Control Tower in Power Apps
Challenge: Manual sales analytics process leading to inefficiencies.
Approach: User research → Wireframing → AI Integration → High-fidelity UI.
Solution: A Control Tower Dashboard providing real-time insights & sales forecasting.
Impact: 30% time savings, improved accuracy, and seamless AI-driven adjustments.
Client Brief
Client G is a global leader in beverage alcohol with an outstanding collection of brands across spirits and beer. With over 200 brands and sales in more than 180 countries, Client G sought to improve its Commercial Analytics process by introducing AI-driven automation.
Challenge
Client G’s North American market faced difficulties with a manual Commercial Analytics process. They identified the need for a Control Tower Web-Application to enhance efficiency.
Control Tower is an employee-focused web application that simulates region-wise sales data. The aim was to leverage Machine Learning (ML) and Artificial Intelligence (AI) to automate sales predictions and eliminate inefficiencies in manual analytics.
Context
The Commercial Analytics Process is a methodology followed by Client G’s sales team to generate sales recommendations.
It involves three major flows:
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Business Rule Creation – Users create a set of rules that define which product should be sold at which outlet or region.
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Scenario Planning – AI processes the business rules against the product master data, generating a set of POD recommendations (brand + outlet). Commercial Managers can manually adjust recommendations in a process called commercial adjustment.
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Execution Planning – Commercial Managers set sales goals for the salespersons based on POD recommendations, aligning sales strategies with AI-driven insights.
User Personas
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Commercial Manager – Responsible for overseeing regional sales performance, adjusting AI-driven recommendations, and setting sales goals.
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Salesperson – Uses the system to access sales goals, track assigned PODs, and execute sales strategies at designated outlets.
Approach
We were involved from the discovery phase to understand user needs. By engaging stakeholders and analyzing workflows, we identified key processes and parameters required to define the application flow.
Discover
1
Once we gathered user data and personas, we developed the Information Architecture and built a skeletal framework for the application to outline functionality and interactions.
Describe
2
With insights from the AI team, we designed low-fidelity (LFD) wireframes. Presenting these to the client helped gather additional input directly from end users, ensuring alignment with real-world needs.
Co-Create
3
We utilized all collected data to develop high-fidelity (HFD) screens. The screens were designed with flexibility, ensuring seamless deployment across multiple platforms.
Scale
4
By working closely with Client G and continuously incorporating feedback, we ensured a seamless user experience throughout the application.
Sustain
5
Final Wireframes
The development of the Control Tower Web-Application transformed Client G’s commercial analytics by automating sales predictions and streamlining decision-making. The integration of AI-driven insights improved efficiency, allowing sales teams to focus more on execution and strategy rather than manual data analysis. This solution not only optimized sales planning but also provided a scalable framework for global implementation.
