Store360 for Beacon

Tangerine Inc. is a data collaboration company that supports data management for retailers by utilizing various IoT sensing data such as Beacon, WiFi, and AI cameras to acquire data from the real world.

 While OMO marketing is becoming more active and the use of apps in real stores is advancing, the reality is that not everyone has easy access to the "app user visit detection/visit measurement" technology necessary for surveying/analyzing how in-store consumers are operating and using apps and for implementing measures

 This service enables measurement of app users' visits to real stores by introducing our SDK into your app, and seamlessly integrates and analyzes this data with your company's internal data such as membership information base and IDPOS data held by Snowflake through data sharing. This data is then seamlessly integrated and analyzed with Snowflake's internal data such as member information base and ID-POS data.

List of data fields
・Key to make a record unique
・ID generated for each visit
・Contact start time
・Store name
・Floor name
・Terminal ID
・Member ID
・Advertisement ID
・Time spent per visit


Expected workflow
- Users integrate the SDK provided by Tangerine into their own apps.
- Sensing data is acquired and stored by using beacons and other devices to react with the SDK.
- Users share store information necessary for analysis via snowflake share.
- Tangerine will use snowflake share to provide users with customized data on store visitors for
each store.


Example of use
- By linking with MA/CDP tools, it can be used for follow-up measures for non-purchasers.
- It is possible to use the information of users who visited the store for digital advertisement
- Now that OMO measures have become common, KPIs can be set not only for MAUs and
DAUs, which are simply the number of users who launched the app, but also for the number of
active users who used the app at a real store.
- This can be used for OMO analysis of the app in combination with operation logs, menus, and
functions after launching the app when visiting a store.
- The number of users who visited the store but did not purchase can be compared with the
number of users who purchased, and the purchase conversion rate of non-purchasing users can
be considered for improvement.




 そこで本サービスでは、自社アプリに弊社SDKを導入することで、アプリユーザーのリアル店舗への来店計測を可能にし、当該データをData Sharingを通じて、Snowflakeに保有する貴社の会員情報基盤、IDPOSデータなどの社内データとシームレスに統合・分析・施策への活用を実現します。


- MA/CDPツールと連携することで未購入者向けのフォロー施策に用いることができます。
- デジタル広告配信に来店ユーザーの情報を利用することが可能になります。
- OMO施策が一般になった今、MAU、DAUという単純にアプリを起動したユーザー数をKPIにするだけ
- 来店時のアプリ起動後の操作ログ、メニュー、機能と組み合わせたアプリのOMO分析に用いることが
- 来店したものの未購買のユーザーと購買したユーザーを比較し未購買ユーザーの購買転換率向上を