Scale Purchased Node
The ‘Scale Purchased’ node changes the Willingness To Pay (WTP) values of individual Customers within the Input WTP Matrix according to the Product each Customer purchased.
Scaling individual Customer WTP values depending upon earlier Product purchases is helpful in a number of situations. For example, ‘Learning Curves’ may be simulated as Customers gain more familiarity with Products after purchase, and ‘Switching Costs’ may be simulated as it becomes increasingly difficult for a Customer to switch to another Product after purchasing the same Product multiple times. Firms may also experiment with the effectiveness of free trials, expecting that the WTP of Customers will increase after they have tried using the Product. And the ‘Law Of Diminishing Marginal Utility’ can be simulated for Customers who purchase Products multiple times.
Individual WTP values can be scaled if a Customer does purchase the Focus Product or does not purchase the Focus Product. The WTP of Products related to the purchased Focus Product may also be scaled. This is useful when, for example, a Customer’s WTP for a Brand increases after purchasing just a single Product. Several Scale Purchased nodes can be cascaded one after the other to fine-tune the updated WTP values depending upon the relationship between the purchased Focus Product and other Products in the Market.
The Willingness To Pay (WTP) of a Purchased Product may increase as the Customer moves up the learning curve. Or the WTP may decrease if the Product loses its novelty and the Customer grows tired of consuming it.
This example simulates the Market for Frozen Dinners. The types of Products include:
Customers in this Market eat Frozen Dinners on a regular basis. But after they finish one type of dinner their WTP for that dinner will decrease. If they’ve consumed the same Product too many times they will ‘burn-out’ and select another type of Frozen Dinner.
The ‘Input Product Array’ is generated by the ‘Customer Distributions’ node based upon the types of Frozen Dinner Products in the Market.
The ‘Willingness To Pay’ (WTP) Matrix is enriched by the ‘Simulate Market’ node. This node predicts which type of Frozen Dinner each Customer will Purchase for their 1st meal.
The Customers who Purchased the ‘Chicken’ dinner have been highlighted along with their original WTP values. These WTP values will be decreased by the ‘Scale Purchased’ node (compare them with the values from the ‘Output WTP Matrix’).
The WTP column having the same name as the value in the ‘Customer Selected Focus Product’ (that is, the ‘Purchased’ column) is scaled.
In this case, just the Focus Product itself is changed. But the ‘Scale Product Set’ may or may not include the Focus Product, other Products with the same Brand, Store, Location, Family, Category or Platform as the Focus Product, and Products that are top Competitive Rivals with the Focus Product.
The user has selected to ‘Add a Fixed Value’ of -1 to the Purchased Product to decrease the Customer’s WTP for that Product after they’ve consumed it.
The ‘Advanced’ Configuration Dialog allows the user to offset and/or qualify the name of the WTP column to scale.
Port-0 Product Array
When the ‘Output Product Array’ is passed through the second ‘Simulate Market’ node the ‘Quantity Error’ field indicates that some Customers have switched dinners from their 1st Purchase to their 2nd Purchase.
Port-1 WTP Matrix
The values in the ‘Output WTP Matrix’ have been scaled depending upon whether the Customer purchased the Product and whether the Product is part of the ‘Scale Product Set’.
The Customers who Purchased the ‘Chicken’ dinner have again been highlighted. When compared with the upstream ‘Input WTP Matrix’ these selected WTP values have all been reduced.
When the ‘Output WTP Matrix’ is passed through the second ‘Simulate Market’ node it becomes clear which Customers switched dinners from their 1st Purchase to their 2nd Purchase.