Peak Sales Research Articles

Data to Decisions: Optimizing E-commerce Sales Potential with Analytics

Descriptive, diagnostic, predictive, and prescriptive analytics play crucial roles in optimizing the performance and user experience of an e-commerce site. Descriptive analytics involves examining historical data to gain insights into past performance, enabling businesses to identify trends, patterns, and anomalies. This analysis helps in understanding what has happened, such as identifying popular products or peak sales periods, and provides a foundation for further analysis. Diagnostic analytics goes beyond descriptive analytics by examining why certain events occurred, identifying factors that influenced outcomes, and uncovering strengths, weaknesses, and areas for improvement within an e-commerce platform. Predictive analytics utilizes statistical algorithms and machine learning techniques to forecast future trends and outcomes, enabling businesses to anticipate customer preferences, demand for specific products, and potential sales opportunities. By leveraging predictive insights, e-commerce sites can adjust strategies, inventory levels, and marketing campaigns proactively to stay ahead of the competition and meet evolving customer needs. Prescriptive analytics takes predictive insights to the next level by recommending specific actions or strategies to optimize business processes and achieve desired outcomes. This could involve personalized product recommendations, targeted marketing strategies, or dynamic pricing adjustments based on real-time data analysis. By harnessing these four types of analytics, e-commerce businesses can make informed, data-driven decisions, enhance customer experiences, drive sales growth, and maximize profitability.

Assessment of Marketing Practices and Strategies of Smoked Fish within Selected Markets in Benin City, Edo State, Nigeria

This study investigates the marketing practices of smoked fish within selected markets in Benin City, Edo State, Nigeria. The research findings highlight a predominant presence of married female marketers in the 25-30 age bracket, primarily equipped with secondary school education. Catfish (Clarias gariepinus) emerge as the favoured fish species, and the primary mode of transportation to these markets is public. Notably, refrigeration stands as the preferred storage method for unprocessed fish, with peak sales occurring in the afternoons during the dry season. Among the key discoveries, inadequate financial resources surface as the predominant external challenge faced by marketers, impacting the efficiency of their operations. To address these findings, we recommend the formation of cooperatives to facilitate access to financial resources through banks and government agencies. Additionally, investments in essential infrastructure such as smoking facilities (smoking kilns) are essential to mitigate adverse health effects on marketers. Furthermore, government intervention through educational seminars and awareness programs is suggested to empower smoked fish marketers to enhance their marketing strategies. Lastly, the implementation of price control measures is advised to mitigate price fluctuations faced by producers, retailers, and consumers, fostering a more stable and equitable market environment.

Spatiotemporal Analysis of Online Purchase of HIV Self-testing Kits in China, 2015-2017: Longitudinal Observational Study.

BackgroundSince the introduction of HIV self-testing by UNAIDS in 2014, the practice has been extensively implemented around the world. HIV self-testing (HIVST) was developed in China around 2015, and the online purchase of HIVST kits through e-commerce platforms has since become the most important delivery method for self-testing, with advantages such as user-friendliness, speed, and better privacy protection.ObjectiveUnderstanding the spatiotemporal characteristics of online HIVST kit purchasing behavior and identifying potential impacting factors will help promote the HIV self-testing strategy.MethodsThe online retail data of HIVST kits from the 2 largest e-commerce platforms in China from 2015 to 2017 were collected for this study. The Bayesian spatiotemporal hierarchical model was used to investigate the spatiotemporal characteristics of online purchased HIVST kits. Ordinary least squares regression was used to identify potential factors associated with online purchase, including GDP per capita, population density, road density, HIV screening laboratory density, and newly diagnosed HIV/AIDS cases per 100,000 persons. The q statistics calculated by Geodetector were used to determine the interactive effect of every 2 factors on the online purchase.ResultsThe online purchase of HIVST kits increased rapidly in China from 2015 to 2017, with annual peak sales in May and December. Five economically superior regions in China, Pearl River Delta, Yangtze River Delta, Chengdu and surrounding areas, Beijing and Tianjin areas, and Shandong Peninsula, showed a comparatively higher spatial preference for online purchased HIVST kits. The GDP per capita (P<.001) and the rate of newly diagnosed HIV/AIDS cases per 100,000 persons (P<.001) were identified as 2 factors positively associated with online purchase. Among the factors we investigated in this study, 2 factors associated with online purchase, GDP per capita and the rate of newly diagnosed HIV/AIDS cases per 100,000 persons, also displayed the strongest interactive effect, with a q value of 0.66.ConclusionsIndividuals in better-off areas are more inclined to purchase HIVST kits online. In addition to economic status, the severity of the HIV epidemic is also a factor influencing the online purchase of HIVST kits.

