It is well known that luxury brands are spending great amounts of money in the destructions of unsold merchandise, extreme measures to avoid the illegal replicas of most of their well-knows brands. Unsold items might fall into the wrong hands and sell them in the so call “black market.”
Luxury brands must develop the processes to modify their pricing models to match demand and maximise sales, while maintaining their brand identity.
Giving the raising of these dreadful practices and no Ethics at all from these hackers, luxury brands must develop the processes which are able to modify their pricing models to counterpart demand and maximise sales, while maintaining their brand identity above all.
The most recent technology, such as Artificial Intelligence (AI) and Machine Learning, can play a fundamental role in helping Luxury brands to identify major indicators of demand from changing-conditions of the specific Market and the generated data, such as sales, promotions, weather and events, to fixed the best possible price.
The destruction of unsold products has a significant impact on a brand’s prestige, and needless to say the considerable collisions it will cause to the global-environmental polices at stake. Some Luxury retailers appear to be taking categorical steps to deal with these issues, with directors announcing strategies for the reduction of the cost of its products in countries where altering their prices is a common malpractice, being one of the main causes for the enterprises surplus stock.
At the same time, these critical changes represent a great opportunity for all Luxury brands, whatever market they operate in, to take more perfected approaches to their pricing problems. By means of AI, retailers can take on a forceful pricing model, which avoid products unsold being left on the shelves. It can be accomplished by charging full rate for the season, adapting pricing strategy towards the end of the season, or when there is excess stock that needs to be sold.
AI can be the answer to the price-optimisation solution retailers urgently need, while making accurate predictions on customer demand and automate pricing, all through every category and stores, and, recognising the relationship between demand and prices changes when retailers deploy a new business strategy.
Indeed, automated-price optimisation means giving a retailer the insights to automatically establish the most advantageous prices to convey the best bottom line, while promptly sensing critical demand signals from fluctuating market conditions and variations of data, as a result of sales, promotions, weather changes or unexpected events.
CONCLUSIONS: Innovative AI and Machine Learning approaches represent a great opportunity for price-optimisation solutions to make it easier for the majority of retailers to be able to keep their iconic brands prestige around the world, avoiding the requirements of broad price cuts and the continuous leverage prices to customer demand.
This strategic data-driven approach could help avoid destruction of products, radically diminish waste, maximizes sales and improves profitability, and most of all, prevents their brands from being illegally duplicated or sold to lower prices.
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