Aggregate Sales Research Articles

Antibacterial resistance and the cost of affecting demand: The case of UK antibiotics

Consumption of broad-spectrum antibiotics is associated with rising antimicrobial resistance (AMR) levels. The use of broad-spectrum drugs, particularly of cephalosporins, quinolones, and co-amoxiclav contributes the most to the rise in AMR. We use aggregate sales data on antibiotics from the UK to estimate structural demand models and reveal drug substitution patterns. We then simulate alternative tax schemes to evaluate the effectiveness of shifting demand from broad- to narrow-spectrum drugs. Our estimates suggest that these policies can be highly effective in demand management and come at a relatively low cost regarding changes in consumer and producer surplus.

경제적 어려움 하에서 ESG와 기업가치의 관계

[Purpose] This study examines the effect of environmental, social, and governance (ESG) performance on corporate value in the face of economic difficulties. If a company’s survival is at stake, we predict that the capital market will focus on increasing the viability of a firm more important than the long-term benefits of ESG engagements.
 [Methodology] We use sales decline as a proxy for economic hardship. A firm’s sales decline is measured in a variety of ways：relative to the firm’s own sales in the preceding year or relative to the change in aggregate sales of the industry in the concurrent year. Additionally, we use operating profit decline as an alternative indicator of economic hardship. We collect ESG performance (ratings) data from Korea Corporate Governance System (KCGS) from 2011 to 2020.
 [Findings] We find that the positive value effect of ESG performance is weaker for firms that experience such shocks. Furthermore, during the COVID-19 pandemic, the effect of enhancing the value of ESG performance is not observed. Our results are robust to controlling for potential endogeneity of ESG performance using the two-stage least squares (2SLS) approach.
 [Implications] Our findings suggest that the beneficial effects of ESG performance on long -term corporate value is attenuated when firms face economic difficulties and severe economic crisis.

Evaluating store location and department composition based on spatial heterogeneity in sales potential

In this paper, we extend a retail location evaluation model with the possibility to include the effect of department size adaptation at the store level. We relate department-level store sales to a store's competitive and demographic environment, thereby providing richer insights into the drivers of department sales than a model of just aggregate sales. Further, we accommodate heterogeneity in consumer characteristics over space by using zip code level data and unobserved spatial effects in department sales by including spatially autocorrelated error terms.Using spatial panel data for 30 clothing stores belonging to one Dutch retail chain, we demonstrate how to use the modeling approach to analyze and predict sales performance of new and existing stores. We show that the predictive performance of our model is superior to that of a benchmark model that does not include spatial autocorrelation.

A Random Coefficients Model of Seafood Demand: Implications for Consumer Preferences and Substitution Patterns

Discrete choice models of demand are growing in popularity for understanding markets for seafood, but have thus far been limited to applications using individual-level choice data. The random coefficients logit model is a discrete choice demand model designed for aggregate sales data and imparts a number of theoretical and empirical advantages. Instrumental variables account for price endogeneity, which can arise when there are unobserved product characteristics. Furthermore, correlated preferences can be accommodated in the random coefficients as well as through demographic interactions, which is especially important for seafood where product characteristics are primarily qualitative. We estimate this model for salmon fillets using four years of county-level seafood sales in California, and demonstrate the insights that can be drawn regarding consumer preferences and substitution patterns. Although the model is computationally burdensome, it offers considerable potential for further seafood demand analysis.