Manipulation of Theatrical Audience Size

Thomas Nashe’s’ satirical ‘ten thousand’ attendees at a London performance exaggeration is similarly absurd to most previous studies of audience size during the British Renaissance. These claims are countered in this article with a realistic calculation of the maximum quantity of people the described dimensions of the licensed London theatres could have accommodated. Claims that a troupe could have seen peak sales when it was forced to close during a plague are also reconsidered. And the failure of the English dramatic genre to reach its neighbouring Welsh market is questioned as indicative of the rarity of this mode of entertainment in comparison with the popularity claimed for it in puffing self-reviews of plays in the first post-origin decades. The ease with which a false belief in popularity could be generated is consistent with the Ghostwriting Workshop’s self-promotion of their published books. This article pulls together pieces of evidence to explain the literary, fiscal and political misdeeds committed by this Workshop in their quest for profit and fame.

Factors Affecting the Product Life Cycle of Generic Medicines.

BackgroundProduct life cycle (PLC) refers to the time ranging from when a product is introduced into the market to when it is taken off the shelves. The PLC management can guarantee product survival and prevent its decline.ObjectivesThis study investigated generic antibiotic PLCs and detected factors affecting them in the competitive pharmaceutical market of Iran to improve the PLC management of such drugs.MethodsTo study the PLC of antibiotics, data were collected from 2002 to 2017, and then the PLC curves were analyzed. Accordingly, factors affecting the PLC of antibiotics were illustrated in two sections: all PLC curves and the PLC curves with one sales peak. Using a generalized linear model combined with a machine learning approach, we identified the sales patterns and the effect of the product-related and the competition-related factors on the PLC curves, peak height, and the time to reach peak sales.ResultsAccording to the findings, 16, 11.87, 13.03, and 59% of the antibiotics had linear, binomial, one-peak, and oscillating sales patterns, respectively. The most crucial factors affecting the PLC shape were the quality, microbial spectrum, dosage forms, number of competitors, and entry arrangement.ConclusionsThis study examined factors affecting the PLC patterns of generic pharmaceutical products. The findings would provide more insights into the generic pharmaceutical market as one of the less-studied markets in many countries.

Analyzing Upward Deviation of Actual vs Predicted Drug Sales in Japan for a Reasonable Drug-Pricing Policy.

Predictions of drug sales play an important role in setting drug prices in Japan, where drug prices are highly regulated. One of 2 primary Japanese drug-pricing methodologies-the cost calculation method- refers extensively to the prediction of drug sales in establishing drug prices. Deviations from predicted sales can induce disproportionate drug prices and unexpected pharmaceutical expenditures. This study aimed to estimate the upward deviation of actual sales from predicted sales and to explore the predictors of such upward deviation. Estimates of upward deviation in 2015 were produced for new molecular entities (NMEs) approved in 2006-2015. To identify the predictors associated with upward deviation through binary logistic regression analyses, sales within 3 years of launch and in the predicted year in which peak sales would be reached were used. Estimated upward deviation was more than 1220 billion yen in 2015 for the targeted drugs. Drugs priced by the cost calculation method or by obtaining additional indications were significantly more likely to show an upward deviation from predicted peak sales. There is substantial upward deviation between actual and predicted drug sales in Japan. So long as drug sales predictions are used in drug price calculations, a flexible repricing system is needed to buffer unexpected pharmaceutical expenditures.

