Imagine being a sales agent.
While the primary task is to drive sales, a significant amount of time goes into resolving service-related issues.
The goal was to reduce this effort and give agents more freedom to focus on what matters most.
Show the image of POS agent to sales and service at a store
Sales focus ⚖ Service effort
So we started a search to understand where support was most needed.
Across research documents and journey findings in retail POS, one area kept surfacing — promotions.
To understand where promos were affected
involved across the POS experience
to understand real resolution challenges
We began noticing a pattern — promotions kept surfacing across journeys.
Promotions were correctly configured, but didn’t always persist consistently till billing.
This became the focus area for deeper investigation.
Browse → Plan → Perks → Promo → Bill
But what within promotions was causing the issue?
Plan change
promo affected
promo affected
promo missing
When a promotion drops, the resolution happens reactively.
Customers either contact support or visit the store, and agents use the Promo Correction Application (PCA) tool to manually resolve the issue.
Resolution can take multiple steps and sometimes more than one billing cycle.
Promo drops
↓
Customer notices issue
↓
Agent investigates
↓
Uses PCA tool
↓
Resolution later
reactive effort
manual steps
delayed resolution
The journey worked, but it relied heavily on manual effort.
Manual investigation
👤 → 🔍 → 🧾
👤 → 👤 → 👤
🧾 → ⏳ → next cycle
This led us to ask:
Could we reduce the dependency on reactive corrections?
• reducing reactive correction effort
• simplifying resolution for agents
• improving promo continuity across the journey
Reactive → Simplified
Manual effort → Guided flow
Multiple steps → Fewer steps
We explored ways to simplify how agents identify and resolve dropped promotions.
Investigate → escalate → correct → wait
Before
Identify → resolve
After
In some cases, the original promotion was no longer eligible.
We explored enabling agents with guided alternatives when a dropped promo could not be restored.
manual investigation
eligibility unclear
agent decides next step
Before
context surfaced
eligibility verified
guided next best action
After
Used order and transaction signals to detect dropped promos earlier.
Reduced dependency on manual identification.
Enabled earlier initiation of correction.
Manual effort to detect the problem
Before
Order details / transaction history dataset used
After
The journey today depends on fixing issues after they appear.
We explored how the experience could gradually move from reactive correction to proactive resolution.
Reducing the need for correction becomes as important as simplifying it.
Reactive
Fix after issue appears
↓
Proactive
Reduce chances of issue occurring
Simplified today’s correction journey while exploring a path towards proactive resolution.
The direction aligns with ongoing platform improvements and future roadmap evolution.
Simplifying correction today
Enabling proactive resolution tomorrow
Because sometimes the best design outcome isn’t fewer screens — it’s fewer doubts.
If you’ve made it this far, thank you for your time and curiosity :)
Twinkle Budhraja © 2026
Always learning. Always designing.
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