Retailers today are facing a number of dynamic challenges—unpredictable consumer behaviors, geopolitical tensions, and supply chain disruptions, to name a few. One of the biggest issues follows the recent surge in e-commerce during the pandemic, which saw a correction due to evolving purchasing patterns in 2023, and has left retailers grappling with excess inventory. However, there are now powerful tools to help retailers overcome overstock (and understock) challenges which we have revealed in our recently published whitepaper: Retailer’s Guide to Overcoming Overstock.
While traditional inventory-based pricing thrived in stable markets, its efficacy wanes in today’s volatility. Research by Telsey Advisory Group has found that, across segments, inventory growth averaged 46% in Q2 of 2022. In apparel and e-commerce, that figure was considerably higher, at 65.6% for both sectors. Clearly, a new, modern approach is required to tackle these challenges.
Here we have outlined five of the key takeaways from our whitepaper on how retailers can tackle excess inventory challenges in 2023 and beyond.
Understand & adapt to the market
Retailers are currently facing a market that is highly unpredictable, from supply chain disruptions triggered by unexpected events such as the COVID-19 pandemic and geopolitical tensions like the conflicts in Ukraine and Gaza to escalating costs fueled by inflation. Consequently, the task of forecasting demand and maintaining optimal inventory levels becomes increasingly complex. The pandemic years witnessed a surge in e-commerce, leading to stockouts followed by swift adjustments in inventory strategies. However, the landscape shifted once more in early 2023, with a resurgence in in-store purchases, an overall decline in consumer spending, and changing energy prices influencing buying patterns.
Many retailers find themselves with surplus stock and depleted resources, while others, anticipating market unpredictability, erred on the side of caution, avoiding losses on obsolete stock but missing sales and growth targets. In the face of these formidable challenges, retailers require innovative solutions to synchronize prices and inventory levels for optimal business outcomes.
One such solution lies in the realm of a new generation of inventory-based pricing. To navigate these dynamic market shifts successfully, retailers must invest in sophisticated software capable of analyzing changes and providing the adaptability necessary for sustainable growth.
Rethink your pricing strategies
Modern challenges require modern solutions. In the case of inventory-based pricing, it has been a fixture in the pricing toolbox for decades. Retailers employing this approach set product prices according to stock levels, with reactions to stock changes aligning with specific business goals. Traditionally, this method has been executed manually or with basic tools like Excel sheets, where adjustments are made based on historical data, seasons, and intuition. In today’s modern world, the pressing question emerges: is this manual approach still sufficient for the demands of contemporary retail?
One powerful solution is inventory-based pricing amplified by artificial intelligence (AI), specifically machine learning algorithms (ML). This is also known as predictive pricing. These algorithms are able to take various shifting influencing factors into account to make accurate demand predictions and price recommendations. By using this software, retailers can transition to a strategy that relies on accurate forecasting rather than relying on guesswork and static rules.
Analyze your data
Retailers sit on a huge amount of internal and external data relevant to their pricing strategy, but many do not have the capability to effectively analyze and act on this data – thus leading to lost profits and inventory mismanagement. Those still following a rule-based system, as opposed to one enhanced with machine learning, are simply not making the most of their available data.
Machine learning algorithms are able to analyze an extensive amount of historical data encompassing a wide array of internal and external factors, including competitor prices, weather conditions, holidays, and marketing expenditures. Within this dataset, these algorithms pick up on patterns in behavior and can make price adjustments accordingly, eschewing the rigid rules of traditional pricing tools. The algorithm not only achieves accuracy in forecasting demand but also provides recommendations for stock adjustments.
Importantly, these predictions are useful for both short-term precision as well as long-term planning, offering valuable insights even in volatile market conditions. This ability to provide proactive guidance positions machine learning algorithms as invaluable tools, ushering in a new era of dynamic and forward-thinking pricing strategies.
Accurately determine price elasticity
Central to all ML inventory-based pricing tools is the concept of price elasticity, a crucial metric for effective pricing recommendations. Price elasticity measures the sensitivity of the quantity demanded or supplied of a good to changes in its price, specifically calculating the percentage change in demand or supply resulting from a one percent change in price. This metric hinges on factors such as product necessity and the availability of substitutes.
Understanding a product’s price elasticity empowers retailers to make informed decisions about discounts and price increases. This knowledge facilitates the optimization of prices for maximum profit. Additionally, awareness of elasticity guides decisions on discounts for seasonal stock. An elastic demand may prompt a small discount to hasten sales, preventing unsold stock obsolescence. Conversely, for an inelastic product, like a dress in high demand due to a celebrity endorsement, a slight price increase can capitalize on trends, maximizing profits. This application of price elasticity exemplifies the nuanced decision-making facilitated by modern inventory-based pricing tools.
Adopt inventory-based pricing with machine learning
To stay competitive in today’s dynamic marketplace and overcome stock-based challenges like excess inventory, retailers need to adopt a predictive pricing solution such as 7Learnings. By doing so, they gain the ability to set explicit business goals—whether achieving sales targets or swiftly selling outdated items—allowing the algorithm to tailor pricing decisions accordingly. Additionally, it is possible to establish company-specific price rules, ensuring AI-driven adjustments align seamlessly with the overarching company strategy.
This multifaceted approach not only enhances precision in pricing decisions but also affords businesses the adaptability to meet diverse objectives, making predictive pricing a comprehensive solution for dynamic and goal-oriented pricing strategies.