Weathering the Winter in Retail

As Christmas draws near, Christmas lunch logistics and unbought Christmas presents lie at the forefront of consumers’ minds, but are retailers ready to take full advantage?

christmas shopping, price optimization,machine learning

The quarter’s seasonal boom for retailers has been greater than usual, with ONS citing a 7.4% rise in retail sales during October alone, the fastest growth rate in 14 years. It has been a profitable few months for retailers, but conditions like these highlight the disparity between those who have chosen to innovate and those that haven’t.

The Challenges and Opportunities of the Season

Black Friday and Christmas are two holidays that pose a serious challenge for retailers, with pricing remaining a consistent challenge. The issue has been keenly felt on Black Friday these past two years, as consumers across the country complained the discounts weren’t high enough and that stock availability was limited. The key issue is that many retailers still rely on gut feeling to determine pricing strategy, leading to sub-optimal results once the customers arrive.

In addition to these commercial holidays, the season is rife with additional variables that affect consumer buying habits. Weather, for example has had a massive effect on the industry this month as customers flocked to their preferred outlets to buy warmer clothes for Christmas. Such spikes in demand can be extremely lucrative propositions for businesses able to take advantage of them. Unfortunately, the sudden nature of these fluctuations often leave retailers ill-prepared, resulting in poor margins or unhappy customers.

Cold Facts

Such sacrifices are patently unnecessary given the technology available to modern retailers. Machine learning is designed to predict customer behavior – based on multiple factors - and adjust pricing and replenishment decisions accordingly.

In the context of Black Friday, machine learning adjusts individual product prices to optimize profit and customer experience. By calculating the ideal balance of price vs. customer satisfaction for each individual item, the algorithms ensure that the retailer extracts the maximum sustainable value from each sale. Customers remain satisfied as they are paying a price they deem fair, ensuring they do not consider the competition, while profits remain high due to optimal margins. Additionally, using artificial intelligence in this way frees up staff to address more important issues, such as ensuring the increased footfall does not impact customer experience as it has done in the past.

Looking at the Big Picture

However, these holidays make up only a fraction of the overall challenge that retailers face during winter. Weather and temperature also play a sizable role in determining consumer behavior during the colder months. Machine learning algorithms are designed to consider multiple external variables in their calculations. This allows for rapid price adjustments in response to changes in consumer context. As the algorithms predict what customers are prepared to pay, the customers’ desire for convenience can be leveraged for higher margins. This is offset by calculated pricing adjustments based on competitor pricing, ensuring that competitors are unable to steal customers by undercutting.

The winter season is rife with challenges for retailers looking to secure advantage over their competitors. A variety of colder weather coupled with commercial holidays accelerate consumer buying habits and accentuate mistakes in pricing. Business leaders looking to make the best decisions and reap the lion’s share of profits should use machine learning algorithms to optimize their pricing. The ability to retain high margins without sacrificing customer satisfaction will be a deciding factor in who benefits the most this Christmas.

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