Our course "Market Basket Analysis: Building a Product Recommendation System for Up- and Cross-Selling" demonstrates how to analyse data from customer transactions to better understand the needs of your customers, to predict their behaviour, and to increase sales and improve profitability by leveraging cross- and up-selling opportunities. Analyzing purchasing patterns in both conventional trade and e-commerce as well as in the financial sector allows the automated generation of data mining models of the customer behaviour. These models can be used in product recommendation engines to provide qualified and individual product recommendations and to thereby increase customer satisfaction and customer retention and loyalty as well as sales per customer by up- and cross-selling. In retail and e-business including banks and insurances, many goods and products, contract items, search terms, web pages, etc. occur in a variety of combinations. An in-depth analysis of this data can reveal hidden behavioural and structural patterns. In this course we will show fast and highly effective methods for shopping basket analysis. We will also describe the practical steps which are necessary to create and exploit such models with the software RapidMiner.

Details

• Course ID: 1104
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: employees of marketing or sales departments, decision makers, analysts
• Previous knowledge: basic knowledge of computer programs
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course includes a compact introduction into the foundations of the relevant aspects of data mining and into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Introduction into shopping basket analysis
  • Apriori and FPGrowth with RapidMiner
  • Frequent Item Sets and how they can be exploited:
  • Combining a lure with a profit earner
  • Generating Association Rules and using them for recommendations
  • Using Association Rules for Cross-Selling
  • Using Classifications for Up-Selling
  • Post-Processing of Frequent Item Sets and Association Rules
  • Skipping non-interesting rules
  • Building a recommendation system with RapidMiner as engine
  • Creating Personal Shopping Assistants
  • Optimizing marketing with individualized product or service recommendations
  Extensive exercises on different data sets will be performed for all topics.

Prices

Value added tax (VAT) may have to be added to these prices.