Introduction to Cross-Selling

  • Lead your customer to buy more or salesperson to sell additional products that are often purchased together with what the customer was initially intending to buy
  • Looks at past sales transactions of all buyers of this product to spot trends about what else they purchased at the same time
  • Apply a statistical algorithm to determine product pairings
  • If Product A123 is in the order, Product B987 and/or C555 is also likely to be ordered

Approach to Cross-Selling

  • Begin with data coming from your invoiced sales files.
  • We apply machine learning to support continuous improvements
  • Initial models include all customers
  • Refinements are made using additional machine learning techniques and are developed over time
  • Recommendations can target subsets like customer types, geography or location characteristics, industry niches, and more.  This allows for greater success in cross-selling based upon real-world variables.  We call these external influences.

The flow of Analytical Process

Cross Sell Flow

Data Transformations Needed

Cross Sell Data

Algorithms are created using R and Python Code

Standard AI/Machine Learning Languages

Cross Sell Algorithms

Examples of Output

Cross Sell Output

Integrate recommendations to Websites, ERP, CRM, Reporting…

Cross Sell Output2

Benefits to Cross-Selling

  • Increased SalesCross Sell Cheer
  • Method to introduce new products
  • Educating customers and salespeople
  • Improved sales order revenue to freight costs
  • Happier customers


Example 2 Trending

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