Disaggregating sales prediction: A gravitational approach

Whenever companies plan to enter new geographical areas, they need disaggregated sales in each location. To make such predictions, sales time series or final customers’ data in geographical disaggregation are necessary. However, for most companies, such datasets are unavailable or impractical. The manuscript has two main goals. One focal problem is how to disaggregate an aggregate sales prediction with no historical proportions. The other is how to improve spatial models using Point of Interest (POI) data. To solve these problems, we combine two literature streams — spatial marketing and sales forecasting — and propose a new hybrid probabilistic approach: Gravitational Sales Prediction (GSP). Our approach uses POI data to estimate area attraction, customer stocks, and flows to predict sales proportions. We later use these proportions to disaggregate an aggregate forecast. GSP is validated using sales data from two countries and more than ten economic segments. When compared to a strong benchmark that relies on past sales proportions, GSP exceeded expectations by achieving not only a similar performance to the benchmark but also outperforming it in some locations. It showed the most promising results in the middle level of aggregation. The result is a powerful and flexible approach that can be embedded in any decision support system.

Effect of seasonal variations on quarry operations in Southwest Nigeria

This study assessed the seasonal effects on quarry operations and productivity in South-Western, Nigeria. Data were collected through oral interview, supplemented with well structured pre-tested questionnaires. The findings show that seasonal changes had effect on all quarry operations. The results revealed that aggregate production was higher during the dry season across all the quarries with Samchaze quarry having the highest production of 54,460-64,000 tons attributing to reduced downtime from rainfall and higher aggregate demand due to increased engineering projects rate in the dry season. With regards to sales, the findings showed a decrease in aggregate sales in the wet season in all the quarries with Western quarry making the lowest sales of 2,700-6,000 tons while Samchaze quarry made the highest sales of 11,000-15,000 tons. Health challenges like respiratory infections and heat stress were greater in the dry season while common cold, malaria and typhoid, cough and catarrh were common during the wet season. Eye infections, chest and hearing problems were felt in all the seasons. Appropriate recommendations were made to mitigate these seasonal changes impacts.

Assortment optimization using an attraction model in an omnichannel environment

Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for the complex shopping behavior of customers in an omnichannel environment. In this paper, we introduce a discrete choice model called the multichannel attraction model (MAM). A key feature of the MAM is that it specifically accounts for both the product substitution behavior of customers within each channel and the switching behavior between channels. We formulate the corresponding assortment optimization problem as a mixed integer linear program and provide a computationally efficient heuristic method that can be readily used for obtaining high-quality solutions in large-scale omnichannel environments. We also present three different methods to estimate the MAM parameters based on aggregate sales transaction data. Finally, we describe general effects of the implementation of widely-used omnichannel initiatives on the MAM parameters, and carry out numerical experiments to explore the structure of optimal assortments, thereby gaining new insights into omnichannel assortment optimization. Our work provides the analytical framework for future studies to assess the impact of different omnichannel initiatives.

Idiosyncratic shocks and aggregate fluctuations in an emerging market

This paper provides the first assessment of the contribution of idiosyncratic shocks to aggregate fluctuations in an emerging market using confidential data on the universe of Chilean firms. We find that idiosyncratic shocks account for more than 40 percent of the volatility of aggregate sales. Although quite large, this contribution is smaller than documented in previous studies based on advanced economies, despite a higher degree of market concentration in Chile. We show that this finding is explained by larger firms being less volatile and by weaker propagation effects across Chilean firms.

Forecasting the Diffusion of Smart Speakers in the Indian Market Using Bass, Gompertz, and Logistic Models

Forecasting the diffusion of new products in an emerging market is important yet challenging due to a lack of historical data. Managers often resort to inefficient forecasting practices to understand diffusion and to stay ahead of the competition. Our study aims to forecast sales for smart speakers in India, that have been introduced recently. Due to the lack of adequate sales data, our research has used data of analogous products using look-alike analysis to estimate the parameters of diffusion models. We forecasted future sales using three relevant diffusion models – the Bass, the Gompertz, and the Logistic to determine the model to forecast sales for smart speakers. The analysis revealed that the Bass model gave better predictions as compared to the other two models. The results were validated using parameter estimates from secondary literature. Our study predicts that the aggregate sales of smart speakers in India will peak around 2023-27.

Economic corollaries of personalized recommendations

The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.

