Conviction is a measure to ascertain the direction of the rule. Unlike lift, conviction is sensitive to the rule direction. Conviction (A => B) is not the same as conviction (B => A).

For a rule A => B:

conviction ( A => B) = 1 - support(B) / 1 - confidence( A => B)

Conviction, with the sense of its direction, gives us a hint that targeting the customers of Organic Hass Avocado to cross-sell will yield more sales of Bag of Organic Bananas rather than the other way round.

Thus, using lift, leverage, and conviction, we have provided all the empirical details to our retailer to design his cross-selling campaign. In our case, we have recommended the top 10 rules to the retailer based on leverage. To provide the results more intuitively and to indicate what items could go together in a cross-selling campaign, a graph visualization of the rules can be very appropriate.

The plot.graph function is used to visualize the rules that we have shortlisted based on their leverage values. It internally uses a package called igraph to create a graph representation of the rules:

Our suggestion to the retailer can be the largest subgraph on the left. Items in that graph can be leveraged for his cross-selling campaign. Depending on the profit margin and other factors, the retailer can now design his cross-selling campaign using the preceding output.