The challenge: Pricing at global scale, without global visibility

The client runs one of the largest franchise chains in the food and beverage industry. Over 100 countries, and more than 50 million customers served daily. Their franchise model gave regional owners the freedom to set their own menus, quantities, and prices.
But that freedom came at a cost: no consistent system for knowing whether a price was right.
Too high and you lose the customer. Too low and you lose the margin. And with raw material costs, labor, compliance, competition, and local demand all shifting constantly, the right price wasn’t something you could guess at.
Too high and you lose the customer. Too low and you lose the margin. And with raw material costs, labor, compliance, competition, and local demand all shifting constantly, the right price wasn’t something you could guess at.
The brief was clear:

  • Build a pricing tool that identifies the optimal price for every product, at every store, across every channel.
  • Factor in competitor pricing, regional demand, customer preferences, and not just internal cost data.
  • Make it usable enough that franchise owners would actually act on its recommendations.

Starting with the playbook, not the code

Before any development began, W2S reviewed the client’s existing pricing process end to end. Mapping out how recommendations were currently made, where the gaps were, and what rules needed to be built into the engine. Only then was the system designed around it.

A pricing engine built around how franchises actually operate

The platform was tailored to the store-level reality of a franchise business, giving each owner recommendations specific to their store and sales channel and not generic guidance from a central team. Owners could review, adjust, and push final prices directly to their POS system from one place.

Result: a seamless flow from recommendation to implementation, with no manual handoffs or data translation.

 

Machine learning that gets smarter over time

Dynamic pricing wasn’t hardcoded. It was built on ML models that pull from both internal sales data and external market signals, continuously updating recommendations based on actual performance rather than static assumptions.

Result: pricing recommendations that improve with every cycle, not just at launch.

 

Three modules, one complete picture

The platform was structured around three focused modules: Pricing Recommendation, Competitive Analysis, and Pricing Performance Review. It gave franchise owners and central consultants a complete view of where prices stood, how they compared, and what happened after changes were made.

Closing the loop on performance

Post-implementation tracking was built in from the start. Every recommendation gets measured against actual sales, and comparative reports show owners exactly how each pricing change performed versus past decisions.

“The system doesn’t just tell you what to charge, it tells you whether it worked.”

Built on

Backend: Python, Django Framework Frontend: Angular Data Analytics: Python, Pandas, NumPy, Jupyter Cloud: AWS Project management: JIRA, GitHub, Confluence Communication: Slack, Google Meet/Zoom

A 16+ person team comprising UI/UX, data analysts, backend and frontend developers, mobile developers, a cloud architect, QA, project management, and a customer success manager worked across the full delivery.

Launch and what’s next

With 52+ weeks in development, the platform evolved by adding new data sources, refining ML models, and expanding to new regions as the franchise footprint grows.

The numbers

Metric Result
Pricing accuracy Continuously improving via ML
Profit margins Higher through optimized, willingness-to-pay aligned pricing
Sales predictability Enabled through advanced analytics
Recommendation-to-POS workflow Fully streamlined

Sitting on pricing decisions you’re not confident in? Let’s fix that.