Helping easy data movements form Google Analytics 360 to AWS S3.

Challenges Faced

Issues with Mercari’s Data Utilization

Mercari faced two main issues with data utilization.

  • The first was designing incentives for sales activities from a business perspective. Previously, incentives were based solely on the introduction of the payment service Merpay to stores. To encourage usagepost-introduction, the incentives needed to be based on actual use of Merpay,necessitating a redesign.

However, there was a disconnect between the customer management system (CRM) and sales support system (SFA) data used by the sales team and the payment service data used by software engineers. Consequently, the extent of Merpay's usage by the store's post-introduction was unclear.

To "promote usage of the service after introduction,"it became necessary to "measure and evaluate sales activities and payment results by linking them," which required integrating the separated data.

  • The second issue was the technical challenge of lacking data engineer man-hours. To integrate the divided data and link sales activities with payment performance data, data engineers were needed at every development, maintenance, and operation phase.The man-hours of in-house engineers were overwhelmingly insufficient. Furthermore, recruiting highly skilled data engineers from a small talent pool was extremely difficult, posing a challenge to staff expansion.

Objective of the Initiative

Solving the Problems

We considered several aspects to solve the above issues:

  • Determining the system configuration that could address the problem.
  • Deciding whether to build the system in-house or utilize external tools.
  • Selecting which external tools to use.

For the first consideration,we successfully integrated various in-house system data into BigQuery within the already introduced Google Cloud domain. For external domain data (CRM/SFA),we chose a configuration that connects directly to BigQuery.

For the second consideration, we opted to use external tools and began assessing which ones for the third consideration. The selection criteria included quality comparison to in-house development, cost-effectiveness,delivery to sales staff, and the capability to handle various SaaS data.

After careful evaluation, Mercari decided to implement the Trocco®️ x BigQuery data analysis platform.

Solution: Introduction of trocco®

Reasons for Selecting Trocco®️and Platform Configuration

Trocco®️ met all QCDSselection criteria:

  • Quality: Assured through security,hands-on, and Q&A support.
  • Cost: Significantly lower than the labor cost of engineerman-hours.
  • Delivery: Usable by sales staff themselves, immediate implementation with SaaS, and rapid development of requested features.
  • Scope: Compatibility with a wide range of data sources.

Another deciding factor was the support system that facilitates internal approvals, with comprehensive Japanese documentation and security checks, being a Japan-originating service.

Economic Effects Achieved

  • Business Impact: A 30% increase in Sales CVR.
  • Technical Impact: Three man-months of engineer man-hours saved.
  • Data Linkage Lead Time: Reduced to one-quarter.   

Mercari now uses the Trocco ® ️-created analysis platform to link various data sources, driving a data-driven culture and business acceleration. The introduction of Trocco®️ has sparked new ideas and potential applications among team members.

Implementation Process

Post-Implementation Effects

Future Prospects

TROCCO is trusted partner and certified with several Hyper Scalers