An Application of Marketing Analytics to Estimate the Impact of Out-of-Stock Instances for Retailers

Sales figures often fail to accurately reflect the true demand observed by retailers. While sales data provides insights into consumer purchasing patterns, it does not capture the full picture of consumer behaviour. There are several reasons for this discrepancy. Firstly, sales figures only consider completed transactions and do not account for potential sales lost due to stockouts, pricing issues, or poor customer service. These missed opportunities significantly impact retailers' understanding of the actual demand for their products.

Furthermore, external factors unrelated to consumer demand can influence sales figures. Seasonal fluctuations, promotional activities, and sudden market trends can artificially inflate or deflate sales numbers, leading to an inaccurate representation of true demand. For example, a retailer may experience a sales surge during a specific holiday period, but it may not indicate a sustained increase in demand throughout the year. Relying solely on sales figures can result in misinterpretation and a misalignment with customers' genuine needs and preferences.

When retailers face uncertain demand, they can utilize observed sales to update their demand estimates. However, this learning process is constrained by the inventory they carry. When demand exceeds inventory, such as during an out-of-stock event, retailers generally cannot directly observe the actual demand. Accurate demand forecasting becomes crucial not only to reduce out-of-stocks (OOS) but also to minimize overstocking (OS). This use case focuses on a food retailer operating through multiple franchisees, aiming to provide customers with restaurant-quality foods, great value, and unique service. Their product lineup showcases top-quality, exclusive, flash-frozen items that are not available in other food retailers.

To estimate out-of-stocks (OOS), it is necessary to know the inventory levels at the store and the demand experienced by the store. In this case, the supplied data includes end-of-day inventory on hand, as reported by the stores to the head office. However, it is important to note that some records may be blank or contain negative values, which were eliminated from the analysis. Figure 1 provides an overview of the specific data fields used in the predictive models for reference. Eliminating these from the analysis cut approximately 1% of the records. In Figure 1, we present the data fields are used in the predictive models:

Figure 1. Data Fields used in the Predictive Model

The newly developed daily model has shown impressive accuracy in predicting sales, with an average percentage error of just 2% at the day and store level. This level of precision is a significant improvement compared to the forecasts generated by the retailer itself. To provide context and demonstrate the superiority of the new model, Figure 2 presents a comparative analysis of the accuracy between the two forecasting approaches. Both forecasts were generated six weeks prior to the commencement of the promotion period and flyer distribution.

By evaluating the performance of the new model against the retailer's forecasts, it becomes evident that the new model offers a more reliable and robust prediction of sales. The new model was more accurate in every Price Look Up (PLU), a KPI equivalent to SKU, in the test; on average with a WMAPE of 7.5% vs the retailer’s mean absolute value weighted by store sales of 32%. The low average percentage error signifies that the new model is consistently close to the actual sales figures, enabling the retailer to make more informed decisions regarding inventory management, resource allocation, and overall business strategy. This level of accuracy is particularly valuable when preparing for promotional activities and flyer drops, as it allows the retailer to anticipate customer demand and ensure optimal stock availability to meet the expected surge in sales.

The utilization of the new model, with its significantly improved accuracy, empowers the retailer to proactively respond to market dynamics and enhance operational efficiency. By having access to reliable sales forecasts well in advance, the retailer can make timely adjustments to their supply chain, optimize pricing strategies, and allocate resources effectively to maximize profitability during the promotion period. The superior accuracy of the new model instills confidence in the retailer's decision-making process, ultimately leading to better customer satisfaction, increased sales, and a stronger competitive position in the market.

Figure 2. Forecast Accuracy Comparison

The next step is to estimate the OOS losses. For our analysis, only the days in which the PLU is on promotion, with a discount greater than 0 are used. As a conservative approach, a predictive model was used to first group store-days into deciles based on their expected sales. As far as the model is concerned, sales for store-days within a decile should be virtually the same. Then, the stores are divided into two groups: a high inventory group, which has inventory on hand at much higher levels than the predicted sales, and a second group in which inventory on hand is much closer to, or even less than predicted sales. Then the difference is taken between the actual, observed sales for both groups as the effect of having higher inventory levels on hand. In other words, where stores order much more inventory than they are likely to need, we expect sales to be higher than if they have little excess inventory on hand or if they run out of stock.

In Table 1, we show a set of store-days for PLU 30, Italian Meatballs. The model predicts sales for these stores will be very similar 9.7 and 9.6 units per day. However, the Hi in Table 1, we show a set of store-days for PLU 30, Italian Meatballs. The model predicts sales for these stores will be very similar 9.7 and 9.6 units per day. However, the Hi Inventory group has, on average, 7.7 units on hand before the start of the current sales day (i.e. yesterday night) while the Low Inventory group has only 1.5 units.