Bayer, BASF eye growth in agriculture

Bayer says its crop protection division has products with the potential to generate peak sales of $33 billion over the next decade, more than half of which will come from either recently or soon to be launched products. Bayer says the division, Crop Science, will spend an average $2.7 billion annually on R&D over the next 10 years, up from $2.5 billion in 2018. One of Crop Science’s goals is to reduce the environmental impact of its crop protection products—including in-field greenhouse gas emissions—by 30% by 2030. Some of that improvement will come from data science and innovative digital tools, according to Bayer. “Agriculture needs to feed a growing world without starving the planet,” Crop Science president Liam Condon said at a Bayer event to discuss the future of farming. Meanwhile, BASF’s $7 billion acquisition of various Bayer agrochemical and seed businesses in 2018—driven by Bayer’s need to meet regulatory

An impact analysis of the implementation of health technology assessment for new treatment of orphan diseases in Japan

ABSTRACT Objectives: In Japan, a cost-effectiveness evaluation (CEE) for pricing was introduced in April 2019 and potentially covers orphan drugs (ODs) within its scope. The purpose of this study was to explore a reasonable approach to evaluate the utility of health technology assessment (HTA) for ODs in Japan. Methods: We extracted ODs that were approved in Japan from 2009 to 2018, and investigated their appraisals in the United Kingdom, Canada, and Australia, where HTA and different frameworks on ODs have been implemented. Results: Overall, 76 ODs were identified, with high recommendation rates in the three countries (80.6%-90.9%). The major reason for negative recommendation was uncertainty regarding clinical effectiveness, with actual decisions varying across countries. This indicates difficulties in setting an objective evaluation for the uncertainty of clinical effectiveness. The results of the CEE were mainly used to adjust prices. Conclusion: As Japan’s CEE is expected to be used only for price adjustment after reimbursement is secured, the approach seems to be similar to the other countries. However, pre-launch clinical data are limited and the peak sales of ODs vary in Japan. Therefore, the careful introduction of CEE and multifaceted measures referring to the policies for ODs in other countries should be considered.

Forecasting electronic waste flows for effective circular economy planning

Rapid evolution in the consumer electronics sector has created new resource and waste challenges that are inadequately managed in the current linear product system. Circular economy (CE) strategies offer potential to close the loop on electronic products and materials, but often lack the future-oriented perspective needed to keep pace with this dynamic sector. The present study addresses this challenge by developing a logistic forecasting material flow model that can predict future resource and waste flows for products with abundant historic sales data (mature products) as well as for products that have just entered the market (emerging products). One of the key trends observed across current and legacy electronics is the steadily shrinking innovation cycle, where the time between a product’s market entry and peak sales is decreasing over time. This trend, coupled with extensive historic and modern product sales data, was used to create adoption scenario forecasts for emerging products, like fitness trackers, smart thermostats, and drones. Findings show that these devices are likely to have rapid uptake in the market, but may be quickly replaced by subsequent product innovations. In contrast, waste flow forecasts for mature products like CRTs, desktops, monitors and flat panel TVs showed their declining contribution to the U.S. e-waste stream. This study contributes a modeling framework that can be used to inform CE strategies in electronics by identifying near term opportunities and risks in end-of-life management of products to extend product life and close the loop on key materials.

Analyzing Upward Deviation of Actual vs Predicted Drug Sales in Japan for a Reasonable Drug-Pricing Policy.

Predictions of drug sales play an important role in setting drug prices in Japan, where drug prices are highly regulated. One of 2 primary Japanese drug-pricing methodologies-the cost calculation method- refers extensively to the prediction of drug sales in establishing drug prices. Deviations from predicted sales can induce disproportionate drug prices and unexpected pharmaceutical expenditures. This study aimed to estimate the upward deviation of actual sales from predicted sales and to explore the predictors of such upward deviation. Estimates of upward deviation in 2015 were produced for new molecular entities (NMEs) approved in 2006-2015. To identify the predictors associated with upward deviation through binary logistic regression analyses, sales within 3 years of launch and in the predicted year in which peak sales would be reached were used. Estimated upward deviation was more than 1220 billion yen in 2015 for the targeted drugs. Drugs priced by the cost calculation method or by obtaining additional indications were significantly more likely to show an upward deviation from predicted peak sales. There is substantial upward deviation between actual and predicted drug sales in Japan. So long as drug sales predictions are used in drug price calculations, a flexible repricing system is needed to buffer unexpected pharmaceutical expenditures.

