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Commerce Data & AI CheckClarify the next steps for your commerce data

We review your data landscape and assess opportunities for better operational management and AI. You receive a data map and prioritised starting initiatives.

What opportunities are hidden in our commerce data, systems and processes?

Commerce Data & AI Check shows where data from shops, marketplaces, inventory and accounting holds untapped potential for analytics, AI-assisted analysis, forecasting or automation. For McFilter, we used just such scattered sources to create a shared data foundation for margins, purchasing and order planning. No new data was needed: it already existed, but in separate places. The check is a compact way to find out whether a similar approach is worthwhile for you.

What changes afterwards

  • A data landscape map shows gaps and manual handovers.

  • Opportunities are assessed by value, feasibility and effort.

  • You know which opportunities call for analytics, AI or process automation.

  • Starting initiatives are prioritised with a recommended next step.

Typical questions

Our data is spread across five systems, and no one has the full picture.
We calculate margins and channel profitability in Excel.
We have plenty of AI ideas, but can our data support them?

When Commerce Data & AI Check fits

A good fit when

commerce processes depend on exports and manual interpretation.
margins, stock coverage or channel profitability are hard to analyse.
you lack a basis for deciding on a data warehouse or AI initiative.

Less suitable when

you need an SEO audit or shop performance check.
you are planning a BI project without a commerce focus.

The approach at a glance

01

Map the landscape

Before
Our data is spread across five systems, and no one has the full picture.
After
A data landscape map shows gaps and manual handovers.
02

Understand data flows

Before
We calculate margins and channel profitability in Excel.
After
Opportunities are assessed by value, feasibility and effort.
03

Prioritise opportunities

Before
We have plenty of AI ideas, but can our data support them?
After
You know which opportunities call for analytics, AI or process automation.

Results and your contribution

What you receive

Commerce data map
Assessed opportunity list
Roadmap with prioritised starting initiatives

Not included

SEO audit
Tool comparison
Data warehouse
Implementation

Your contribution

We need access to the relevant systems and a meeting with the people who work with the data today.

You do not need to prepare the data.

When AI offers the greatest value

Explore AI & automation

The check shows which of your existing commerce data can create value and in what order analytics, AI or automation is worthwhile