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Amit Kumar, Senior Design Manager, UX & Product Design

// Home/// Work/// Sales Incentives

Sales Incentives

Sales Incentives product introduced as a new offering in the recognition and rewards platform.

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Sales Incentives cover
Industry
Sales incentives
Timeline
Dec 2022 to Present
Technologies
Figma, UX strategy, Jira, AI-assisted analysis, Perplexity, Google Stitch, Claude Design, Figma AI

Project Overview

Introduced Sales Incentives as a new product offering, contributing to business growth, product adoption, and revenue generation.

Aligned design deliverables with engineering sprints and product releases in Agile/Scrum.

My role

  • UX strategy and design direction
  • End-to-end design lifecycle ownership

Key Features

  • Incentive experience for sales teams
  • Sprint-aligned design delivery

Challenges & Solutions

Challenge: A new product line that had to fit an existing platform
Solution: User-centered strategy and design QA so the offering shipped with the rest of the suite.

Takeaways

Portfolio expansion through a new sales-incentives offering.

At a glance

My role

UX strategy and design direction

Timeline

Dec 2022 to Present

Sales incentives

Key decision

Progress bar and next milestone at the top.

Evidence on this page

2 built, 0 provisional, 0 illustrative, 0 planned

Each outcome below is labeled.

// The problem

What was getting in people's way

How to read this page: the outcomes marked Built are results I can stand behind. Personas, decisions and the problem framing are my design reasoning for this product and are not interview findings. Measured numbers are added only where I hold them.

Sales Incentives was added as a new offering in the recognition and rewards platform. Sales teams care about one question above all: what do I earn and how close am I.

Targets and payouts are hard to follow

Reps cannot easily see how work turns into reward.

Managers run schemes by hand

Setting up and tracking incentives takes manual effort.

Fit with an existing platform

The product had to ship with the rest of the suite.

// Strategy

From business problem to measurable outcome

KPIs are what the product is built to move. They are targets until a measured result is shown.

A clear incentive experience where progress, rules and rewards are always visible.

Business problemProduct capabilityKPIIntended outcome
Unclear progressLive progress against targetDashboard views per weekMotivated reps
Manual scheme setupGuided scheme creationSetup timeFaster launches
Platform consistencyShared patterns with rewardsReuseCoherent suite
Incentive status in side sheetstoProgress inside the platform
Rules explained by messagetoRules shown beside the target
Schemes set up by handtoA guided setup flow

// Users

Who the product is for

Provisional personas, derived from the workflow and the roles involved. They are not interview findings.

Sales representative

Know what they have earned and what is next

  • Progress view
  • Plain rules
  • Reward status

Sales manager

Run schemes that drive behaviour

  • Scheme builder
  • Team view

HR or finance

Pay accurately

  • Audit trail
  • Clear approvals

// Capabilities

What the app can do

A capability map of what the product is designed to do. It describes design scope, not a feature audit.

Incentive schemes

Define targets, periods and rewards.

Live progress

Reps see how close they are to each target.

Plain rules

Scheme rules sit next to the target.

Team view

Managers compare progress across the team.

Notifications

Alerts for milestones and changes.

Reward status

Track earned, pending and paid rewards.

Approvals and audit

A clear trail for payout decisions.

Platform fit

Works with recognition and rewards in the same suite.

// Scenarios

A rep checks progress

Opening the app shows 78 percent of target and the next milestone, with the rule one tap away.

A manager sets a scheme

A quarterly scheme is created with targets and rewards and published to the team.

Finance verifies payouts

Approved rewards are reviewed with a clear trail before payment.

// Process

How I ran the design lifecycle

Stage 01 of 5

Discover

Understand how incentive schemes are run and what reps ask about.

// Design decisions

Problem, evidence, decision, trade-off, validation

Each decision is written the same way so the reasoning can be checked.

Decision 01

Lead with progress

Problem
Reps open the page to check how close they are.
Evidence
Observed in how incentive questions are asked.
Decision
Progress bar and next milestone at the top.
Trade-off
Detailed rules sit lower.
Validation
Check what people tap first after launch.

Decision 02

Keep rules visible

Problem
Mistrust grows when rules feel hidden.
Evidence
Design principle for transparency.
Decision
Rules attached to each scheme.
Trade-off
More text on screen.
Validation
Review with managers for clarity.

Decision 03

Reuse rewards patterns

Problem
Consistency reduces learning cost.
Evidence
Suite strategy.
Decision
Extend existing components.
Trade-off
Less novelty.
Validation
Design QA.

// Evidence from the company site

What Advantage Club says publicly

Source: advantageclub.in, checked 8 Oct 2026. Company claims, paraphrased. Not my measured results.

What Advantage Club says publicly about sales incentives. These are the company's claims, not results I measured.

  • The sales commission and incentive pages describe automated payout calculation and tracking for sales teams.

    Source ↗
  • Go-live is stated as possible in as little as four weeks.

    Source ↗

// AI in the process

How AI shapes the design process

This describes the method my team uses. It is not a measured result.

AI at Advantage Club is part of how my team decides what to design next. We use it to read the data, turn it into a usability and engagement report, and write clear design instructions. Designers then use generative tools to produce reference pages and refine them by hand in Figma.

For Sales Incentives the report looks at how often people open targets and leaderboards, which views are ignored, and where payout questions come from.

  1. Step 1 · AI analysis

    Analyze the data

    Usage and engagement exports are analyzed to find drop-offs, unused features and unusual patterns.

  2. Step 2 · Perplexity

    Check and research

    Data sanity checks and secondary research. Numbers and claims are verified before they go into a report.

  3. Step 3 · AI-assisted

    Write the report

    A usability and engagement report that names the problem screens, the affected users and the likely cause.

  4. Step 4 · Design brief

    Turn it into design instructions

    Each finding becomes a specific instruction for the new or revised page: what to change, for whom, and what to measure.

  5. Step 5 · Google Stitch, Claude Design, Figma AI

    Generate references

    The instructions are fed to generative design tools to produce several reference layouts quickly.

  6. Step 6 · Figma

    Design and review

    Designers choose, rebuild and refine in Figma with the design system, then run usability review before release.

Tools

  • Perplexity · Data sanity checks and research
  • Google Stitch · Quick UI reference screens from written instructions
  • Claude Design · Reference layouts and design exploration
  • Figma AI · Variations and cleanup inside the design files
  • Figma · Final design, design system and handoff

Guardrails

  • AI output is a reference. A designer makes every final decision.
  • Data is checked before it drives a design instruction.
  • Generated screens are rebuilt with the design system, never shipped as they come.
  • Results are stated only when they are measured.

// Outcomes

What exists, and how sure I am

Built

New product offering

Sales Incentives was introduced as a new offering.

Built

Business growth

Contributed to business growth, product adoption and revenue generation.

// Leadership

My contribution, what is next, what I learned

// What I did

  • UX strategy and design direction
  • End to end design lifecycle ownership
  • Sprint aligned delivery

// What is next

  • Adoption tracking per scheme
  • Forecasting for payouts
  • Team leaderboards with opt in

// What I learned

Incentives are emotional. The design that worked best was the one that answered 'where do I stand' before anything else.

Hiring a design leader for an AI product?

I'm open to design leadership roles. Send me a note and I'll reply within two working days.