Finding the hottest item in data streams

We study a problem of finding the hottest item interval in a data stream, where the hotness of an item over an interval is determined by its average frequency. Finding the hottest item interval is particularly helpful in business promotions, such as monitoring the peak sales records, finding the hottest period in an online game, digging the highest click rate of an online music, etc. Existing work focus on finding the most frequent item over a fixed length interval. However, these solutions cannot return the hottest interval since the best length (i.e., maximizing the average frequency) is unknown in advance. To discover the hottest item interval, a straightforward solution is to calculate the average frequencies of items for every possible interval length, which is too costly for stream applications. To efficiently compute the hottest item interval, we propose an algorithm that employs the arrival timestamps of items and reduce the search space by three pruning strategies. Extensive experiments show that the proposed algorithms can efficiently discover the hottest item interval on both real and synthetic datasets.

Exploring sales data during a healthy corner store intervention in Toronto: the Food Retail Environments Shaping Health (FRESH) project.

Population health interventions in the retail food environment, such as corner store interventions, aim to influence the kind of cues consumers receive so that they are more often directed toward healthier options. Research that addresses financial aspects of retail interventions, particularly using outcome measures such as store sales that are central to retail decision making, is limited. This study explored store sales over time and across product categories during a healthy corner store intervention in a lowincome neighbourhood in Toronto, Ontario. Sales data (from August 2014 to April 2015) were aggregated by product category and by day. We used Microsoft Excel pivot tables to summarize and visually present sales data. We conducted t-tests to examine differences in product category sales by "peak" versus "nonpeak" sales days. Overall store sales peaked on the days at the end of each month, aligned with the issuing of social assistance payments. Revenue spikes on peak sales days were driven predominantly by transit pass sales. On peak sales days, mean sales of nonnutritious snacks and cigarettes were marginally higher than on other days of the month. Finally, creative strategies to increase sales of fresh vegetables and fruits seemed to substantially increase revenue from these product categories. Store sales data is an important store-level metric of food environment intervention success. Furthermore, data-driven decision making by retailers can be important for tailoring interventions. Future interventions and research should consider partnerships and additional success metrics for retail food environment interventions in diverse Canadian contexts.

Impact of free sampling on product diffusion based on Bass model

Free sampling is an important marketing tool to promote product information diffusion and enhance sales. Based on customer preference and enterprise pricing strategy, the work used Bass model to analyze the effect of free sampling on product diffusion. There were three questions to be explored, including the quantity, opportunity and effect of sampling. Research shows that enterprises should select different sampling levels according to different pricing strategies and product types. Skimming pricing has the highest optimal sampling level; fixed pricing takes second place; penetration pricing is the lowest. Digital product has higher optimal sampling level than physical product. It is never too early for the enterprise to select free sampling. Moderate sampling can increase the expected return on the business. Product diffusion is promoted to achieve peak sales more quickly. Digital product has better sampling effect than physical product.

Applying Looks-like Analysis and Bass Diffusion Model Techniques to Forecast a Neurostimulator Device with No Historical Data

This work utilizes looks-like analysis and the Bass diffusion model to generate sales forecasts of a responsive neurostimulation (RNS) system. Due to the lack of historical data, a combination of techniques is utilized to predict the device's demand. Looks-like analysis is used to analyse analogous devices and their sales patterns to select the device closest to the RNS system. Next, parameters for the Bass diffusion model are estimated, and potential baseline forecasts are developed using two methods. Results suggest that peak sales for the device will occur around years 2021-2024.

Price and Sales Volume Patterns of Mobile Handsets and Technologies

This article provides empirical evidence on the price and unit sales volume patterns of mobile handsets and mobile technologies, using data on the Finnish market. The prices and sales are studied on product category, product model, and product feature levels. The results show how the dynamic of prices and sales changed after the proliferation of smartphones. Otherwise, the dynamics seem to be relatively systematic supporting the use of simple assumptions in practical estimations. The median price of handset models decreases linearly, from 89% of the introduction price at peak sales in the fifth sales month to 47% in in two years. For mobile handset features, a decreasing price pattern was also identified. After a 10% market share is reached, the decrease is on average 30 Euros per 10% market share change. The prices at feature takeoff were identified to be on average at 58% of introduction price.

