Geo Differentials

Location is a primary compensable factor when comp professional benchmarking jobs and developing salary structures. This is a feature in PayScale MarketPay that helps people understand how their position in the market vary by locations using PayScale crowd-sourced data

My role: Product Designer - User research, UX strategy and solutions, user-flow, prototype, hi-fidelity UI design

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WHY IS THIS Problem?

geo Differential data is in demand

Paying employees based on location or cost of living is becoming an industry standard, especially for companies who want to attract talents from the hot markets for specific jobs.

Provide more value to our customers and stay competitive

As a data company, gathering geo differential data would provide a more robust data package to our customers and stay on top of the game in the competition landscape.

Challenges

Scaling needs

It could be used by small business owner, HR generalists , all the way to compensation professionals of Enterprise companies.

The variety of use cases

  • Learning the trend only

  • Apply geo differentials to salary ranges or to jobs based on how they price their jobs

Adaptability for other PayScale products

  • Implement in product for small and medium-sized businesses

  • on PayScale.com for Individual salary seekers


Design Process

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User Research

We conducted a few customer interviews to understand their perception of geo differential data, the use cases, the business impact and the value of the features

Key Findings

High business Value and Impact

Leverage Geo Differential data is critical for our clients to stay competitive in the talent market, especially during this pandemic as WFH starts becoming the new normal. Most of the customers rated the business value of this feature Medium or high.

favor the seamless experience

Most of the customers we interviewed are currently using another data resource for geo differentials. However, they would still consider PayScale data since they can directly apply geo diff to their data and perform analysis.

Need to see data from different angles

  • Quarterly or yearly trend

  • Compare location to national or a specific area

  • Want the data to be more granular ( by job family, by job title,etc)


synthesize

I synthesized my findings under three categories - goals ( what customers want to achieve through this feature ), emotions ( how customers would feel when using this feature ), decision-making ( what is essential and helpful for them to validate their decisions on data )

Goal/Motivation

Incorporating geo differentials to their pay philosophy so they can pay their employees fairly and offer competitive salaries in the market

Emotions

  • Need to feel confident with the data

  • Apply geo differential directly to their data inside of the product is appealing

  • Fear of messing up the current job pricings

Decision Making

Salary structure is the analysis users would perform to see if they have applied reasonable geo differential data based on locations


ideate

Then I created the user flow including three main tasks:

  • Edit geo differentials for locations based on selected employees/jobs

  • Preview salary ranges/pricing impact

  • Apply geo differentials and generate Reports

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design Solutions

  • Separate “Explore geo differentials “ and “ Apply geo differentials”

  • allow users to configure the way to display data

  • Include a panel for data methodology

  • allow users the preview the impact analysis


KEY Takeaways

The biggest challenge when designing this feature is that each customer has a slightly different way of positioning and using geo differential data according to their compensation strategies. Thus it requires the designer to identify the typical/primary tasks v.s. Organizational/secondary tasks. It's not easy to design a flow that works for everyone without overwhelming them with all the different tasks.