OMO Canvas Flow with Geofence, Landing Pages, Context Steps & More!

1016_ueda

Hi Braze Marketers & Builders!

We are TOMORROWLAND Co., Ltd., a premier Japanese fashion enterprise celebrating our 50th anniversary in 2028.
We operate over 10 original house brands and manage more than 100 high-end brick-and-mortar boutiques across Japan.

Here is our new OMO Canvas Flow built to connect online customer intent with physical stores.

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1016_ueda

Hi Braze Marketers & Builders!

We are TOMORROWLAND Co., Ltd., a premier Japanese fashion enterprise celebrating our 50th anniversary in 2028.
We operate over 10 original house brands and manage more than 100 high-end brick-and-mortar boutiques across Japan.

Company Brands: https://www.tomorrowland.co.jp/brand/index.html
Store Locator: https://store.tomorrowland.co.jp/store-locator/

Here is our new OMO Canvas Flow built to connect online customer intent with physical stores.

The Strategy: What was the core goal?

1.Core Goal:

To bridge the gap between our online store and physical retail stores by seamlessly guiding customers who registered "back-in-stock" requests online to nearby physical stores.
By facilitating high-touch, in-person customer service, we aim to lower the psychological barrier to high-price purchases and transform casual shoppers into loyal brand advocates.

2.Background & Key Challenges:

• Inventory Utilization & Purchase Barriers:
Although inventory was synced between our e-commerce platform and physical stores, minimum threshold constraints prevented us from utilizing 100% of store stock to fulfill online back-in-stock requests. Furthermore, given our premium price positioning, buying items online without trying them on posed a high psychological barrier for customers.

• Optimizing In-Store Foot Traffic vs. Message Fatigue:
Our analysis revealed that nearly all top-tier loyal customers were personally managed by dedicated store sales associates—proving that "exceptional in-store hospitality" is vital for building brand loyalty.
However, aggressive outreach to light users carried a high risk of opt-outs. The core challenge was driving store visits naturally, without feeling intrusive.

3.The Solution:

“Real-Time Geofencing × Online Back-in-Stock Requests × Local Store Inventory Alerts”
Using a customer’s entry into a physical store’s Geofence as the primary trigger, Braze automatically evaluates local store stock. If a requested back-in-stock item is available at that specific store, personalized Push Notifications and Content Cards are instantly delivered.

The Architecture: Why did you design it this way?
In Japanese retail, customer service standards are exceptionally high due to two cultural factors:

• "Omotenashi" Culture:
The deeply ingrained philosophy of anticipating customer needs before they are voiced.

• Zero-Tolerance Quality Expectations:
Customers hold high baseline standards and tend to judge brands on a deductive basis (deducting points for minor oversights or awkward timing).

To maintain this standard in digital communications, we leveraged Braze’s advanced features to craft a flawless, highly contextual Canvas Flow:

1.Eligible Store Verification via Geofence Entry
(Context Step + Catalogs + Decision Split)

By executing SetStoreData and CheckStoreAvailability, the Canvas evaluates:
• Is the store eligible for stock alerts? (Excludes stores without real-time inventory tracking).
• Is the store currently open, operating on an active business day, and within business hours? (Calculated as Closing Time minus 30 minutes to allow realistic travel time).
• Is the store closed for seasonal holidays (e.g., New Year holidays)?

2.In-Store Inventory Matching for Requested Items
(Context Step + Custom Attributes + Catalogs + Decision Split)

By executing SetItemStockData and CheckItemStockAvailability, the system verifies:
• Was the back-in-stock request made within the last 14 days? (Limits messaging to high-intent items).
• Does the local store have at least 2 units of the item in stock? (Prevents driving foot traffic to an item that might sell out immediately).

3.Intelligent Capping via Decision Split Retargeting
(Decision Split)

Via CheckMessageFrequency, we verify messaging eligibility using a Retargeting Filter:
• Has the user never received a message from this Canvas? OR
• Has it been at least 14 days since their last message received from this Canvas?

Why this design?

・Why not Entry Controls (Specified Window)?
If we controlled re-entry via Entry Controls' Specified Window, users who entered the Canvas but were filtered out without receiving a message would be blocked from re-entering within that timeframe.

・Why not Native Frequency Capping?
Native Frequency Capping only throttles message delivery—it cannot prevent downstream User Update steps. If a capped user passed through, their Custom Attributes would still be updated, creating a critical mismatch between the Landing Page (LP) content and the Push/Content Card notifications.
Using a Decision Split before the User Update step ensures ineligible users immediately Exit the Canvas, preventing state changes and preserving eligibility for future qualified entries.

4.Dynamic Custom Attribute Updates
(User Update Step + Custom Attributes)

• If all criteria in Steps 1–3 are fully satisfied, the user profile is dynamically populated with landing page metadata via UpdateDataForLP.

5.Channel Permission Branching
(Audience Paths Step)

The journey evaluates channel permissions via CheckChannelSubscription:
• Push Enabled (Opted-in): Routed to receive both Push Notifications and Content Cards.
• Push Disabled (Not Opted-in): Routed to receive Content Cards only as a non-intrusive fallback.

Why Audience Paths?
We intentionally utilized Audience Paths instead of a Decision Split to maintain scalability, allowing us to seamlessly expand messaging to additional communication channels in future campaign iterations.

6.Cross-Channel Delivery with Dynamic Personalization
(Context Variables + Custom Attributes + Catalogs + Push Notifications + Content Cards + Landing Pages)

• Messages reference Context Variables, Custom Attributes, and Catalogs to deliver personalized Push Notifications and Content Cards, seamlessly directing users to dynamic, high-engagement Landing Pages.

I’ve recorded a short video demonstrating the user flow. 
https://www.loom.com/share/1562c052655d460090a68ab0f01974db

The Impact: What kind of results or learnings came out of this journey?
Core Strategic Impact:
The greatest triumph of this initiative was eliminating the internal conflict over revenue attribution between physical retail stores and e-commerce, successfully establishing a true OMO Ecosystem.

Previously, store associates hesitated to promote the mobile app or online store out of concern that offline revenue would migrate online.
This campaign visibly demonstrated that "digital customer behavior directly drives high-value foot traffic back to physical stores.
"By converting digital intent into physical store visits, we dismantled organizational silos and created a win-win, bi-directional customer lifecycle.

Future Roadmap & Next Steps:
To further strengthen this ecosystem, this campaign highlighted key structural focus areas that we are actively redesigning:
• Incentivizing App Registration:
Driving higher sign-ups to capture comprehensive online browsing logs.
• Expanding Geofence Triggers:
Utilizing additional digital behavioral signals beyond back-in-stock requests for autonomous geofence campaigns.
• Location Permission Opt-ins:
Strategically promoting Location Services permissions among app users.
• Push Notification Opt-in Optimization:
Enhancing soft-prompts to maximize Push Notification permission rates.