Enabling Dynamic Pricing

Enabling Dynamic Pricing

Building trust through accurate volume estimation

Building trust through accurate volume estimation

Overview_

Overview_

Getamover is a marketplace for moving companies in 23 countries. The business needed to shift from fixed to dynamic pricing, but moving companies didn't trust our volume estimates, blocking the transition.

Problem_

Moving companies didn't trust our volume estimates; they couldn't tell a small job from a big one. Without that trust, dynamic pricing wasn't possible.

My role_

I owned this initiative end-to-end. I defined the research strategy and success criteria, designed the new user flow, and led cross-functional collaboration with Product, CS, and BI, identified the failure point, and drove the pivot.

Problem_

Moving companies didn't trust our volume estimates; they couldn't tell a small job from a big one. Without that trust, dynamic pricing wasn't possible.

My role_

I owned this initiative end-to-end. I defined the research strategy and success criteria, designed the new user flow, and led cross-functional collaboration with Product, CS, and BI, identified the failure point, and drove the pivot.

Homeowners
Focus point

Homeowners
Focus point

Moving companies
Focus point

Constraints_

Business:

• We couldn't hurt conversion rates.

• Any experiment had to include a fast pivot
option.


Engineering: Fixed 6-week timeframe to design, test, and iterate.

How do we ask for more information without driving homeowners away?

Approach_

I interviewed moving companies to understand what information they actually needed. They required accurate room counts and item lists to calculate volume (m³), enabling them to price competitively and win on speed. I defined the research strategy with the head of product: five markets, ~100 participants per market, starting without incentives to measure natural responses.

I analyzed homeowner behavior through heatmaps, drop-off rates, and surveys. Homeowners said they felt overwhelmed by the questions and worried about data privacy, but would share more information if it meant faster results without having to talk to movers first.

Tellet AI Research: Moving companies

Typeform Survey: Homeowners

Competitors Analysis

We ran an A/B/C test. Users selected move size (big, medium, small), then: Variant B asked for cubic meters, Variant C asked for room type. Small moves followed a different path, an open-text item list.

Control version

Variant B: Cubic meters

Variant C: By room type

What failed, and what we learned_

Neither variant reached 80% accuracy. Room size expectations varied by country; no universal model worked. But for small moves, 70% of users completed item lists accurately, providing movers with reliable data.

Insight_

Abstract measurements don't work. Concrete item lists do. I coordinated follow-ups with the CSM and BI lead to validate this finding and align on next steps.

Pivot_

Since conversion rates held steady, I proposed expanding the item list approach to all move sizes. I presented the failed experiment results to stakeholders, showing clearly why we missed the target and how we could course-correct:

• 

• Movers confirmed item lists let them calculate value accurately

• We could build a reliable volume database across markets

• If conversions dropped, we could stop immediately

Homeowners' side

Item list / Variant A

Item list / Variant B.1.0

Movers' side

Before

After

We ran a second A/B test. Users selected Varian A or Variant B, asked them for their item list, and a sub-section. For moving companies, we implemented a before-and-after solution.

Solution_

Variant B.1.0 won and moved into refinement. I redesigned the iconography to clarify move types and improved the labels and helper text for better
context. I also split the item list into a separate step, reducing cognitive load so users focused on one task at a time. Instead of asking homeowners to
guess measurements, we collected concrete item lists, accurate, scalable, and conversion-safe.

Variant B.1.0 won and moved into refinement. I redesigned the iconography to clarify move types and improved the labels and helper text for better
context. I also split the item list into a separate step, reducing cognitive load so users focused on one task at a time. Instead of asking homeowners to
guess measurements, we collected concrete item lists, accurate, scalable, and conversion-safe.

Variant B.1.0 won and moved into refinement.

I redesigned the iconography to clarify move types and improved the labels and helper text for better context. I also split the item list into

a separate step, reducing cognitive load so users focused on one task at a time. Instead

of asking homeowners to guess measurements, we collected concrete item lists, accurate, scalable, and conversion-safe.

Solution_

Variant B 1.0
It didn't required more iterations

Variant B 2.0
Final iteration

Outcome_

The new approach met all success criteria:

  • 89% of homeowners added item lists (target: 70%)

  • 80%+ volume accuracy achieved (target: 80%)

  • 70% of homeowners felt confident in their estimation

Takeaway_

By reframing volume estimation as trust-building rather than data collection, we transformed unreliable leads into actionable intelligence, unlocking the foundation for dynamic pricing.

Moving companies now receive leads with 80%+ volume accuracy, giving them confidence to bid faster and more competitively.