Training course · Make · Retail & E-Commerce

Make for Retail

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 Retail” is a hands-on course for roles such as E-commerce and online shop teams, Buying and category management and Store and area managers. Rather than learning Make in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Retail & E-Commerce.

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.

Whether bricks-and-mortar stores, online shops, wholesale or omnichannel, retail works with large ranges, thin margins and high customer expectations. Generative AI helps wherever there is lots of text, lots of data and lots of customer contact. It writes product descriptions and category pages, maintains product master data, answers customer service enquiries, analyses reviews and return reasons and supports promotion planning and campaigns. AI agents can check order status, pre-sort complaints or bundle supplier requests. For buying and category management, AI becomes a tool for commenting on sales data and preparing range decisions. The key is balancing efficiency, brand voice and legally accurate product information.

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 and online shop teams
  • Buying and category management
  • Store and area managers
  • Customer service and returns management
  • Retail marketing and CRM

Typical challenges we address

  • Large ranges requiring heavy effort for product copy and master data
  • High volumes of customer enquiries, complaints and returns
  • Seasonal peaks and short-notice promotions
  • Data from tills, web shop and merchandise systems that is hard to combine

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.
  • Produce product copy and campaign content at scale with AI in a consistent brand voice.
  • Analyse customer feedback, returns and sales data with AI in a structured way.
  • Identify service cases and processes that can be automated with AI agents.

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: Retail & E-Commerce
  • Product copy at scale
  • Cleaning master data
  • Supporting customer service
  • Analysing reviews and returns
  • Promotions and campaigns
  • Commenting on sales data
Module 7 Data protection, quality & transfer to daily work
  • Customer data from web shop, CRM and loyalty programmes is subject to the GDPR, and marketing activities to consent and unfair competition rules. Product information, price displays and advertising claims must comply with consumer protection law, and customer-facing AI chatbots must be disclosed as such under the EU AI Act.
  • 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 copy at scale

Manufacturer data and attributes become consistent, search-optimised product descriptions, bullet points and category pages in several languages, with mandatory details carried over accurately. Spot checks happen before publishing.

Cleaning master data

AI helps standardise product data, detect missing attributes, normalise colours and sizes and assign products to the correct categories – with spot checks by the team.

Supporting customer service

Enquiries about delivery status, exchanges, warranty or product details are classified and given draft replies; AI agents can handle standard cases directly. Complex cases are handed to staff with a summary.

Analysing reviews and returns

Customer reviews and return reasons are grouped by themes such as fit, quality or inaccurate descriptions in order to improve copy, range and suppliers in a targeted way.

Promotions and campaigns

Promotion plans, newsletters, social posts and copy for leaflets or displays are created for each target group and aligned with seasonal occasions. Results of past promotions inform the planning.

Commenting on sales data

For buying and category management, AI summarises sales data, explains variances against last year and plan and prepares arguments for supplier negotiations. Figures come from the merchandise system.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. Professional experience in store-based or online retail; no technical background required.

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

Customer data from web shop, CRM and loyalty programmes is subject to the GDPR, and marketing activities to consent and unfair competition rules. Product information, price displays and advertising claims must comply with consumer protection law, and customer-facing AI chatbots must be disclosed as such under the EU AI Act.

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, 7 January 2027 – Fri, 8 January 2027 Hanover Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online 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.

How do I make sure AI product copy is accurate?

Feed the AI only verified product data, specify which details must not be changed and run systematic spot checks. Dimensions, materials, mandatory labelling and advertising claims are critical. In the course you develop a workflow with templates and review steps for producing copy at scale.

Is AI worthwhile for smaller retailers too?

Smaller teams in particular benefit, because AI takes on tasks there is otherwise no capacity for – such as product copy, newsletters or customer replies. Many features can be used without major investment. The course shows pragmatic starting points that work without an IT department.

How much does “Make for Retail” 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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