Deployed demand forecasting and pricing models that reduced stockouts by 30 percent across marketplaces
A data science and AI layer for an e-commerce analytics SaaS platform, covering demand forecasting, dynamic pricing, marketplace data aggregation and a conversational analytics bot.
Key Takeaways
EComm Pulse is an analytics SaaS for direct-to-consumer brands selling across Amazon, Shopify and other marketplaces. Unico Connect built the data science and AI layer that powers the platform: demand forecasting, dynamic pricing optimisation, marketplace data aggregation, anomaly detection for revenue reconciliation, and a conversational analytics bot called QueryAI.
The result is a 30 percent reduction in stockouts, a 25 percent improvement in pricing accuracy and a data pipeline that is 50 percent faster.
The Challenge
Brands selling across Amazon, Shopify and other marketplaces face data fragmentation that compounds with every channel. Inventory, sales and customer data sit in different systems, each with its own refresh cycle, schema and API quirks. The data is technically available, but making it actionable is work most brand teams do not have the in-house data engineering bandwidth to do.
So operators decide on partial information: they restock too late and run out at the worst time, leave pricing on autopilot while the market moves, and discover marketplace payouts do not reconcile only at quarter-end. The cost is real revenue lost to stockouts, margin lost to mispricing and working capital tied up in the wrong inventory.
EComm Pulse needed one platform: data aggregation across marketplaces, forecasting and pricing intelligence on top of it, and a way for operators to ask questions of their data without waiting for an analyst — at the scale where a single brand generates hundreds of thousands of transactions a month.
Our Approach
We engaged on the data science and AI layer, in three parallel streams: the aggregation pipeline, the modelling layer (forecasting, pricing, anomaly detection) and the conversational interface.
Key decisions:
Normalise, do not flatten
We preserved source-level fidelity across Amazon and Shopify quirks while presenting a unified view, so the models could trust the data and new marketplaces could be added without re-architecting.
Trustworthy models over deep learning
Time-series forecasting and elasticity-based pricing — forecasts operators can trust and pricing recommendations with the supporting numbers, not a black box.
Grounded conversational AI
QueryAI translates natural-language questions into queries against the data model, grounded tightly in the data rather than generating open-ended answers.
The solution we built
A data science and AI layer with four capabilities working together on top of a normalised marketplace data model.
Marketplace data aggregation
Pulls product, sales and inventory data from Amazon, Shopify and other APIs into one normalised, trustworthy model.
Demand forecasting
Time-series analysis with seasonal decomposition predicts restock timing — recommendations with confidence ranges, not point forecasts.
Dynamic pricing optimisation
Analyses competitor pricing, margin targets and demand elasticity to recommend price points, advisory rather than autonomous.
QueryAI conversational analytics
Operators ask questions in plain English; the system maps them to the data model and returns answers with the supporting numbers.
Tech stack
Outcomes & metrics
-30%
Reduction in stockouts across brands
+25%
Improvement in pricing accuracy
50%
Faster data-to-insight pipeline
What Our Clients Say
Suhrid Thacker
CEO, KATALYSST
UNICO Connect had an incredibly meticulous approach from day one. Prior to even onboarding, the team was extremely clear on the task on hand by gaining clarity from us over multiple introductory calls, which set the expectation right for both parties in terms of SOW. The detail taken in developing clear wireframes to drive a de-cluttered and clean UI/UX journey for the end user of our product was clearly understood from day one.
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