Retail Prescriptive Analytics Implementation
Budget: ₹750 – ₹1,250 INR
Our multi-channel retail operation has outgrown the descriptive dashboards we built years ago; I now need an MIS professional who can push us into true prescriptive analytics. The core objective is to turn the streams of data we already collect—POS, e-commerce, inventory, loyalty, and promotional spend—into clear, data-driven recommendations I can act on each week.
I will provide direct access to our SQL data warehouse and the raw exports coming out of Shopify and Lightspeed. Your task is to design the analytics layer that mines this information, models the business levers, and serves up “what should we do next?” guidance that my merchandising and marketing teams can trust.
Because this is prescriptive work, I’m expecting optimisation techniques rather than simple trending. If Python (pandas, scikit-learn, PyMC), R, or even a tightly configured Power BI model can get us to SKU-level reorder quantities, price elasticity insights, and promotion lift forecasts, I’m open to the stack you prefer—so long as everything sits on top of our existing Microsoft SQL Server and can be refreshed automatically.
Deliverables I need to see:
• A clear data pipeline—from extraction and cleansing through to the modelling layer—documented and scheduled.
• At least two optimisation models (e.g., inventory reorder, markdown timing) exposed through an easy interface or embedded in a Power BI / Tableau dashboard.
• A short user guide plus a walkthrough call so my in-house analyst can maintain and extend your work.
I’ll handle licensing for any commercial tools you specify ahead of time; just flag them early. Code must be delivered as source files in a private Git repo with reasonable commenting and version history.
If you have rolled out prescriptive analytics in retail before, especially around inventory or dynamic pricing, that experience will stand out.
I will provide direct access to our SQL data warehouse and the raw exports coming out of Shopify and Lightspeed. Your task is to design the analytics layer that mines this information, models the business levers, and serves up “what should we do next?” guidance that my merchandising and marketing teams can trust.
Because this is prescriptive work, I’m expecting optimisation techniques rather than simple trending. If Python (pandas, scikit-learn, PyMC), R, or even a tightly configured Power BI model can get us to SKU-level reorder quantities, price elasticity insights, and promotion lift forecasts, I’m open to the stack you prefer—so long as everything sits on top of our existing Microsoft SQL Server and can be refreshed automatically.
Deliverables I need to see:
• A clear data pipeline—from extraction and cleansing through to the modelling layer—documented and scheduled.
• At least two optimisation models (e.g., inventory reorder, markdown timing) exposed through an easy interface or embedded in a Power BI / Tableau dashboard.
• A short user guide plus a walkthrough call so my in-house analyst can maintain and extend your work.
I’ll handle licensing for any commercial tools you specify ahead of time; just flag them early. Code must be delivered as source files in a private Git repo with reasonable commenting and version history.
If you have rolled out prescriptive analytics in retail before, especially around inventory or dynamic pricing, that experience will stand out.