A rare presentation of actinomycosis: case report

Background: Anaerobic, nonsporulating, Gram-positive bacteria groups called actinomyces organisms are responsible for the so called actinomycosis. This chronic disease is rare in children and has tendency to mimic many other diseases. It also has wide variety of manifestations and non-specific symptoms. As a result, it is difficult to diagnose before the biopsy and microscopic examination. Although infection may involve any organ in the body, the significant sites of actinomyces infection include cervicofacial, abdominal, pelvic and pulmonary tissues. Case report: Here, we describe one case of unusual presentation; an 11-year-old girl with a soft tissue mass in the left lower lateral chest wall which was finally diagnosed actinomycosis based on the pathological findings. Conclusions: Actinomycosis may rarely present with chest wall mass

Relationship between β-Thalassemia minor and Helicobacter pylori infection

Background: Until now, no study has been reported investigating the association between β-thalassemia minor and Helicobacter pylori (H. pylori) infection. This study was designed to compare H. pylori infection rate between β-thalassemia minor patients and healthy controls. Methods: A number of 100 β-thalassemia minor patients (50 males, 50 females) and 100 gender-matched healthy controls were prospectively recruited in this study in a period of 3 months. The study population consisted of the people who referred to a health center in Babol, North of Iran, for premarital counseling. H. pylori status was assessed by measuring the anti-H. pylori IgG antibodies using enzyme-linked immunosorbent assay. Demographic information and informed consent were collected from all participants. Results: The overall H. pylori infection rate was 43%. The infection was significantly more prevalent in thalassemia patients (53%) than in the controls (33%) in both univariate (OR=2.29, 95% CI: 1.3-4.06) and multivariable analyses (OR=2.05, 95% CI: 1.12-3.76). Age was the only significant factor which was positively correlated with the infection in β-thalassemia minor cases (OR=1.11, 95% CI: 1.02-1.2). Gender, blood groups, residency, and education level were not related to the infection. Conclusions: According to the results, it can be concluded that β-thalassemia minor patients are possibly more susceptible to H. pylori infection than healthy people. Further studies are needed to discover more about the exact mechanisms of increased susceptibility to H. pylori infection in β-thalassemia minor patients

Predicting customer churn in telecommunications service providers

Customer churn is the focal concern of most companies which are active in industries with low switching cost. Among all industries which suffer from this issue, telecommunications industry can be considered in the top of the list with approximate annual churn rate of 30%. Tackling this problem, there exist different approaches via developing predictive models for customers churn, but due to the nature of pre-paid mobile telephony market which is not contract-based, customer churn is not easily traceable and definable, thus constructing a predictive model would be of high complexity. Handling this issue, in this study, we developed a dual-step model building approach, which consists of clustering phase and classification phase. With this regard firstly, the customer base was divided into four clusters, based on their RFM related features, with the aim of extracting a logical definition of churn, and secondly, based on the churn definitions that were extracted in the first step, we conducted the second step which was the model building phase. In the model building phase firstly the Decision Tree (CART algorithm) was utilized in order to build the predictive model, afterwards with the aim of comparing the performance of different algorithms, Neural Networks algorithm and different algorithms of Decision Tree were utilized to construct the predictive models for churn in our developed clusters. Evaluating and comparing the performance of the employed algorithms based on “Gain measure”, we concluded that employing a multi-algorithm approach in which different algorithms are used for different clusters, can bring the maximum “Gain” among the tested algorithms. Furthermore, dealing with our imbalanced dataset, we tested the cost- sensitive learning method as a remedy for handling the class imbalance. Regarding the results, both simple and cost-sensitive predictive models have a considerable higher performance than random sampling in both CART model and multi-algorithm model. Additionally, according to our study, cost- sensitive learning was proved to outperform the simple model only in CART model but not in the multi-algorithm.Validerat; 20101217 (root

Modelling customer churn in non-contractual settings

In today’s increasingly saturated and highly competitive markets, customers have become more demanding and more likely to switch between companies. In response, astute companies acknowledge that their business strategies should focus on identifying those customers who are most likely to ‘churn’ (defect) and then use the outcome of this prediction to design the appropriate churn management campaigns. In this regard, the current study contributes to literature on churn modelling in non-contractual settings by investigating the performance of different primary churn modelling approaches to find an optimum approach based not only on its accuracy (from a lift measure perspective) but also on its ability to maximize the profitability of a churn management campaign. In addition, it proposes a data-mining approach to model non-contractual customer churn in B2B contexts. The constructed model is then used to demonstrate the profit that the company can make by implementing such predictive models in a B2B churn management campaign. Finally, the study improves the existing profitability framework of churn management campaigns by modelling the probability of accepting an incentive as dependent on its monetary value, using an exponential distribution functional link. The resulting model has the ability to maximize the profit of the campaign on an aggregate level as well as the individual level, by optimizing the incentive value offered to a given customer

Modelling customer churn in non-contractual settings

In today’s increasingly saturated and highly competitive markets, customers have become more demanding and more likely to switch between companies. In response, astute companies acknowledge that their business strategies should focus on identifying those customers who are most likely to ‘churn’ (defect) and then use the outcome of this prediction to design the appropriate churn management campaigns. In this regard, the current study contributes to literature on churn modelling in non-contractual settings by investigating the performance of different primary churn modelling approaches to find an optimum approach based not only on its accuracy (from a lift measure perspective) but also on its ability to maximize the profitability of a churn management campaign. In addition, it proposes a data-mining approach to model non-contractual customer churn in B2B contexts. The constructed model is then used to demonstrate the profit that the company can make by implementing such predictive models in a B2B churn management campaign. Finally, the study improves the existing profitability framework of churn management campaigns by modelling the probability of accepting an incentive as dependent on its monetary value, using an exponential distribution functional link. The resulting model has the ability to maximize the profit of the campaign on an aggregate level as well as the individual level, by optimizing the incentive value offered to a given customer

The impact of advertising on market share: Controlling for clutter, familiarity, and goodwill decay

The advertising-sales relationship remains paramount. This study--believed to be the largest that focuses on automotive sales and advertising--investigates ceilings and thresholds by optimizing budget allocations among media. The study considered 12 vehicle brands in South Africa, spanning 8 years of sales, reflecting sales and media expenditure. It also considers the effect of competitors' advertising expenditure and familiarity on the ceilings and thresholds of each medium, as well as the decay rate of advertising goodwill. Findings suggest that the allocation of expenditure among media types is subject to threshold phenomena. The findings also establish the impact of clutter and familiarity on market share, advertising efficacy, and ceilings and thresholds. Results highlight the interaction between current market share and advertising efficacy.http://www.journalofadvertisingresearch.com2021-09-29hj2020Marketing Managemen

Managing B2B customer churn, retention and profitability

It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics. © 2014 Elsevier Inc

Comparing churn prediction techniques and assessing their performance: a contingent perspective

Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques