The recent growth of eCommerce has presented businesses with the ability to compile and process data pertaining to consumer characteristics in a faster, more cost-effective way than ever before.
By evaluating personal purchase thresholds, companies are better equipped to correlate pricing with which customers are willing to pay them. This is being done by implementing automations that target customers evaluated to be those who are willing to spend more than others. By tailoring the price based on individual spending trends, companies can earn extra money by making sales that they would lose out on. Leveraging such a strategy boosts conversion rates and profits while having no negative impact on the unit price.
This is not a new strategy. Whether the customer entered a music shop looking for an instrument to acquire, a watchmaker’s shop to purchase a pocket watch, or a tailor shop looking for a new tailcoat, the approach of the experienced seller would always involve a screening process to access how much this customer was able to and willing to pay for the product they were seeking. Sellers would accomplish this by observing the customers’ appearance and asking questions, which while harmless on the surface, had a very real impact on their ultimate pricing offer.
The market has worked this way for years, ultimately helping to drive the prices for most consumers down, but the currently available technological prowess permits the more prevalent, personalized, and intense application of these pricing methods. The primary difference between the sellers at brick-and-mortar businesses of the past and contemporary eCommerce businesses is that AI has now optimized the evaluation of customers, helping to determine the pricing strategy for such a consumer.
This strategy has been generally well-received by knowledge economists who view it as beneficial to both buyers and sellers in the marketplace, leading to an increase in both consumer and producer welfare. This strategy can secure a high number of buyers by offering a cost-efficient deal while allowing sellers to claw back some of the fixed costs without raising the prices for everyone in order to do so. Since certain consumers are willing to pay more, a sophisticated pricing personalization strategy will yield a higher value for nearly everyone involved. By squeezing out extra revenue from those who are willing to pay higher prices, this strategy assists in financing the costs that allow sellers to continue selling to marginal consumers at lower prices.
Consider the above OECD graph to help clarify this methodology. We see a ranking of ten customers by their willingness to pay ranging from highest to lowest (with customer 1’s willingness to pay is the top of the range and number 10 being the bottom). Assuming production costs are the same, if the company charged the same price to every client, it would set the uniform selling price at a level higher than the cost of production, something that is imperative if the product is to yield a profit. That cuts down the range of customers who would be willing to purchase to only those whose willingness to pay bars exceed that selling price (in other words only customers 1 through 6 would make the purchase, while 7 through 10 would not).
Conversely, if the business sets its charges based on a personalized pricing model indicated by the yellow dots on the graph, customers 1 through 5 would be charged more (and be willing to pay more) than a standard price, while the standard price of the product would be perfectly suitable for customer 6. Because the company now charges a lower price for customers 7 through 9, they will also buy the product at this point. Because customer 10s willingness to pay does not reach even the level of production costs, they will not purchase the product.
Therefore, this strategy yields two points via which the company can stand to draw in higher profits:
Customers 1 through 5, who highly value the product or service, are willing to pay a higher price, which will do so, increasing the revenue margin per sold unit.
Customers 7 through 9, who would have been unwilling to pay the original price for the product, can now purchase because they are being offered an optimum price. This too yields higher revenue to the company that it would have otherwise sacrificed.
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