Training course · Make · E-Commerce

Make for E-Commerce Teams

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Make for E-Commerce Teams” is designed for roles such as E-commerce managers and shop owners, Marketplace and category managers and Product data and PIM managers and shows step by step how to use Make productively, safely and transparently in E-Commerce – from the fundamentals to advanced workflows.

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. Over two days you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within your organisation.

In e-commerce, every product page, search query and customer enquiry counts. Generative AI opens up new possibilities for online retailers and brands: AI assistants create and optimise product descriptions, translate catalogues, structure product attributes, analyse customer reviews and support marketplace listings. Automation and AI agents maintain product data, monitor prices and stock, answer routine questions and prepare campaigns for sales events. Shop analytics also become more accessible, because questions about conversion, baskets and returns can be asked in natural language. Success requires clean product data, clear quality rules for generated copy and compliance with the many information obligations in online retail. Then AI becomes a lever for visibility, conversion and efficient operations.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • E-commerce managers and shop owners
  • Marketplace and category managers
  • Product data and PIM managers
  • Digital marketing and SEO specialists
  • Founders and owners of online shops

Typical challenges we address

  • Keeping thousands of product records complete, consistent and multilingual
  • Increasing visibility in search engines and on marketplaces
  • Analysing and handling returns, reviews and customer enquiries efficiently
  • Delivering sales events and range changes with limited resources

What you will be able to do

  • You can build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • Create and maintain product content efficiently, in several languages and at consistent quality with AI.
  • Analyse shop, review and returns data with AI and derive concrete actions.
  • Design AI-supported shop processes in compliance with consumer protection and data protection law.

Course outline

Day 1

Module 1 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team

Day 2

Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
Module 6 Practice lab: E-Commerce
  • Product description generation
  • Product data enrichment
  • Catalogue translation
  • Reviews and return reasons
  • Marketplace listing optimisation
  • Natural language shop analytics
Module 7 Data protection, quality & transfer to daily work
  • Online retail is subject to extensive consumer information duties, price indication rules, unfair competition law for product claims and the Digital Services Act for platforms; AI-generated copy must not contain misleading statements. Customer data from orders and reviews must be processed in line with the GDPR.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Product description generation

Using manufacturer data, attributes and brand guidelines, AI creates search-friendly product copy, bullet points and meta descriptions, which the team spot-checks and approves.

Product data enrichment

AI extracts attributes such as material, dimensions or compatibility from data sheets, normalises spelling and assigns products to the right category to improve filters and on-site search.

Catalogue translation

Product copy is translated and localised for new markets using a glossary for brand terms and units, with native-speaker review for key pages.

Reviews and return reasons

Customer reviews and return reasons are clustered by topics such as fit, quality or delivery, so buying, product development and content teams can make targeted improvements.

Marketplace listing optimisation

AI adapts titles, bullet points and keywords to the requirements of individual marketplaces and checks listings for missing mandatory information or policy violations.

Natural language shop analytics

Exports from the shop system and web analytics are analysed with AI, for example conversion rate by category, basket abandonment or best-sellers, including hypotheses for optimisation tests.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. You work in an online shop, on marketplaces or in digital retail and have a basic understanding of your shop system.

Technical requirements

You will need a laptop with an up-to-date browser and a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

Data protection & compliance

Online retail is subject to extensive consumer information duties, price indication rules, unfair competition law for product claims and the Digital Services Act for platforms; AI-generated copy must not contain misleading statements. Customer data from orders and reviews must be processed in line with the GDPR.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Munich Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Online Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Stuttgart Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Berlin Request

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

Does AI-generated product copy hurt SEO?

Not if it is helpful and unique. The problem is interchangeable copy that merely rephrases manufacturer information. Precise attributes, usage examples, answers to frequent customer questions and a quality check produce copy that convinces search engines and shoppers alike. We practise exactly these prompting and review routines.

Who is liable for errors in AI-generated copy?

The retailer is liable for all product information, regardless of how it was created. Incorrect dimensions, materials or efficacy claims can lead to legal warnings. AI copy should therefore be based only on verified product data, and sensitive claims such as health or environmental statements should be approved separately.

How much does “Make for E-Commerce Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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