The instability of the global economy, which results in sudden variations in demand, raw material costs and currency exchange rates, is combined with inflation which remains high in certain regions. Added to this, a change in consumer expectations: in search of attractive prices, they now favor sustainability, ethics and price transparency. It is in such a context that the issues surrounding algorithms have intensified: by increasing market surveillance and allowing an almost immediate reaction to competitors’ movements. Faced with these challenges, AI opens up promising prospects for optimizing pricing strategies while meeting consumer expectations.
How does AI optimize retail pricing strategies?
Artificial intelligence pricing allows retailers to become more responsive to competitive and rapidly changing markets, while adjusting prices based on demand, competition and raw material costs. It also optimizes inventory management by adapting prices to availability levels, thus maximizing margins.
AI pricing makes it possible to achieve a very high level of price personalization. By analyzing purchasing behaviors, browsing histories and other customer data, it helps create a personalized pricing strategy that goes beyond setting prices.
AI pricing is therefore a powerful lever for optimizing pricing strategy and obtaining a real competitive advantage for companies. This is why many companies, particularly in the retail, transportation and hospitality sectors, use AI pricing solutions. In the hotel industry, we can cite Airbnb with its Smart Pricing tool allowing it to automatically adjust host prices based in particular on seasonal demand, local events, room availability, and competition.
What are the limits of AI in price optimization?
The use of artificial intelligence in the field of pricing constitutes a strategic advance for companies, but it nevertheless brings its share of challenges between data quality, consumer acceptance and organizational adaptation.
Stéphane Marty, economist, raises a major concern: algorithms could, intentionally or not, reproduce behaviors close to collusion. Price adjustment algorithms can, after several cycles, stabilize prices, giving the impression of an implicit agreement between companies, which affects consumer confidence. By adjusting prices based on demographic or behavioral criteria, AI optimizes revenue, but can lead to price discrimination. These practices, perceived as unfair, raise questions about the transparency and fairness of pricing policies.
In order to reduce the risks relating to these two points, companies must introduce control mechanisms into their systems and ensure that algorithmic bias is minimized. The technical teams responsible for implementing such models must integrate ethical and competitive constraints into their models.
Data Scientist, experts in digital ethics, lawyers, etc. must work together to create and supervise these tools. This transversality will ensure not only the effectiveness of the algorithms, but also their compliance with standards and their adequacy with consumer expectations. We can particularly add to this an issue at the level of communication carried out by the company. Indeed, it represents a fundamental challenge since it will directly affect the consumer’s perception of the pricing policy put in place.
Towards an organizational transformation to innovate
In addition, the adoption of AI pricing involves a fundamental organizational transformation. Companies must invest in technology as well as in team training. This involves establishing transitional governance that drives change and establishes a culture of innovation. It is essential to train employees in new tools and new methods while establishing a climate of continuous improvement to maximize the benefits of AI pricing.
Although AI presents significant opportunities in terms of the efficiency of a player’s pricing model, it should not neglect the human and ethical aspects. The success of these strategies relies on a continuous exchange between technological innovation, consumer expectations and regulatory compliance, in order to create pricing models that are both efficient and responsible.
Source: www.e-marketing.fr
