retail prices

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses retail price optimization as a regression problem, utilizing machine learning models to predict optimal price points for products. Leveraging factors such as product attributes, competitor pricing dynamics, and customer behaviors, regression analysis provides a structured approach to understanding the intricate relationships between variables. Among various regression techniques, the Random Forest Regressor emerges as a potent strategy, offering robustness and versatility in tackling complex pricing tasks. Results indicate that Random Forest outperforms Decision Tree and Logistic Regression models regarding accuracy, precision, recall, and overall predictive performance. With Random Forest achieving an accuracy of 94%, it demonstrates superior capability in capturing complex data patterns and making accurate predictions of retail prices. By leveraging advanced analytics and machine learning techniques, retailers can optimize pricing strategies, maximize profits, and maintain competitiveness in the market. This study underscores the importance of continuously analyzing and refining pricing strategies to gain a competitive edge in the retail landscape.

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Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses retail price optimization as a regression problem, utilizing machine learning models to predict optimal price points for products. Leveraging factors such as product attributes, competitor pricing dynamics, and customer behaviors, regression analysis provides a structured approach to understanding the intricate relationships between variables. Among various regression techniques, the Random Forest Regressor emerges as a potent strategy, offering robustness and versatility in tackling complex pricing tasks. Results indicate that Random Forest outperforms Decision Tree and Logistic Regression models regarding accuracy, precision, recall, and overall predictive performance. With Random Forest achieving an accuracy of 94%, it demonstrates superior capability in capturing complex data patterns and making accurate predictions of retail prices. By leveraging advanced analytics and machine learning techniques, retailers can optimize pricing strategies, maximize profits, and maintain competitiveness in the market. This study underscores the importance of continuously analyzing and refining pricing strategies to gain a competitive edge in the retail landscape.

There is a growing academic interest in the development of machine learning (ML) models applied to price optimization. Historically, retail prices were set with exogenous reference to competitors, a base price was selected for the promoted product, and a simple markup on cost was applied to all items in a product category. The emergence of e-commerce provided retailers with both opportunity and challenge, as they faced competitive pressures to adjust prices across thousands of products on an hourly basis. Price optimization is now being pursued using methods ranging from simple heuristics to sophisticated, machine-learned price models based on historical sales data. ML models can mine pricing-relevant relationships and patterns from transactional data generated by the ongoing management of price changes. Demand models can then simulate the impact of price adjustments on sales volume, revenue, and profit (or margin). Price models can recommend a price to be applied to the future and/or a scheduled price adjustment to capitalize on the forecasted demand change. Pre-emptive pricing models can be used to profitably preempt a new competitor or the next move from an existing competitor. Demand is affected by many different variables across a variety of time scales. Regular marketing activities are widely adopted by retailers, including regular sales promotions on specific products and regular changes in price tags. In addition, various unexpected exogenous factors, such as extreme weather conditions, holidays, and sports events, may substantially affect demand. Price movements can also affect inventory changes in the subsequent time periods. Retailers could thus benefit from accurately forecasting the impact of these multiple factors/categories on sales demand. Data-driven analytical forecasting methods have gained popularity as they can automatically discover patterns from available data. More complex applications could improve forecasting accuracy by leveraging time-evolution sales data across different stores or product categories, although they remain complicated. Moreover, transparency is lacking in traditional complex ML models, which may lead to perceptions of a "black box" as regards sales forecasts, and hence retail supply-chain executors may lack comprehension of expected demand changes after implemented decisions.

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses retail price optimization as a regression problem, utilizing machine learning models to predict optimal price points for products. Leveraging factors such as product attributes, competitor pricing dynamics, and customer behaviors, regression analysis provides a structured approach to understanding the intricate relationships between variables. Among various regression techniques, the Random Forest Regressor emerges as a potent strategy, offering robustness and versatility in tackling complex pricing tasks. Results indicate that Random Forest outperforms Decision Tree and Logistic Regression models regarding accuracy, precision, recall, and overall predictive performance. With Random Forest achieving an accuracy of 94%, it demonstrates superior capability in capturing complex data patterns and making accurate predictions of retail prices. By leveraging advanced analytics and machine learning techniques, retailers can optimize pricing strategies, maximize profits, and maintain competitiveness in the market. This study underscores the importance of continuously analyzing and refining pricing strategies to gain a competitive edge in the retail landscape.

The agricultural commodity crisis of 2006-8 and the recent evolution of commodity markets have reignited anxieties in Finland over fast-rising food prices and food security. Although the impact of farm commodity price shocks on the final consumer is mitigated by a large degree of processing as well as the complex structure of the food chain, little is known about the strength of the linkages between food markets and input markets. Using monthly series of price indices from 1995 to 2010, we estimate a vector error-correction (VEC) model in a co-integration framework in order to investigate the short-term and long-term dynamics of food price formation. The results indicate that a statistically significant long-run equilibrium relationship exists between the prices of food and those of the main variable inputs consumed by the food chain, namely agricultural commodities, labour, and energy. When judged by the magnitude of long-run pass-through rates, farm prices represent the main determinant of food prices, followed by wages in food retail and the price of energy. However, highly volatile energy prices are also important in explaining food price variability. The parsimonious VEC model suggests that the dynamics of food price formation is dominated by a relatively quick process of adjustment to the long-run equilibrium, the half life of the transitional dynamics being six to eight months following a shock.