Table 1

In theory, the only difference between the two store-day groups is inventory. In all other respects, they should generate the same sales, but they don’t. The Low Inventory group generates 2.3 units per day less. The Hi Inventory group generates 10.1 units, whereas the Low Inventory group generates only 7.8 units. This difference is aggregated across all store-days and multiplied by the sales price of the day to arrive at an estimate of dollar loss. When we divide this dollar loss by the annual actual sales, we arrive at the percentage figures for 5 specific PLUs presented in Table 2 below:

Table 2

Table 2 provides valuable insights that can help estimate the financial impact of out-of-stock (OOS) losses for retailers. It is important to note that reducing OOS not only increases sales but also brings higher percentage benefits to franchisees compared to the retailer. This is due to the distribution of margins, where franchisees retain the majority of sales profits. Therefore, mitigating OOS not only reduces losses but also lowers carrying costs for franchisees, which becomes increasingly significant with rising interest rates.

For instance, based on the figures provided by the retailer, their annual revenue is approximately $400 million. Using the average OOS value from Table 2, we can estimate the annual revenue lost due to stockouts to be around $19.2 million. Considering an overall 30% margin, with the corporate share at 3%, the estimated losses due to OOS amount to $5.8 million in margin for the entire system, with the retailer experiencing a loss of $576,000. However, the more accurate model demonstrates the potential to avoid most of these losses through improved inventory management.

In summary, accurately forecasting consumer demand is of paramount importance for retailers, particularly when dealing with the challenge of out-of-stock (OOS) sales. While sales figures provide valuable insights, they often fail to capture the true demand observed by retailers due to various factors, such as missed sales opportunities and external influences. By utilizing advanced analytics and predictive models, retailers can gain a deeper understanding of customer behavior, integrate marketing efforts with inventory management, and make informed decisions to reduce OOS occurrences.

The financial impact of OOS losses can be substantial, with revenue losses and margin erosion affecting both the retailer and franchisees. Estimating the magnitude of these losses helps highlight the urgency of addressing OOS through effective inventory management strategies. By leveraging accurate demand forecasting models, retailers can optimize their supply chain operations, improve inventory replenishment processes, and ultimately reduce OOS occurrences.

Furthermore, accurate demand forecasting not only minimizes revenue losses but also leads to improved customer satisfaction and reduced overstock. By aligning inventory levels with actual demand, retailers can ensure product availability, avoid missed sales opportunities, and deliver enhanced customer experiences. Moreover, as interest rates rise, the impact of reducing carrying costs and avoiding revenue losses due to OOS becomes increasingly significant for both the retailer and franchisees.

In conclusion, accurately forecasting demand and effectively managing inventory are vital for retailers to mitigate the financial and operational impact of out-of-stock sales. By embracing advanced analytics and developing robust predictive models, retailers can align their marketing efforts with inventory management, reduce revenue losses, optimize profitability, and enhance customer satisfaction. Emphasizing the importance of accurate demand forecasting enables retailers to proactively respond to market dynamics, capitalize on sales opportunities, and drive sustainable growth in today's competitive retail landscape.

About the authors

Dr. Murat Kristal is an Associate Professor of Operations Management at the Schulich School of Business at York University in Toronto, Canada. He received his Ph.D. from the University of North Carolina at Chapel Hill in 2005. Dr. Kristal is the founding director of Master of Business Analytics, and Master of Management in Artificial Intelligence programs at Schulich. Currently he serves as the director of MBA in Technology Leadership Program at Schulich. In 2016, he was named one of the Top 40 Professors under 40. In 2020, he was the recipient of the Award of Excellence from Minister of Colleges and Universities, Ontario, Canada. He has published in top Operations Management Journals throughout his career, and he works with various companies in North America and Europe to help them achieve their analytics, AI, and digital transformation goals.

David Beaton is the co-founder of both Navigation ME, a company that helps marketing clients become more effective through the development of customized advanced analytics (predictive models and optimization algorithms), and Crater Lake & Company, a company dedicated to helping marketing clients make sense of it all by integrating advanced analytics, innovative research tools, and creative and media planning disciplines. Navigation ME has a strong track record of lifting business performance across B2B and B2C sectors and over a wide variety of brands and industries. The team has also developed successful innovations in the application of marketing analytics to solve problems in measurement, attribution and optimization. Navigation ME clients have used the work to improve results in customer acquisition, upsell and cross-sell, and retention. The company has developed a suite of tools that measures the effect of complex, dynamic omnichannel marketing campaigns and optimizes budget allocations for better results. This approach is both holistic and rigorously validated, making it a reliable choice for improving business performance. David completed his MBA at the Rotman School of Management at the University of Toronto.