When it comes to navigating through pricing, one is faced with two methods: Rule based pricing or machine learning based pricing. Rule-based pricing or rule engines are the traditional way of pricing in retail and online retail. Most online retailers change prices using this methodology. In this case prices are changed according to static formulas. This type of pricing can also be dynamic. For example, a retailer can set the rule to always match the third cheapest competitor price. This way prices can change everyday or even multiple times per hour.
There are two main shortfalls of rule based pricing:
- It takes a lot of manual effort to administer the price formulas and
- price rules are often based on parameters that are easy to measure but entail only limited information about customer behavior
Retailers that use rule-based pricing give away profit and revenue potential because their pricing is not based on the willingness-to-pay of their customers.
Next generation dynamic pricing based on machine learning algorithms
On the contrary, advanced machine learning algorithms can measure the willingness-to-pay of customers. To be exact they measure price elasticity which is the key to set optimal prices. Combined with advanced forecasting algorithms they can model the price demand curve of each product. Based on this, companies can steer prices automatically towards their company goals. They do not have to create rules and test their performance. Their pricing reacts dynamically and automatically to a change of customer behavior. As a result, retailers apply discounts more differentiated and smarter.
Real AI vs. fake AI
With advanced pricing software, you can simulate various price scenarios for individual categories, forecasting revenue, sales and profit outcomes for your respective targets. Pricing software also makes it possible to employ different strategies for selected categories which are expected to sell out quickly, for example.
Advanced pricing software uses machine learning so that retailers can capture unique customer behavior and thus improve their pricing. The machine learning algorithms are used to calculate products’ price elasticity based on data gathered from past sales activities, such as those from Black Friday in 2019. In other words: sales volume and all the relevant influencing parameters that go into achieving them – from the competitors’ prices to weather and seasonality – are all factored in by the software when calculating optimal prices. Compared to the more traditional rule-based pricing, dynamic pricing offers highly accurate predictions and enables differentiated, smart price setting, which in turn leads to higher revenue and profits for retailers.