CASE
STUDIES

Simple elasticity-based pricing was not automated and could not suggest optimal prices. Lack of standardized data and alignment on scenarios meant chaos in fast-paced environments.

Decentralized pricing across stores meant premium and high-traffic locations were priced too low relative to their cost structure and demand. Store location potential was underestimated.

Inefficient staffing levels failed to match volatile visitor traffic and seasonal sales peaks. Lack of forecasting horizons led to expensive last-minute scheduling. Staffing followed cost-minimization instead of profit potential approach.

Marketing and loyalty teams had to manually research customer behavior. This high manual effort led to recycling of old target groups and campaigns performing below their potential.

Initial customer segments relied only on RFM (Recency, Frequency, Monetary value), missing behavioral nuance. Campaign targeting was ineffective and generic.

Lack of a churn model and intransparency into tracking data and online footprints of customers turned reactivation campaigns into guesswork instead of challengable and measurable activities.

Complexity in forecasting demand across a vast, perishable product portfolio. Production teams and sales teams operated independently, leading to waste and stockouts.

Current practices only considered basic data for revenue estimation. These sources had either bias towards platforms or industries or lacked data on platforms.

New EU legislation requires the introduction of pay equity guidelines. Organization was already leading the charge, but wanted to know if its budget is sufficient to close gaps.

Introduction of new SaaS tools, rising complexity in worker compliance requirements, made decision-making times much longer. Many solution integration produced erranous data, requiring the setup of a data governance team.

Harsh operational environment and long planning and transportation windows require clinical precision when planning machine maintenance.