As food and drink businesses strive to protect margins at a time when cost volatility is affecting everything from ingredients to energy and transportation, intelligent pricing strategies are essential to remain competitive. Rules that may have been used to inform pricing decisions in the past, may no longer be enough to cut through the complexity of today’s fast-moving trading environments, and a more sophisticated, data-driven approach is needed.
For example, global supply chain challenges could cause the price of raw materials to soar, or consumer demand for a specific product could decline sharply due to changing market trends and evolving consumer preferences regarding brand, sugar, sustainability and ethical consumption.
In a high volume, low-margin trading environment, such as FMCG, pricing mistakes based on costs that have been underestimated or demand forecasts that are not up to date, can quickly erode profit margins and impact the company’s bottom line, writes Ebru Yildirimli-Kafkas, Associate Vice President specialising in AI and new technologies at Siemens Advanta.
In a dynamic pricing environment, AI and machine learning models can help decision makers to manage complexity and make better pricing decisions by considering multiple parameters in real time. If a new regulation is introduced that requires a change of packaging, or consumer trends have shifted away from certain products or ingredients, these intelligent data-based models can help the business to identify issues quickly and take aversive action. For example, if demand for a specific product, such as ice creams or soups increases sharply due to extreme weather warnings, action could be taken quickly to bolster stocks. Of course, where AI and machine learning models are used to inform strategic pricing decisions, it’s important to keep a human being in the loop to protect the business from potential over- or under-reactions.
There are several pricing models to be considered. Dynamic pricing, for example, is a pricing model used by Uber and other ride-hailing services to optimise profits and rewards for drivers. This model is rooted in real-time supply and demand data, allowing increased prices at peak demand periods, for example, when hundreds of people are leaving a major concert venue. Flight booking systems used by Ryanair and Easyjet also have a similar pricing model. Despite being particularly effective at driving revenues and optimising margins in fast-moving environments, dynamic pricing isn’t necessarily right for FMCG business as customer trust could be eroded if cost increases are perceived as unfair, which may impact consumer loyalty.
Many businesses will be familiar with the cost-plus model, which essentially involves applying a fixed margin to the cost of goods and services. This is a transparent pricing model that can work well for high-volume, low-margin businesses, such as discount retailers. It can help brands to build consumer trust, but when operating with ultra-low margins it is vital to keep a close eye on product life cycle costs, and fixed overheads, to ensure the company remains profitable. Issues can arise if the business is undervaluing its products or its brand proposition.
A value-based pricing model is suited to brands selling products or services that are perceived as premium in some way. A key product might have innovative features that are highly valued by customers in a specific market, for example, a foldable smartphone or one with an advanced camera system that works seamlessly with other branded goods in the same ecosystem. Built on multi-layered customer and market data, the value-based pricing model needs to be as dynamic as possible, so it can adapt to shifts in customer perception quickly.
Before choosing a pricing model, businesses must understand what is influencing purchasing decisions currently, and what customers value about their products and their brand. Data intelligence about the market – for example, the direction that competitors are moving and how quickly – combined with industry analytics, should also be considered.
In today’s volatile and fast-moving trading environments, ‘conjoint analysis’ is often advisable as it enables businesses to mitigate risk by combining more than one approach. Conjoint analysis usually starts with in-depth market research to learn about the preferences of the target market, and this information is used to guide strategic decisions, such as whether to invest in new product development and price setting. This customer analytics and other data derived from simulation technologies, along with business data and data supplied by third parties, can then be used to train a bespoke pricing model that is both value-based and capable of reacting to market or regulatory changes quickly.
In a world where managing complexity and cost volatility have become the norm for many businesses, a data-driven approach to pricing is essential. Using a holistic deep understanding of what customers value to optimise pricing decisions can differentiate businesses, protect margins and drive value in the long term.
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