Mobee · Pin Level Pricing
Killing Mosquitos with Axes
How implementing dynamic, store-level pricing unblocked mission-critical metrics, empowered operational efficiency, and drove up coverage by 20%.
00 - Context & Challenge
The Problem
Fixed Payout Limits
Mission Control's pricing system launched mystery shop missions uniformly. Payouts could not be adjusted at a store level, severely limiting pricing flexibility for harder-to-fill areas.
My Design Mandate
Streamline Pricing Controls
Design a flexible, low-engineering-cost MVP to empower ops team members to configure dynamic, pin-level payouts without rebuilding the entire database.
Product Impact
+20%
Increase in mission coverage speed
Achieved regional completion goals rapidly, often at a lower cumulative payout than generic pricing templates.
- 01 · The Challenge
A rigorous system hampered by uniformity.
Mission Control has a major structural limitation — payout rates cannot be dynamically controlled at the individual store level. This meant remote, less-desirable stores had identical incentives to high-traffic urban venues, leading to massive gaps in coverage.
By analyzing user activity densities and store performance logs, we discovered that simple geographic averages failed. Some stores required distinct incentives to attract secret shoppers.
“Uniform payouts represent an operational bottleneck. To drive consistent brand intelligence, we needed store-level granularity without burdening engineering pipelines.”
Figure 1: A classic pin-level pricing comparison (Advocating Adventurously mission), reaching 87% coverage with optimized, store-level pricing vs 60% with traditional flat rates.
- 02 · Experimentation
Playing favorites: validating the hypothesis.
We ran a lightweight, manually fragmented pilot test. Selecting Walmart and Target as ‘easy’ stores, and specialty or remote branches like Costco or Petco as ‘hard’ stores, we launched them simultaneously with differentiated payouts.
By keeping the average payout at 400 points (350 for Walmart, 450 for Costco), the pilot cost exactly the same as uniform pricing. However, completion times plummeted and coverage rates soared, proving that price elasticity was heavily localized.
Pilot Results
20% Faster
Test missions reached their completion threshold almost twice as fast, with zero net impact on budget.
“Price elasticity was heavily localized — a uniform system was solving the wrong problem entirely.”
- 03 · The Solution
A collaborative MVP side-steps platform constraints.
Our API and database limitations prevented selecting more than 500 pins at a time in the Mission Control web interface. Rebuilding the DB wasn't an option.
Prototype 1: Manual Filtering
Required users to filter pins down to maximum batches of 500, update rates, and repeat. Discarded as too labor-intensive and tedious during dry runs.
Final Solution: CSV Import Bypass
An operations collaborator proposed updating prices inside the density check CSV directly. This bypassed the 500-pin limit, saved 15 sprint points, and simplified the workflow.
- 04 · Outcome & Impact
A stronger platform: cost goals meets coverage.
Optimized Operational Metrics Since Launch
Sustained Performance Gains
Since deploying the integrated store-level pricing in July 2024, Mobee has consistently met its monthly coverage target of 90% while keeping average payouts within the 30% margin threshold.
“The flexibility to dynamically react to local competitive changes and density drops remains critical to satisfying retail client agreements.”
90%
Monthly coverage target consistently met since July 2024
30%
Margin threshold — average payouts kept within budget
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