Driver Categories Research Articles

Investigating the factors driving bidirectional relationships between universities and public research institutes, and Brazilian small and medium-sized enterprises

Objective: investigating the factors driving bidirectional relationships (i.e., “scientific research with considerations towards the immediate use of results” and “scientific research without considerations towards the immediate use of results”) established between research groups (RGs) and Brazilian small and medium-sized enterprises (SMEs). Methodology/approach: estimating four logistic regression models to investigate the factors driving bidirectional relationships between RGs from the Brazilian Directory of Research Groups (DRGs) of the National Council for Scientific and Technological Development and 1,819 companies with up to 499 employees. Main results: the economic sector and the access to public funding can influence the establishment of bidirectional relationships between RGs and SMEs. Some specificities of factors driving bidirectional relationships are identified in SMEs’ different size ranges. Theoretical/methodological contributions: adopting a comprehensive database on collaborations between RGs and SMEs, based on information deriving from the Directory of Research Groups and from the Annual Report of Social Information, and approaching to a topic that remains poorly investigated in the literature. Relevance/originality: assessing how different driver categories influence the establishment of bidirectional relationships between RGs and SMEs and featuring the factors driving these relationships in interactive companies’ different size ranges. Social/Management Contributions: Innovation policy should address the factors driving knowledge-related cooperation between public research organizations and SMEs.

Adaptive forward collision warning system for hazmat truck drivers: Considering differential driving behavior and risk levels

The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver’s response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems.

Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following

Driving aggression is a major concern during car-following situations as it is closely associated with collision risk and crash severity. Despite the tendency of reckless driving actions and increased crash risk with the vehicle ahead, aggressive driver behavior remains unexplored. The present study aimed to investigate the effects of aggressive driver behavior (three driver categories: aggressive, moderately aggressive, and non-aggressive drivers) and drivers’ tendency to aggressive stimuli (due to lead vehicle (vehicle moving ahead) reckless driving) on car-following behavior. A sample of fifty-eight Indian drivers participated in this study. All the experiments were conducted using a driving simulator. The simulator design includes a lead vehicle dynamic maneuver with rapid accelerations and decelerations (at 1 m/s2, 1.5 m/s2, and 2 m/s2). Three longitudinal performance measures including speed variability (SPV) from LV, speed recovery time (SRCT), and time spent tailgating, were analyzed during LV acceleration stage using generalized linear models (GLM). Further, to assess collision avoidance behavior and crash risk, deceleration adjusting time (DAT) was analyzed during the LV sudden deceleration stage using Weibull Accelerated Failure Time (AFT) analysis. The findings indicate that due to their tendency to risk-raking behavior, aggressive drivers decreased their SPV and SRCT by 25 % and 18 %, respectively. The time spent tailgating increased by 107 % for aggressive drivers. The survival probabilities were decreased by 23.77 % for aggressive drivers at 3 sec DAT. The findings of the study can be used in car-following models to enhance the model performance, improve the reliability of simulation tools, and design interventions to improve safety (in automated driving).

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

Advanced driver assistance systems and conditional automation (SAE level L3) bring full of challenges, central to the human role in such automated driving and are the transitions from automated to manual driving mode: driver monitoring systems should be able to adapt to the detected human state adjusting time given for takeover request. In this study, the human driver’s control authority during the transition from the automated system is evaluated by using a large set of sensors, including the galvanic skin response sensor, the speed, and the current sensor of the electric vehicle. The Local Outlier Factor (LOF) method detects the driver’s readiness to take control during the control authority transition transient stage. Eighteen drivers (8 females and 10 males) between 24 and 65 years of age participate in the experimental evaluation and data collection campaign. Takeover request time analysis evaluates each driver and three driver categories, such as experienced, semiexperienced, and inexperienced, and the questionnaire validates the takeover request time analysis. Galvanic skin response measurements and the LOF approach illustrate successful detection of the driver’s status about his/her readiness to take over and regulate the driving task when the transition of driving authority from the automated system is requested in a real road driving scenario. The takeover request time is personalized for each driver, which may improve the current conditional automated driving technologies’ penetration and acceptance.

Interaction strategies with advanced driver assistance systems

When using advanced driver assistance systems (ADAS) drivers need to calibrate their level of trust and interaction strategy to changes in the driving context and possible consequent reduction of system reliability (e.g. in harsh weather conditions). By investigating and identifying categories of drivers who choose inadequate interaction strategies, it is possible to address unsafe usage with e.g. tutoring lessons tailored to the respective driver category. This paper presents two studies investigating categories of drivers who apply different interaction strategies when using ADAS. Study I was designed as an exploratory field study with 37 participants interacting with a SAE level 2 system. For the exploratory study, it was important to observe and understand the interaction strategies in a driving context which entails the real complexity of the driving task. The experimental set-up of study II (simulator study), however, allowed to clearly interpret the interaction strategies as either calibrated or un-calibrated by varying the situational risk. Participants (N = 33) were driving in a situation where the system was either working reliably (low-risk condition) or in a situation where the system displayed repeatedly errors under harsh weather conditions (high-risk condition). Cluster analyses with the variables trust, monitoring behavior towards the system and usage behavior were performed to analyze potential categories of drivers. Extreme driver categories with interaction strategies indicative for both misuse and disuse were observed in both studies. In study I, drivers were categorized as either highly trusting attentive, moderately trusting attentive, moderately inattentive, inattentive or skeptical. In study II, drivers were categorized as either un-calibrated, calibrated, inconsistent or skeptical. Taken together, results underline the need of tutoring systems that are tailored for different driver categories.

