to compare and manage multiple store locations.
03
Data That Fails to Drive Action
Although data exists, non-expert staff often struggle to interpret it and turn insights into action. Siloed legacy systems and standardized reports obscure root causes, while the lack of real-time analysis limits timely, on-site decision-making.
Now, Just Ask. Get Answers.
01
Type Naturally
Users can access advanced analytics simply by asking questions in natural language, without writing queries or relying on technical expertise. This improves data accessibility and allows teams to make consistent, data-driven decisions.
02
Check Insights Report
Key metrics are summarized by store with year-over-year and month-over-month performance and growth, presented through cards and charts. Users can review details as needed and explore insights through follow-up questions.
03
Act on the Cause
By combining UWB-based positioning data with contribution analysis, the platform goes beyond surface-level metrics to identify root causes and provide actionable insights that drive measurable performance improvement at scale in practice.
Fully AI-tonomous
Pure Efficiency
Faster Insights
Question
Please tell me how much Toyosu Store’s sales increased or decreased in 2025 compared to 2024, analyze the underlying causes, and provide recommendations based on those causes as well.
Answer
In 2025, sales at the Toyosu Store totaled KRW 830,812,767, down –0.63% (–KRW 5,278,000) year over year. Visitor traffic also declined by –0.69% to 4,441,842, contributing to the overall sales decrease, while the average transaction value remained nearly flat at KRW 503.
By category, beverages (+2.56%), fresh food (+2.48%), and bakery (+2.48%) recorded positive growth, whereas condiment sales fell by –5.69%, weighing on total performance. Conversion rates were generally stable, but limited growth in transaction value was not sufficient to offset the decline in visitor traffic.
Overall, the sales decline was driven by lower foot traffic and weak performance in certain key categories, indicating a need to strengthen traffic-driving marketing efforts and conversion-focused operational strategies.
MADE FOR REAL-WORLD DECISION MAKERS
01
Executives
Executives can instantly understand key performance and drivers of change in real time simply by asking questions in natural language—without needing to understand data structures or analytical methods. Without waiting for reports or relying on specific analysts, leadership can make fast, consistent decisions based on a shared, single source of truth.
02
Strategy Teams
Go beyond revenue numbers to compare store performance with deeper context. Ask questions in plain language to understand how each store is performing, uncover customer behavior patterns, and identify what drives revenue changes and differences between locations—supporting faster strategic decisions across the organization.
03
Data & DX Teams
There’s no need to explain data structures or build complex dashboards. Business teams can ask questions in natural language and get clear answers on their own, without relying on analysts or technical resources. This reduces manual reporting and enables practical, self-serve insights that teams can use in day-to-day decisions.
04
Marketing Teams
Anyone can analyze customer behavior using natural language questions—without complex data or queries. Ask questions like “Why did conversion drop?” or “Where are customers dropping off?” and get clear insights instantly, with AI-powered, data-backed decision support. Optimize campaigns by understanding the customer journey based on real data.
Ready to See it in action?
Have questions or want to explore a demo or PoC?
Just email us — no forms, no pressure.
E-mail: sales@onecheck.co.kr