Stripe Makes Customer Data

A Profitable Habit

CDP Makes Digital Transformation a Reality for Apparel & Lifestyle Brand Stripe International Inc.

Stripe Cuts Unsold Inventory and Boosts Sales with Treasure Data CDP Insights

At Stripe International Inc., the effective use of customer data has become a healthy habit. The apparel retailer and lifestyle brand started using customer data to improve its advertising results and grow its customer base. The results of its first CDP-driven data modeling were so compelling that the company decided to expand the use of its customer data platform (CDP). Stripe now leverages its deep understanding of customers to other parts of its business, including:

Personalized customer journeys and targeted selling in its retail and lifestyle brand business

Synching its supply-chain systems and hyper-localizing store inventory using CDP-powered projections of customer demand based on sophisticated customer behavior models

Predictive analytics, targeting, and segmentation for better retail results

Treasure Data CDP analytics and AI that power more company-wide digital transformation


Revenue attainment increase

Up 70 percentage points in three months

Revenue attainment shot up from about 90% of goal to more than 160% of target in about three months.

Interstore Inventory Management

$220,000 annual savings in estimated labor costs

Predictive analytics got inventory to the stores most likely to sell it.

Unified and Hyper-localized Data

10+ Types of Data Unified

Stripe unified data from its multiple brands and many data sources, including online and in-store purchase histories, advertising and behavioral data, mobile data, and weather sensors.

The Challenge

Like many companies, Stripe initially wanted to evaluate its new customer acquisition efforts, including its advertising. It also hoped to use its new Treasure Data Customer Data Platform to understand its customers better, keep the brand experience of existing customers fresh and fashionable, and avoid cannibalizing existing sales with new online programs.

Pilot program successes later led to a supply-chain and inventory-management program that used predictive analytics to get inventory to the right stores—those most likely to sell it all—at the right time.

The Solution

Treasure Data CDP unified data from many diverse sources, including:

First-party data such as online and in-store purchase histories

Advertising and behavioral data

Second-party and third-party data

IP location and NPS data

Weather data

Next, Stripe used Treasure Data’s analytics capabilities for predictive scoring, targeting and segmentation, and lookalike analysis to find new prospects for Stripe’s lifestyle brands. The company modeled customer behavior to discover if high click rates and good lead generation were the result of effective advertising and relevant promotions, or other factors.

The results were so impressive that Stripe decided to use the insights and predictive models of its customers’ behavior to understand how to fine-tune its supply chain. Their goal was to have the right merchandise, in the right stores, at the right moment for customers to find and buy what they need right away.

“We used to outsource some aspects of our marketing and data analysis. Now that we can easily access and analyze customer data in-house, we are motivated to look at problems and say, ‘Let’s try this too.’ Also, our CDP helps us understand how our actions bring our customers closer. By increasing the accuracy of our work, I feel we have come closer to understanding our customers, the original goal of introducing Treasure Data.”

Shigeki Yamazaki, Advisor of the Digital Transformation Division of Stripe International Inc.

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