As we enter a new year, many of the challenges facing retailers from 2024 still remain. Retailers are seeking new methods and strategies in order to overcome a testing and unpredictable economic landscape, where inflation is waning but is not fully tamed. The latest surprise fall is indicative of this.
The knock-on effects are that consumer spending habits remain inconsistent and unpredictable, making it increasingly hard to know how to price products in line with inventory levels – and this often leads to overstocked inventory to ensure no sales are missed.
As the post-holiday season emerges and January sales are underway, consumer brands are turning to dynamic intelligence and AI tools to optimise their pricing. It’s a new, agile way for retailers to understand customer habits and offer the price most likely to secure conversions and increase their revenue.
By using AI to analyse data points such as historical pricing and customer spending habits, retailers can accurately pinpoint where they can adjust prices to boost sales and profit margins, and increase sell-through. And ultimately, by employing such data-driven strategies, they can differentiate themselves in their market.
Overcoming hurdles: The retail challenges in post-holiday season
Even before we factor in global macro events, retailers face several challenges in the post-holiday season. With reduced sales and volatile customer traffic, the January sales are brands’ way of moving unsold merchandise from the holiday season in order to maximise revenue and limit waste. This can be particularly necessary with the added level of returns and exchanges that take place throughout the month.
But how do they know what pricing is best to achieve these aims? Retailers will also have acquired new customers and need to create campaigns that keep this audience engaged alongside regular buyers.
Volatility in the global economic landscape has only added to these challenges, triggering high borrowing rates by governments which are, in turn, impacting inflation and economic growth. Judging what prices customers are willing to spend can be tricky, especially as some sectors have totally different experiences from others.
This means consumer behaviour can be erratic, and that means merchandising teams are buying more inventory to ensure no sales are missed. However, not only does this strategy lead to overstocking (and therefore wasted products and money) but it also creates pricing complexities as a result.
So, how can retailers begin to overcome these post-holiday hurdles?
Fast decision-making at scale: How AI optimises pricing
For the post-holiday season, teams can spend hours, even weeks, in spreadsheets working out stock-keeping unit (SKU) markdowns in order to shift out-of-season inventory. As such, efficiently and accurately forecasting demand and how to price SKUs can be a tall order.
Yet AI has progressed to the point where it can provide dynamic pricing recommendations that strike the perfect balance between consumer demand and business targets. So, rather than introducing a blanket markdown of prices, this allows retailers to access the optimum price or discount for each of their products throughout their lifecycle.
By using AI to analyse a stream of inventory, transaction and pricing data, merchandising teams can access real-time suggestions for SKU pricing and promotions. These insights account for business targets and constraints, such as setting a goal to sell through all out-of-season SKUs in the markdown period ‘January’, for any location, e.g. a store or city, and for any channel, be it web or social media.
Key to working out the best markdown is evaluating how demand shifts in line with different prices – this is known as price elasticity. Price influences demand, and by using AI models to analyse hundreds of thousands of SKU/price combinations, teams can access individual price elasticity models to visualise the impact on demand with and without various promotions.
With this data to hand, teams may discover that a markdown isn’t necessary at all or is perfect for specific products – but they know which products to mark down, when, and by how much. Ultimately, AI helps retailers make faster and data-driven decisions to best preserve their margins and drive product sales.
Fuelling broader merchandising decisions
Promotions are not only used to shift products, but also to enhance campaigns and inform broader merchandising decisions. While teams may be accessing price recommendations for a specific period, it’s just as important to have longer-term insights to understand the effect of promotions on the bottom line and overall customer experience.
Through using AI to analyse the impact of discounts and timing on a specific customer profile, for example, retailers can explore a range of promotional strategies to see what approach is likely to land with their target audience. Over time, the technology can incrementally assess the results from these campaigns to improve and inform overall strategy.
Such decision-making is set to accelerate with the integration of Agentic AI assistants. Touted to have a big impact this year, they could transform how teams retrieve information.
A retailer, for example, could submit a request like “What five products are most understocked?” and then ask to have the data broken down in a “monthly report”. The AI assistant can produce this information in seconds and explain the reasoning behind the results.
Evidently, this can significantly accelerate business decision-making and free up time for teams to focus on strategic activities.
Tackling post-holiday hurdles
Facing economic volatility, erratic consumer behaviour and the same annual post-holiday hurdles, retailers are after the tools and techniques to ensure they don’t leave profit on the table and out-of-season inventory gathering dust in the stockroom.
AI shines when analysing data and can redefine a retailer’s post-holiday strategy. It can optimise prices in line with SKU levels, demand and business goals; it can assess promotional strategies over time to enhance the customer experience and revenue; and the rise of agentic AI is set to transform information retrieval and fuel faster and more in-depth merchandising decisions.
Ultimately, through tackling post-holiday hurdles with AI, retailers can reinvent their pricing strategies and differentiate themselves in a turbulent landscape.