How to maximize your revenue?


The availability of real-time data on customer characteristics has encouraged companies to personalize operational decisions for each arriving customer (Golrezaei, et al., 2014). For instance, Orbitz.com has found that Mac users spend on average $20 to $30 more per night on a hotel than Windows users . Therefore the online travel agency can show different and more expensive hotels to Mac users (Mattioli, 2012).

The key question in this paper is: Given the complexity of coordinating real-time, front-end, customer-facing decisions with the back-end supply chain constraints, what policies should companies use to take advantage of real-time data?

Once a customer arrives on the website, his or her customer type will be revealed. This can be based on computer type , zip code, gender or on any available information that is relevant for the company. Based on the customer’s type and the remaining inventory, the firm offers an assortment. An example of a website that shows different assortments based on customer type is Zalando. When I visited the website last week, I was automatically directed to the ladies department without logging in. Associated with each customer type is the probability of purchasing each product under each assortment. This means that the company calculates, for example, the probability that a 40 year old woman who lives in a high-income neighbourhood purchases a certain dress which is showed between other dresses under Assortment A but also the probability that she purchases the same dress that is showed between different dresses under Assortment B. The authors of this paper want to design an revenue-maximizing algorithm that determines the assortment to offer to each arriving customer, taking into account the customer type and current inventories.

In the paper, the authors propose a couple inventory-balancing algorithms. An inventory-balancing algorithm makes use of a discount factor that depends on the fraction of the product’s remaining inventory. This means that when the inventory of a product drops, the discount becomes higher which results in a lower discounted revenue. Upon the arrival of each customer, based on the customer’s type, the algorithm offers the assortment that maximizes the expected discounted revenue. By adjusting the revenue of each product according to its remaining inventory, the algorithms hedge against the uncertainty in the types of future customers by reducing the rate at which products with low inventory are offered. For example, a pair of red leather boots (low inventory) that normally would be showed at page 1 to a 40 year old woman who lives in mens-inner-real-leather-western-glossy-red-side-zip-high-heel-ankle-boots-made-in-koreaa high-income neighbourhood (in case of enough inventory) might now be showed on page 4, because it is likely that she is willing to buy another, more expensive, pair of shoes. When a 35 year old woman who lives in a low-income neighbourhood arrives at the website 5 minutes later, these red leather boots will actually be showed at page 1 because this maximizes the revenue.

These inventory-balancing algorithms work very well in cases with significant uncertainty in the market size, yielding 5%-11% more revenues than reoptimization methods. Reoptimization methods work extremely well and yield nearly optimal revenue because they can effectively ration the inventory to all customers. This means that inventory-balancing algorithms are more suitable for situations with a lot of uncertainty. Also, the inventory- balancing algorithms have a strong performance under both nonstationary and stationary demand processes. This implies that, even when there are sudden shocks in the customers’ arrival patterns, for example in case of seasonality, the algorithm maintains a strong performance guarantee. Another advantage is that this inventory-balancing algorithms are simple and flexible which makes it possible to combine them with reoptimization methods.

Golrezaei, N., Nazerzadeh, H. & Rusmevichientong, P. (2014) Real-Time Optimization of Personalized Assortments. Management Science. 60 (6), 1532-1551.

Mattioli, D. (2012) On Orbitz, Mac Users Steered to Pricier Hotels. Available: http://www.wsj.com/articles/SB10001424052702304458604577488822667325882. Last accessed 23-4-2015.

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