Collaborating Authors

Which of These 7 Pricing Strategies is Right for Your Business? Growth Hackers


Basically, this means determining the most your customer would be willing to pay for your product or service. Value-based pricing is less effective in very heavily saturated market, where you are more likely to be undercut by another business. Ideally, you should be able to offer higher quality, more convenience, or more streamlined experience in order to succeed with value-based pricing. An example of value-based pricing would be if you were a website designer who typically charges by the hour. As you become more skilled and can offer a high-quality product in a few hours, you may be designing a client's entire website in half a day at a bargain rate.

On Dynamic Pricing with Covariates Machine Learning

We consider the dynamic pricing problem with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is jointly determined by the price and an observable covariate vector $x_t\in\mathbb{R}^d$ through an unknown generalized linear model. Most of the existing literature assumes the covariate vectors $x_t$'s are independently and identically distributed (i.i.d.); the few papers that relax this assumption either sacrifice model generality or yield sub-optimal regret bounds. In this paper we show that a simple pricing algorithm has an $O(d\sqrt{T}\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$ (which can even be arbitrarily chosen). The upper bound on the regret matches the lower bound (even under the i.i.d. assumption) up to logarithmic factors. Our paper thus shows that (i) the i.i.d. assumption is not necessary for obtaining low regret, and (ii) the regret bound can be independent of the (inverse) minimum eigenvalue of the covariance matrix of the $x_t$'s, a quantity present in previous bounds. Furthermore, we discuss a condition under which a better regret is achievable and how a Thompson sampling algorithm can be applied to give an efficient computation of the prices.

Dynamic ticket pricing taking root in Japan amid pandemic

The Japan Times

Amusement parks, baseball clubs and other entertainment businesses in Japan are increasingly adopting dynamic ticket pricing in a bid to avoid creating crowds amid the COVID-19 pandemic while stabilizing revenue. Those businesses hope that dynamic pricing will help bring in more customers as tickets are cheap on days with low demand. The ticket sales market in Japan in the year ended in February 2021 shrank to a quarter of that of before the pandemic, according to Pia Research Institute, an arm of ticketing agency Pia Corp. Meanwhile, the total value of dynamically priced tickets sold in the country is expected to grow by 1.5-fold to around ¥6.2 billion in the year ending this month from the previous year, according to Dynamic Plus Co., a Mitsui & Co. unit that uses artificial intelligence to offer dynamic pricing services. Under a dynamic pricing plan, prices are changed depending on demand until the day of the event.

Thompson Sampling for Dynamic Pricing Machine Learning

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.

AI and Dynamic Pricing – Secret Weapon of Tech Giants Today


The invention of price tag took place in the 1870's to maintain the fairness of everybody looking to buy the product they love. Dynamic pricing had always been the norm ever since human history. A century back even the ticket for a cinema was charged less for a matinee screening as compared to the usual popular evening shows. Born out of the '80's, dynamic pricing is now one of the most commonly used marketing techniques by several industries. Anyone old enough will remember the American Airlines' Super Saver fares online commercial where the airline played a major cutthroat with the fares.