Supply Chain Management Company

Introduction

This case study revolves around a comprehensive customer profile application for a leading shipping and supply chain management company. It amalgamated data from a variety of sources to present a unified view of customer’s details. Data from various sources was previously processed by several internal company departments. The difficulty of managing data separately was addressed with the help of Customer 360 implementation. Using Google PubSub, Kafka, or MQ, it merges the real-time and aggregated data into a single profile.

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Problem Statement

• Multiple teams were allocated to handle the incoming data from various sources.
• No common CRM to maintain and consolidate the data from different sources
• The customer care team was dependent on other teams to dig in the required data
• Increased time consumption due to manual checking of data
• Different categories and different teams were processing the data

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Analysis

The categories involved, type of data, number of sources and the time taken to consolidate the entire data along with the manual work involved were taken into consideration while designing the architecture for this customer profile application.

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Implementation

Data Types

• Contact Data: This data was collected from the conversation between the customers and the customer care executives. The data included details such as the customer’s name, email, address, and phone number.
• User Data: This data indicated the details collected during the registration or account set up process. It included information such as the customer’s name, email, MyChoice registration status, address, phone number, and other Customer account-related information.
• HEAT Data: This encompassed all shipment-related information such as shipment ID, tracking numbers, ship-from and ship-to addresses & names.

Data Processing & Consolidation

• These data from various sources were processed by different internal teams. The real-time data was then transmitted to Customer 360 via Kafka/MQ/Google PubSub (the method varies depending on the data source).
• Within the Customer 360 application, the raw data was stored in Google BigTable. Then, this data was processed using a custom matching logic to consolidate customers’ information under a single profile.

Data Load

It was responsible for storing all the data from different sources such as Kafka, IBM, MQ. It extracted information like name, email address, contact information, residential address which was used for matching same data from different sources. Those data were sent to ICM via Kafka.

ICM

Intelligent Customer Matching (ICM) consolidated the data from different sources and matched with the respective user, so that it could be found when searched.

SOLR

This application was implemented to index the data during the search

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Technologies used

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Outcomes & Benefits

• A common system was developed to store the consolidated data arrived from different sources
• Reduction of time consumption because of the single profile application
• Any team was able to access the consolidated data without dependency, and process it without any challenges
• Consequently, a single document that encapsulated all their shipment activities, contact information, and Customer website information for each customer were engaged to find out the required information.

Conclusion

Our customer thought it was a brilliant idea to create a customer profile application because it transformed their manual system into an automated one, made it much easier for them to handle customers. The process was simplified and trouble-free since the misaligned data were entirely transformed into an aligned database. Each customer had a unique profile that the company could access on their own whenever necessary, and they could discover the right information.