Principle Success Story: Saved over $1.4 million in annual DM spend

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Use Case: Saved over $1.4 million in annual DM spend For: Major airline’s eCommerce website Client Challenge: The client had unnecessarily large spend on their product catalog through Direct Mail. They want to reduce the number of DM sends while maintaining sales. Principle's Approach: - Generated data template by guiding the client to prepare a data input file. - Setup Google BigQuery with a logistic regression model used for machine learning. - Verified the accuracy of the model and implemented a feature value of engineering. - Deployed a model to calculate “purchase probability resulted from the catalog”. - Built and recommended a customer list that has a high purchase possibility.

Date Prepared by
Principle
Analytics Use Cases &
Example Deployments
2020 Principle Co., Ltd.

Copyright (C) Principle Co, Ltd. All Rights Reserved
Major Airline’s eCommerce Website

Copyright (C) Principle Co, Ltd. All Rights Reserved
Use Case: Saved over $1.4 million in annual DM spend
Major airline’s eCommerce website
2
Customer Challenge
● Client had unnecessarily large spend on their product catalog through Direct Mail. They want to
reduce the number of DM sends while maintaining the sales.
Principle’s Approach
● Generated data template by guiding the client to prepare data input ﬁle.
● Setup Google BigQuery with logistic regression model used for machine learning.
● Veriﬁed the accuracy of the model and implemented feature value of engineering.
● Deployed a model to calculate “purchase probability resulted from catalog”.
● Built and recommended a customer list that has high purchase possibility.
Results
● $1,440,000 Estimated annual cost reduction by removing unqualiﬁed customers from DM list
● The number of catalog circulation reduced from 160K to 100K copies while maintaining a similar level
of sales

Copyright (C) Principle Co, Ltd. All Rights Reserved 3
● Using Google BigQuery ML and Tableau, we observed customer segments and predicted
their purchase.
● With this data and segments, Principle provided client with a customer list to remove from
DM sends.
Use Case: Saved over $120,000 in annual DM spend
Major airline’s eCommerce website

Copyright (C) Principle Co, Ltd. All Rights Reserved
End Of Document

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1. Date Prepared by Principle Analytics Use Cases & Example Deployments 2020 Principle Co., Ltd.

3. Copyright (C) Principle Co, Ltd. All Rights Reserved Use Case: Saved over $1.4 million in annual DM spend Major airline’s eCommerce website 2 Customer Challenge ● Client had unnecessarily large spend on their product catalog through Direct Mail. They want to reduce the number of DM sends while maintaining the sales. Principle’s Approach ● Generated data template by guiding the client to prepare data input file. ● Setup Google BigQuery with logistic regression model used for machine learning. ● Verified the accuracy of the model and implemented feature value of engineering. ● Deployed a model to calculate “purchase probability resulted from catalog”. ● Built and recommended a customer list that has high purchase possibility. Results ● $1,440,000 Estimated annual cost reduction by removing unqualified customers from DM list ● The number of catalog circulation reduced from 160K to 100K copies while maintaining a similar level of sales

4. Copyright (C) Principle Co, Ltd. All Rights Reserved 3 ● Using Google BigQuery ML and Tableau, we observed customer segments and predicted their purchase. ● With this data and segments, Principle provided client with a customer list to remove from DM sends. Use Case: Saved over $120,000 in annual DM spend Major airline’s eCommerce website

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