The agricultural commodity crisis of 2006-8 and the recent evolution of commodity markets have reignited anxieties in Finland over fast-rising food prices and food security. Although the impact of farm commodity price shocks on the final consumer is mitigated by a large degree of processing as well as the complex structure of the food chain, little is known about the strength of the linkages between food markets and input markets. Using monthly series of price indices from 1995 to 2010, we estimate a vector error-correction (VEC) model in a co-integration framework in order to investigate the short-term and long-term dynamics of food price formation. The results indicate that a statistically significant long-run equilibrium relationship exists between the prices of food and those of the main variable inputs consumed by the food chain, namely agricultural commodities, labour, and energy. When judged by the magnitude of long-run pass-through rates, farm prices represent the main determinant of food prices, followed by wages in food retail and the price of energy. However, highly volatile energy prices are also important in explaining food price variability. The parsimonious VEC model suggests that the dynamics of food price formation is dominated by a relatively quick process of adjustment to the long-run equilibrium, the half life of the transitional dynamics being six to eight months following a shock.

BackgroundIn the Mekong Delta region of Vietnam, high quantities of products containing antimicrobial are used as prophylactic and curative treatments in small-scale chicken flocks. A large number of these contain antimicrobial active ingredients (AAIs) considered of ‘critical importance’ for human medicine according to the World Health Organization (WHO). However, little is known about the retail prices of these products and variables associated with the expense on antimicrobials at farm level. Therefore, the aims of the study were: (1) to investigate the retail price of antimicrobials with regards to WHO importance criteria; and (2) to quantify the antimicrobial expense incurred in raising chicken flocks. We investigated 102 randomly-selected small-scale farms raising meat chickens (100–2000 per flock cycle) in two districts in Dong Thap (Mekong Delta) over 203 flock production cycles raised in these farms. Farmers were asked to record the retail prices and amounts of antimicrobia...

Background: In the Mekong Delta region of Vietnam, high quantities of products containing antimicrobial are used as prophylactic and curative treatments in small-scale chicken flocks. A large number of these contain antimicrobial active ingredients (AAIs) considered of 'critical importance' for human medicine according to the World Health Organization (WHO). However, little is known about the retail prices of these products and variables associated with the expense on antimicrobials at farm level. Therefore, the aims of the study were: (1) to investigate the retail price of antimicrobials with regards to WHO importance criteria; and (2) to quantify the antimicrobial expense incurred in raising chicken flocks. We investigated 102 randomly-selected small-scale farms raising meat chickens (100-2000 per flock cycle) in two districts in Dong Thap (Mekong Delta) over 203 flock production cycles raised in these farms. Farmers were asked to record the retail prices and amounts of antimicrobial used. Results: A total of 214 different antimicrobial-containing products were identified. These contained 37 different AAIs belonging to 13 classes. Over half (60.3%) products contained 1 highest priority, critically important AAI, and 38.8% 1 high priority, critically important AAI. The average (farm-adjusted) retail price of a daily dose administered to a 1 kg bird across products was 0.40 cents of 1 US$ (₵) (SE ± 0.05). The most expensive products were those that included at least one high priority, critically important AAI, as well as those purchased in one of the two study districts. Farmers spent on average of ₵3.91 (SE ± 0.01) on antimicrobials per bird over the production cycle. The expense on antimicrobials in weeks with disease and low mortality was greater than on weeks with disease and high mortality, suggesting that antimicrobial use had a beneficial impact on disease outcomes (χ 2 = 3.8; p = 0.052). Farmers generally used more expensive antimicrobials on older flocks. Conclusions and recommendation: The retail prices of antimicrobial products used in chicken production in Mekong Delta small-scale chicken farms are very low, and not related to their relevance for human medicine. Farmers, however, demonstrated a degree of sensitivity to prices of antimicrobial products. Therefore, revising pricing policies of antimicrobial products remains a potential option to curb the use of antimicrobials of critical importance in animal production.

The Jamaica Agriculture Market Information System reports wide variations in the price of similar food items across supermarkets in the capital city, Kingston. Possible explanations for this phenomenon are explored in this paper. Our findings indicate that supermarkets that practice price smoothing are more likely to persistently sell food items at the highest price. Likewise, supermarkets that are centrally located are prone to consistently have the highest price for food items. Whether or not farmers who supply food items to the supermarkets and consumers who buy from them are sensitive to price smoothing will provide validation for this practice by supermarkets. This constitutes an area of future study.