Trends in public interest in shopping and point-of-sales of JUUL and Puff Bar 2019–2021

IntroductionWe investigated public interest in shopping and point-of-sales (POS) of JUUL and Puff Bar products in the context of five regulatory, company sales policy and other events of interest that...

Forecasting Aggregate Retail Sales with Google Trends

As the internet grows in popularity, many purchases are being made in online stores. Google Trends is an online tool that collects data on user queries and forms categories from them. We forecast the dynamics of both aggregate retail sales and individual categories of food and non-food products using macroeconomic variables and Google Trends categories that correspond to various product groups. For each type of retail, we consider the best forecasting models from macroeconomic variables and try to improve them by adding trends. For these purposes, we use pseudo-out-of-sample nowcasting as well as recursive forecasting several months ahead. We conclude that forecasts for food and non-food products can improve significantly once trends are added to the models.

COMMODITY ASPECTS OF THE INSURANCE STOCK OF GOODS IN THE STORE: FEATURES OF CALCULATION AND FORMATION

The main prerequisite for the stability and continuity of trade is the formation of optimal inventories of trade entities and their effective management. It depends on how much the range of goods of the store will meet the demands of consumers, and hence the social and economic efficiency of the store. However, excessive, irrational accumulation of inventories by the business entity leads to “freezing” of working capital and reduce their turnover, which in turn reduces the efficiency of their use and profitability of the store as a whole. In addition, inventories and their volume in the trading company are influenced by a number of factors, including the characteristics of the commodity characteristics of the goods themselves. Thus, given the peculiarities of economic activity of trade entities at the present stage of national and world economy, including those caused by the global pandemic Kovid-19 and the post-quarantine period, it is extremely important to ensure the smooth functioning of trade entities through the formation and effective management insurance stock. Thus, the competent formation and management of insurance stocks of goods in the store, including taking into account the commodity characteristics of the goods themselves, provides retailers with stable and uninterrupted trade. and unavoidable costs that do not always pay off. Therefore, we offer the following steps to save on the insurance reserve: increase or decrease the planning horizon or supply frequency, increase supplier discipline, aggregate data on several product items, increase the number of available statistics over time through sales statistics, aggregate sales history by several warehouses. These methods will help reduce the amount of excess inventory in the warehouse, without compromising the quality of service, sales and peace of mind of the entrepreneur.

Product variety in local grocery stores: Differential effects on stock‐keeping unit level sales

AbstractWe empirically examine the effect of product variety on sales at the stock‐keeping unit (SKU) level. Prior empirical operations management studies generally treat product variety as a single‐dimensional measure (e.g., the number of SKUs carried by the store, distribution center, or firm) and study its impact on aggregate sales (e.g., sales at the store, distribution center, or firm). As such, SKUs from different brands are assumed to contribute equally to total sales, which is not always the case. To address this, we need to conceptualize product variety within brands and investigate its impact on sales at a more granular level. In this study, we propose and theoretically articulate two product variety dimensions (within‐brand variety [WV] and average WV of other brands [AWVO]) to investigate the mechanisms that connect product variety to SKU‐level sales. Using a detailed transaction dataset from a local grocery retailer, we first show that product variety conceptualized within brands (i.e., the mean of WVs [MWV]) does have an impact on aggregate subcategory sales, as the literature suggests. At the SKU level, we find WV increases same‐brand SKU sales, while AWVO reduces it. Our work provides an alternative explanation to the inverted U‐shaped relationship between product variety and aggregate sales observed in the literature: Increasing variety only increases sales of SKUs belonging to the brand whose variety has increased, while it decreases sales of the other SKUs. We also find that variety effects vary significantly across different profitability levels and product categories. Product variety appears to have the strongest positive effect on high‐margin products. The impact of product variety on specific product category sales varies from as high as 11% to negligible. We further discuss the practical value of our findings and show the robustness of our results through multiple tests while addressing endogeneity using the control function approach.