Quantifying the Value of Flexibility in Supply Chains for High-Risk Products

A model is developed to motivate financially-driven decision makers in companies that make high-risk products to invest in infrastructural changes to support postponement or other forms of flexibility. Data collected at the manufacturer of a high-risk product forms the basis of the effort. The model is used to estimate the magnitude of costs associated with a traditional speculative supply strategy. A customized version of the classic newsvendor model is developed and applied, based on non-normal demand distributions and carrying costs. It is shown that a speculative strategy based on capacity smoothing and production cost minimization can result in excessive overstock, understock, and inventory carrying costs. Specifically, a cost increase of 52% can be expected when production is initiated nine months prior to peak sales. The cost increase is reduced to 24% if the product is produced three months prior to peak sales. The model can help supply chain managers convince corporate executives of the need to redesign the supply chain for speed and flexibility.

Death of a salesman: birth of a knowledge broker

Call centres have emerged as an important channel for the sale of infrequently purchased goods and services. The purpose of this paper is to examine the predictors of sales agent performance and their changed role in inbound call centres. Two independent samples were taken from a single inbound call centre, the first taken during a peak sales week, and the second from an off–peak sales week. PLS was used to model the relationship between agent tenure, selling time, administrative time and sales performance metrics. Agent tenure was found to have a limited effect on sales performance. Increased selling time and reduced administrative time increased both sales performance metrics. Both of these findings are contrary to traditional sales and call centre management theory. From these results it is apparent that adaptive selling techniques are required to deal with the reduced information asymmetry in the information age.

Novel methodology for pharmaceutical expenditure forecast

Background and objectiveThe value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the ‘EU Pharmaceutical expenditure forecast’; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm).Methods1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis.ResultsThis methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time to reach peak sales for new drugs; 2) estimating the statistical distribution of the budget impact; and 3) testing different pricing and reimbursement policy decisions on health expenditures.ConclusionsThis methodology was independent of historical data and appeared to be highly flexible and adapted to test robustness and provide probabilistic analysis to support policy decision making.

PHP31 - Does Europe Reward Reformulations? A Data Driven Analysis of Value Preservation Through Reformulation

Reformulating existing drugs can improve patient convenience, compliance and create new uses for the product. However, reformulations cost millions of Euros - a risky investment without guaranteed exclusivity or returns. This study assessed whether markets reward incremental value creation through reformulation by comparing sales performance close to patent expiry for reformulated and non-reformulated products. IMS MIDAS data was interrogated to identify 829 small molecule, non-generic products which had peak sales above €5M and lost patent protection between 2001 and 2010 in EU-5 (France, Germany, Italy, Spain and UK). Ex-manufacturer sales value (€) 2 years after patent expiry was compared with sales value 4 years earlier to calculate percentage value erosion for each of the 829 products. A subset of 133 products which launched at least 1 reformulation close to patent protection expiry (launch between 3 years before and 1 year after patent protection expiry) were analysed to assess whether value erosion varied across countries or therapy areas. Mean ex-manufacturer sales value erosion of reformulated products (24%) was significantly less than non-reformulated products (37%, P<0.01). Reformulated product value erosion varied with country and therapy area. Germany saw highest number of reformulations launched yet only reformulations launched in Italy showed significantly less value erosion than non-reformulated products (P<0.01). Across therapy areas, reformulations were most common in nervous system, metabolism and anti-infective categories. However, reformulated anti-infective product value erosion was pronounced while musculoskeletal and nervous system products experienced significantly less value erosion than for non-reformulated products (both P<0.05). Overall, reformulated products do maintain more value than non-reformulated products following loss of patent protection. Chronic disorders e.g. nervous and musculoskeletal system disorders appear to be the most promising areas for reformulating. Future reformulation potential is tightly linked to country-specific pricing and market access policy decisions to recognise incremental value products.

NPV modelling for the selection of value-creating biosimilar development candidates

The purpose of this study was to apply net present value (NPV) modelling to evaluate the financial attractiveness and business risk of different categories of biosimilars. Challenges and opportunities of biosimilars are compared with those of standard small molecule generics. Minimum peak sales levels are required to create financial value were determined in order to derive recommendations for the selection of commercially rewarding biosimilar development candidates.