2.3.4 Customized Pricing Bid Response Model

2.3.4 Customized Pricing Bid Response Model (CPBRM)

Majority of the literature in the price-optimization area focuses on pricing of consumer goods or the optimal design of auctions but, a large percentage of firms face pricing decisions in a business-to-business setting where a customer requests bids from a small set of competing firms getting a quote for the product or service. When the total annual sales to the firm requesting the bid does not justify a dedicated sales person on behalf of the firm responding to the bid, many firms have started using bid response models to provide customized pricing recommendations on what price to offer for the business being bid upon. (Agarwal and Ferguson, 2011, p212). This means that when the firms are
This implies, that each unique price is quoted on the basis of difference in the attributes of the transaction. These differences can include customer types, product types and transaction types. Each segment contains customers with similar attributes with similar price sensitivities. Customer attributes can include location of the customer, size of the industry the customer is in, type of business of the customer, customer uses of the product, purchase frequency of the customer, and purchasing sophistication of the customer. Product attributes can include type product, version of product (latest vs old), use of product (end product/intermediate), lifecycle stage, and purchase frequency of the product (e.g. Best selling product vs not) . Transaction attributes can include order size of the order, other products included in the order, channel of delivery/payment, specific competitor, time of the order and the urgency is of the bidder. Past quotes and current quotes of the competitor can also be taken into account when building the model, however, to get this data is a big challenge for the firms. The most important assumption of this model is that the seller retains historic “win/loss” data. This means, that the customer keeps a track record of all the past purchases of the customer including the data of unsuccessful orders and also the type of products
This is due to the fact that Meta has an advanced IT system, where the information of every client is retained including the quotes given to each client. Secondly, the size of each deal is not very large, and hence, a dedicated salesperson is not required for every unique order. A salesperson typically gives a large number of quotes to different customers everyday. The product offered by the company is toilet cubicles and most of the orders received are not very large in value. Hence, it is not profitable for the company to dedicate a specific salesperson to study each and every order. And lastly, despite a low quote given on some orders, it is still not certain that the order will be successful. This is because, there are different types of clients, and some clients such as government clients are automatically quoted the lowest price, however still each and every order of this kind is not