Below we’ll discuss the need to adjust prices more dynamically based on:
Blog 1: The volatility of raw material, energy and logistics costs
Blog 2: Competitor actions
Blog 3: Changes in demand and supply
Blog 4: A combination of the above factors
In this first blog, we’ll focus on pricing decisions based on the volatility of input costs. Let’s start by looking at the magnitude of recent changes of certain input costs.
For example, the price of construction input materials has risen sharply since the beginning of 2021.
As one customer put it, “We were used to pretty stable lumber prices and could increase the prices of our wooden building solutions based on their high quality. That all changed radically last year, and some of our long-term contracts did not take that into account, resulting in negative gross margins.”
The import price for energy more than doubled (115%) between December 2020 and December 2021 in the euro area. It is a sharp contrast to the relative price stability of energy import prices between 2010 and 2019.
(Source: Eurostat, https://ec.europa.eu/eurostat/web/products-eurostat-news/-/edn-20220210-2)
This is a huge blow to, for example, large bakeries that use natural gas in their production process: One large Finnish bakery paid 0,02 euros per kWh in January 2020, but by December 2021, this cost had risen to 0,09 euros per kWh – an increase of 350%!
So, given such input cost volatility, how can companies improve their pricing? You need to consider:
– Necessary frequency of price adjustments
– Contractual limitations and negotiation power
– Technology enablers
– Competitor actions (see upcoming ‘Dynamic pricing blog 2’)
First, how often does your company need to adjust prices given input cost volatility? Many companies update their prices only once or twice a year. Obviously, that approach no longer works in markets where costs fluctuate significantly month-to-month. To determine an ideal pricing frequency, you need to analyze changes to cost structure and the impact on margins both on a historical basis and based on expected future scenarios.
A company’s ability to make price changes differs by industry and market and is also influenced by the negotiation power of the company. For example, in the label materials market, the norm is that companies are not tied to long-term contract prices, so it’s possible to compensate for increased input costs by raising prices. Naturally, pricing flexibility is different for high-end labels such as perfume bottle labels vs. commodity product labels.
On the other hand, some markets have a tradition of negotiating longer contracts with fixed prices. Public sector procurement is an example of this type of purchasing, which is also regulated by laws that apply to contracts over a certain threshold value.
The CEO of a prefab house builder recently told me, “We need to deliver houses to customers based on contract prices even though the cost of lumber, steel, and windows has increased significantly. To add to the pain, some suppliers have had to send us force majeure letters because they cannot guarantee material availability and price levels due to the war in Ukraine.”
The technology enablers for dynamic pricing are:
– Cloud computing
– Pricing rule management
– AI and ML
– Modern Application Programming Interfaces (APIs)
When it comes to dynamic pricing, technology plays an increasingly central role as the frequency of price adjustments increases. Cloud computing provides the needed scalability in a cost-efficient way for cases that require real-time price calculations.
Whatever the price change frequency is, pricing rule management enables assessing the impact of different pricing scenarios as well as automates price adjustments within set boundaries. Pricing rule management must be flexible so the logic can be adjusted to the company’s specific needs.
In many cases, an algorithm-based pricing approach is often a good fit for a company’s needs. But in cases where there is plenty of high-quality data, machine learning (ML) offers the ability to extract knowledge and patterns from a series of observations – potentially leading to new insights on, for example, customer willingness to pay for a product or service. Just keep in mind that the most effort will go into extracting, cleaning, and organizing data when taking ML into use.
Modern APIs are a must-have when it comes to dynamic pricing because they enable integration to internal and external data sources as well as to transaction systems. If the required pricing frequency is, for example, monthly then real-time integration is of less importance and a simple file upload approach may do the job – but even in these cases APIs will provide a future-proof platform.
Our next blog post will discuss how you can benefit from taking competitor actions into account in dynamic pricing.