In this quickly developing digital economy, businesses reap the benefits from a vast amount of data by using dynamic pricing to change prices in real-time. Dynamic pricing is the technique of determining a product or service's price based on the state of the market.
Dynamic pricing can be used in various price setting methods:
• Cost-based pricing, which maintains profit margins at a predetermined level while constantly adjusting prices in accordance with business costs.
• Competitor-based pricing, which considers pricing choices made by competitors.
• Demand-based pricing, which makes the prices rise as consumer demand rises and supply declines, and vice versa.
Setting the right price for a good or service is an old problem in economic theory. Many pricing tactics exist, and they vary depending on the goal being pursued. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Additionally, many situations can coexist in the same business, for various commodities or customer segments.
Some of the most important questions that retailers frequently have are:
• If we want to sell all of our stock in less than a week, what price should we set?
• In light of the current status of the market, the season, the competition and other factors, what is the reasonable pricing for this product?
The vast majority of pricing algorithms estimate the demand function using historical sales data. The four key stages of a typical pricing algorithm's workflow are as follows:
• The engine consumes historical information on price points and demand for specific items to process it using the dynamic pricing algorithm.
• The demand function is built based on discovered dependencies.
• To provide ideal prices, it analyzes hundreds of pricing and non-pricing elements.
• The algorithm repeats the cycle once the suggested prices are applied, accounting for the most recent repricing outcomes.
Most dynamic pricing engines are based on two-stage machine learning. The first stage implies calculating the precise effect of price changes on sales. The price optimization stage uses the results of the first stage to recommend prices for the whole portfolio.
Because of the complexity of dynamic pricing, different modules are used depending on the demand.
Fig. 1: Module usage. Source: McKinsey & Company
Long tail module
This module is for new products or products with little or no historical data. The main challenge for this module is to use product attributes to match the products with little purchase data with the products that have rich purchase data, so the prices can be informed by the related rich data.
Elasticity module
The elasticity module accounts for seasonality when calculating the effect of price on demand.
Key Value Items (KVI) module
Key value items are popular items whose prices consumers tend to remember more than other items (for a grocery store, this would be eggs, milk and bread). By making sure that goods that have a significant impact on a customer's sense of pricing are priced appropriately, the KVI module seeks to manage consumer price perception. For grocery companies, this is crucial.
Competitive-response module
This module uses detailed pricing information from competitors and the effect those prices have on the company's customers to respond in real-time.
Omnichannel module
Companies set different rates for different channels in order to both price discriminate and entice customers to use less expensive channels. Omnichannel modules ensure that prices in different channels are coordinated.
Considering all the benefits it provides to businesses, dynamic pricing will likely entirely replace fixed prices in the near future. The dynamics of the strategy may change to put more emphasis on understanding customers and the impact the price has on their decisions.
References:
[1] Bright. (2022, June 23). How Machine Learning Is Helping In Providing Dynamic Pricing. Medium. https://medium.com/total-data-science/how-machine-learning-is-helping-in-providing-dynamic-pricing-7efdb8af9083
[2] Dynamic pricing. (n.d.). Big Data Analysis. http://ibigdata.in/works/dynamic-pricing/
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