[FAQ] Black Lives Matter: An Update on Our Commitments

What methodology did you use to collect and analyze your representation data?

Stitch Fix employees voluntarily enter their diversity data during onboarding in Workday, our Human Resource Information Systems (HRIS). We partnered with the diversity consulting firm Paradigm to determine the terminologies and functional groupings to analyze the data. In this process, we balanced the challenge of leaning into transparency while respecting employee confidentiality, and researched peer organizations to establish meaningful benchmarks for contextual analysis that took into consideration our identity as both a retail and tech company.

What methodology did you use to collect and analyze your pay data?

Data is core to our business, including our compensation model. At Stitch Fix, an employee’s compensation is determined by variables including location, job function, and job level. We run regression analyses to assess any potential pay differences among comparable employees, and the model tracks pay variance by protected class (age, gender, and race) to determine whether there are any statistically significant gaps. We leverage the analytics platform PayEQ from Syndio Solutions, our expert third-party partner and leader in the pay equity space.

There are lots of companies that claim pay equity. What makes Stitch Fix different?

At Stitch Fix, we have employed and upheld a philosophy and administration of equal pay from the beginning, using objective compensation drivers like location, job function, and job level, while excluding factors that are more prone to bias like performance and educational background. Our results show equitable pay not only across gender, but across race and age.

We partnered with an expert third party, Syndio Solutions, to audit our pay data to ensure that we weren’t inserting subjective biases within our own analyses. With this external review and rigorous methodology applied, we have confidence in our approach and can stand behind our findings of pay equity.

Why is the data presented on gender company-wide (global), but the data on ethnicity is for the U.S. employee populations only?

The standard practice is for U.S.-headquartered companies to report only U.S. ethnicity data, and we have followed this practice for this share out in order to provide information that offers the most meaningful insight.

In addition, proper apples-to-apples comparison on ethnicity data across the U.S. and U.K. is not currently possible, given that different countries have different approaches, data laws, and regulations when categorizing and collecting these data. As a result, we cannot bring global consistency to our ethnicity data across countries.

What does the “Other” category under ethnicity include?

American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. To balance the challenge of leaning into transparency while respecting employee confidentiality, we combined smaller groups into this category.

What roles does “Tech” include?

Stitch Fix defines “Tech” as a job family that includes employees within our Engineering, Data Science, IT, Security, and Product orgs. This is similar to other companies, including our peer benchmarks.

What roles does “Leadership” include?

Our “Leadership” category includes all employees who are leveled at the Director tier and above. For our Styling, Warehouse, and Customer Experience organizations, this category encompasses our regional leadership such as Styling Regional Managers and Warehouse General Managers.

What roles does “Business (Non-Tech)” include?

Employees who are part of our Corporate organizations (e.g. Finance, Merchandising, Marketing, etc.) but excludes Tech (and also excludes Styling, Warehouse, and Customer Experience organizations).

Why don’t the percentages shown always add up to 100%?

In some instances, due to rounding and multi-race self-identification, representation percentages may not add up exactly to the overall percentage for each racial/ethnic group. We want to report our data in alignment to the way our employees identify themselves racially and ethnically, which doesn’t always fall neatly into one category. In certain cases where it was statistically sound, we adjusted the numbers to round up or down.


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