Your best-looking products may not be your best bets.
High revenue can hide ad dependency, weak margin, return risk, or stock pressure.
Focused marketplace decision analytics
Know what to grow, what to cut, and where to protect margin.
I help marketplace teams turn product, advertising, price, stock, review, and return signals into clear weekly actions: what to grow, what to clean up, what to protect, and what to monitor next.
- Spot products with real growth potential before budget goes to the wrong SKUs.
- Separate ad spend that expands total sales from spend that only makes reports look good.
- Use price and stock signals to protect margin, avoid stockouts, and reduce slow inventory.
- Leave each review cycle with 2-3 concrete actions, evidence, risk, and metrics to watch.
Built for teams operating on leading platforms such as Amazon and other online marketplaces, where the reports exist but the next high-value move is still unclear.
Focused marketplace decision analytics
Know what to grow, what to cut, and where to protect margin.
Turn product, ad, price, stock, review, and return data into weekly actions that grow the right products, clean up waste, and protect margin.
Approach
What you get: clearer actions from the data you already have.
You do not need another pile of reports. You need a practical way to decide which product to push, which campaign to clean up, which price to test, and which stock risk to manage next.
We start with the decision questions that can create value fastest, then turn your available data into short action notes: the recommendation, the evidence behind it, the expected effect, the risk boundary, and the metric to watch.
Why teams get stuck
The costly decisions are usually hiding between reports.
Ad sales can hide wasted spend.
Some campaigns generate attributed sales without clearly increasing total product sales.
Price and stock decisions can fight each other.
A price cut, price increase, or ad push can damage margin or inventory if the signals are read separately.
The team sees the numbers, but not the next action.
The missing piece is often a short, evidence-based priority list that turns data into decisions.
Focus areas
Where the work creates value
Product and portfolio growth
Prioritize which products to scale, defend, repair, or remove from active focus.
Advertising contribution and budget waste
Find campaigns and terms where spend should be protected, reduced, restructured, or tested.
Price and stock balance
Identify where price, demand, and inventory need to be managed together to protect value.
Weekly decision rhythm
Turn scattered signals into 2-3 weekly actions with evidence, risk boundaries, and follow-up metrics.
Working model
A first meeting should quickly reveal where the value is.
- First meeting Map your platform setup, data sources, pressure points, and decisions that could create value soon.
- Diagnostic review Choose the product, advertising, pricing, or stock questions most likely to change action.
- Weekly action notes Receive concise recommendations with evidence, expected effect, risk limit, and what to monitor.
- Later automation Automate repeatable checks only after the weekly decision model has proved useful.
The contribution is not more analysis. It is better judgment under marketplace pressure.
The work is designed for teams that need sharper priorities without adding a heavy reporting burden. Each recommendation connects the available data to a concrete action: why it matters, what could improve, what could go wrong, and how to check the result.
Credibility
Scientific method, translated into practical business decisions.
Current research collaboration
The Management of Agentic Services research project is part of an international academic collaboration linked to Villanova University Human-Centric and Agentic AI Laboratory (HAAL) and Copenhagen Business School.
View the Management of Agentic Services projectPublications in leading international academic journals
International Journal of Operations & Production Management Journal of Business Logistics Journal of Environmental Management Waste Management Industrial Marketing Management Retail, logistics, returns, circular operationsRelevant MBA courses taught at the university
Data Analytics for Business Technology and Operations ManagementFirst step
Use the first meeting to find the decisions where analytics can pay off fastest.
In the free first meeting, we can review your platform setup, available data sources, current decision pain points, and the first areas where a focused analysis could lead to action. You do not need to share sensitive commercial data before the meeting.
Book a free first meetingShort briefing
Share enough context to make the first conversation concrete.
The briefing form is not for sharing commercial data.
Information submitted through this website is used to plan the first meeting, understand the need, and tailor the conversation. Company datasets, customer lists, order files, inventory files, or similar operational data are not requested through this form.