Marketways worked with a regional money-services provider to build a foreign-exchange analytics system for a large branch network serving varied urban and remote markets.
The business objective
The branch network served locations with very different customer and transaction profiles. The company needed a clearer way to anticipate demand and translate branch conditions into consistent operational decisions.
How Marketways translated the problem
We represented the branch network through a customised quantitative model connecting location, customer mix, transaction demand, operating formulas and branch capacity. The model allowed management to compare different local realities through one analytical system without assuming that every branch behaved alike.
What Marketways built
- Operational dashboard: We brought branch-level demand and operating measures into a management view that could be compared across the network.
- Business formulas: We developed and tested formulas that translated the company’s operating logic into consistent measures for branch planning and review.
- Demand models: We modelled demand across different customer and location profiles, including visitor-heavy areas, workforce communities, established residential and commercial areas, affluent neighbourhoods and remote parts of the UAE.
- Testing: We tested the formulas and predictive models against branch evidence so that the outputs reflected the operating differences across the network.
Why location and customer mix mattered
A branch serving tourists faced a different demand pattern from a branch serving a workforce community, a high-income residential area or a remote location. We treated geography and customer composition as operating variables. One network-wide average could not guide every branch.
How our engagement contributed to business impact
- Demand modelling
- Method: Demand forecasting and predictive modelling.
- Evidence: Branch histories, location, customer mix and transaction demand.
- Decision supported: Anticipate demand and adjust branch plans for different local conditions.
- Impact measure: Forecast error by branch group, customer segment and planning horizon.
- Operational formulas
- Method: Statistical analysis and operational measure design.
- Evidence: Business rules, branch histories and material operating exceptions.
- Decision supported: Apply consistent planning calculations while identifying branches that required separate review.
- Impact measure: Formula stability, exception frequency and coverage across branch types.
- Management dashboard
- Method: Business intelligence and dashboard design.
- Evidence: Demand, capacity, forecast deviations and operating measures by location type.
- Decision supported: Compare branches and focus management attention on material changes and exceptions.
- Impact measure: Branch-level coverage, management use and resolution of operating exceptions.
Evidence of impact
The engagement was evaluated through forecast error, stability across customer and location segments, branch-level demand coverage, operating exceptions and management use of the dashboard. These measures connected statistical performance to the decisions the network needed to make.
Business use
The system gave management a structured basis for comparing branches, anticipating demand and fine-tuning operating decisions for different local realities.
Services and methods used
Services: Sales Forecasting & Demand Planning, Operational Performance Diagnostic and Business Systems Design & Architecture.
Methods: Statistics & Econometrics, Machine Learning & Predictive Analytics and Data Foundations & Business Intelligence.