/case-studies / ecommerce-ai-pricing
DATA & PREDICTIVE AI

AI-driven pricing improves margin for an e-commerce brand

client
E-commerce Brand
industry
E-commerce
services
AI / ML · Data
duration
5 months
fig.80// skillikzmodeltraininfervectorAImodel.evalrollout87%98%accuracyusage81coveragelive
// OVERVIEW

E-commerce Brand — a e-commerce organisation — engaged Skillikz on ai personalised pricing: Dynamic, margin-aware pricing tuned by machine learning. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 5 months.

// TECHNOLOGIES
PythonPyTorch / scikit-learnMLflowSparkAWS SageMaker
+9%
gross margin
real-time
pricing
sell-through
guardrailed
by policy
01 // THE CHALLENGE

Flat pricing left margin on the table and failed to respond to demand, competition or inventory positions.

02 // OUR APPROACH

We built margin-aware pricing models with policy guardrails, integrated into the storefront and continuously evaluated.

Demand and elasticity modelling
Policy guardrails on price moves
Storefront and ERP integration
Experimentation and continuous tuning
03 // THE RESULTS

Gross margin rose 9% with healthier sell-through, all within pricing guardrails the commercial team controls.

Smarter prices, set safely — margin we were leaving on the table every day.

Commercial Director · E-commerce Brand
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
→ all_case_studies

Want outcomes like these?

Tell us your challenge and we'll map an AI-led path to measurable results.

[ start_a_conversation → ]