The international propagation of economic downturns through multinational companies: The real economy channel

We study how non-financial multinational companies propagate economic declines from their subsidiaries located in countries experiencing an economic downturn to subsidiaries in countries not experiencing one. We find that investment is 18% lower in subsidiaries of these parents relative to the same-industry, same-country subsidiaries of parents that are headquartered in the same parent country but do not have a subsidiary in a country experiencing an economic downturn. The employment growth rate in the affected subsidiaries is zero or negative while it is 1.4% in the subsidiaries of unaffected parents. The aggregate industry-level sales and employment are also negatively impacted in the countries of the affected subsidiaries.

Customer segment transition through the customer loyalty program

PurposeThis study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.Design/methodology/approachThe authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.FindingsThe sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.Research limitations/implicationsDigital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.Practical implicationsMulti-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.Originality/valueThis study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

When Brands Go Dark

Because of various financial reasons, or a change in strategic focus, sometimes brands stop broad-reach media advertising for a year or longer. These long dark periods have not been subject to much study, so little is known about the likely consequences. This exploratory study addresses this omission by documenting the sales performance of 41 beer, cider, and spirit brands that advertised intermittently over almost two decades. Changes in aggregate brand sales are reported for the years when brands stopped advertising relative to the last advertised year. On average, brand sales declined immediately in the first year and every subsequent year of advertising cessation. Decline generally was faster for smaller brands and for brands that already were declining in sales before advertising cessation.

Appraising the Economic Growth Prospects of China’s Services Sector

This article examines the economic growth prospects of China’s services sector. Reportage on China’s economy and its data releases tends to focus largely on aggregate GDP growth, manufacturing growth, retail sales and investments, and fails to adequately cover services despite them being the largest contributor to growth. The article parses the growth of China’s various services subcomponents across a range of parameters and assesses that the industry has come under duress with a slowdown in complementary manufacturing and the dissipation of growth bubbles in finance and real estate. It is further observed that reforms necessary for spurring services growth are at a nascent stage, proceed slowly, and are imperilled by GDP growth targets that result in policies favouring the manufacturing industry. In sum, while China’s services growth prospects are hardly in dire jeopardy due to favourable demand-side conditions, achieving potential and affecting convergence with high income countries will necessitate the expedition of reforms and the engineering of a sustained high growth phase in the sector.

Decoupling the individual effects of multiple marketing channels with state space models

An important problem in marketing is understanding the impact of various marketing efforts on sales and revenue. If specific measures have not been taken before hand to distinguish responders across the various marketing initiatives, it becomes increasingly difficult to assess the effectiveness of investment across individual marketing channels. We present State Space model to estimate the individual channel effects using aggregate sales or response data across all channels. The proposed framework allows for varying carry over effects across marketing channels. Also, the proposed framework allows for differing rates of decay across marketing channels. We demonstrate its use when data on sales due to individual marketing channels is not available and only aggregated sales data are available. The proposed State Space modeling approach offers the advantage of: (1) allowing for varying rates of decay across marketing channels, and (2) allowing for a natural way of modeling time series dynamics. The approach also opens the way for more comprehensive marketing-mix optimization by allowing varying rates of decay.

Financial crises, firm‐level shocks and large downturns: Evidence from Greece

AbstractHow do firm‐specific shocks contribute to large economic downturns associated with financial crises? Using a large and representative dataset on Greek firms covering all sectors of the economy over the period 2000–2014, we find that the contribution of firm‐specific shocks to the volatility of aggregate sales growth increased substantially (about 30%) during the Greek financial crisis and dominated the contribution of macro‐economic and sectoral shocks. We also find that, throughout the sample period, inter‐firm linkages are two and a half times as important as the direct effect of firm shocks in driving aggregate fluctuations. However, during the financial crisis, the Greek economy became more granular, and the direct effect of firm‐specific shocks had increased importance in driving aggregate volatility.