Understanding complex spatial dynamics from mechanistic models through spatio‐temporal point processes

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially‐explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements and spatio‐temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio‐temporal relationships or patterns. An alternative solution is given by meta‐models and meta‐analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta‐modelling approach based on spatio‐temporal point processes (STPPs), we characterise spatio‐temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially‐explicit population model simulate hierarchically the pest–predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio‐temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi‐type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio‐temporal dynamics of pest–predator systems. We are able to differentiate the contributions of different driver categories (i.e. spatio‐temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi‐natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

Module Drivers in Product Development: A Comprehensive Review and Synthesis

Modular product design is recognised as a successful approach to efficiently fulfil today's market requirements. Erixon [6] and several later publications have proposed drivers for modularising products, yet to the best of the authors’ knowledge, none have attempted to unify these contributions into a coherent and comprehensive overview. Based on a review of the literature, this study has identified, analysed, and synthesised module drivers into an extensive set of module drivers. Taking outset in the value chain concept, this study identified 68 individual module drivers grouped into 29 module driver categories spanning 6 value chain steps.

Hybrid Model Of Structural Equation Modeling Pls And Rfm (Recency, Frequency And Monetary) Model To Improve Bank Average Balance

As a business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. In this study, Structural Equation Model (SEM) identify the transaction that influence the average balance. The objects of the RFM segmentation on the selected transaction is to understand customer segment score and build a marketing strategy for each segment with different levels of loyalty for the Financial result of higher Average Balance. 
 The segmentation results found three driver categories, High Recency, Mid Recency and Low Recency category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary category. Mid Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered already Churn customer where campaign category is to conduct reactivation.

Exploring the drivers of malnutrition in West Africa from health and social science perspectives: A comparative methodological review

West Africa has a high burden of malnutrition and the drivers are often complex, highly context-specific, and cut across individual, social, political and environmental domains. Public health research most often considers immediate individual health and diet drivers, at the expense of wider considerations that may fall outside of a health agenda. The objective of this systematic mapping review is to map the broad drivers of malnutrition in West Africa, from public health and social science perspectives, and to evaluate the additional value of an interdisciplinary approach. Evidence was gathered from one public health (MEDLINE) and one social science (International Bibliography of Social Science) database using a detailed search syntax tailored to each disciplinary configuration. Literature was screened against pre-determined eligibility criteria and extracted from abstracts. Studies published in English or French between January 2010 and April 2018 were considered for inclusion. Driver categories (immediate, underlying and basic drivers) were coded against the UNICEF conceptual framework of malnutrition. A total of 358 studies were included; 237 were retrieved from the public health database and 124 from the social science database, 3 studies were included in both. The public health and social science literature document different drivers, with MEDLINE most often reporting immediate drivers of malnutrition and the International Bibliography of Social Science database reporting underlying and basic drivers. The combined literature offers more balanced representation across categories. An interdisciplinary approach proved successful in achieving complementarity in search results while upholding rigorous methods. We recommend that interdisciplinary approaches are utilised to bridge recognised gaps between defined disciplines.

Atrial fibrillation in the UK: predicting costs of an emerging epidemic recognizing and forecasting the cost drivers of atrial fibrillation-related costs.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and a major preventable cause of stroke, heart failure, and dementia. Atrial fibrillation already accounts for a significant amount of National Health Service (NHS) funding, and over the coming years is highly likely to impose a growing cost on NHS budgets and the wider UK healthcare system. We, therefore, need greater understanding of the main cost drivers (e.g. hospitalizations) of this increasingly prevalent arrhythmia. Such data would help with NHS resource planning over the next decades. Based on prior published data, we initially calculated the cost of AF for 1995, and then again for 2000 which was calculated from a combination of contemporary and extrapolated data from that time. These data have been used as the basis for forecasting AF costs in the UK and as a share of total NHS expenditure. Atrial fibrillation direct costs were split between cost driver categories; general practitioner (GP) consultations, GP referred OPD (outpatient department) visits, prescriptions and monitoring visits, primary admissions, and post-discharge OPD visits. Forecast assumptions used: (i) NHS expenditure from 2020 onwards assumed to increase at annual rate of 3%/year; and (ii) the UK inflation rate to increase by 2% annually. Sensitivity modelling of 3%, 4%, and 6% projected annual increase in AF prevalence amongst the population was applied. The estimated direct and proportion of NHS expenditure of AF in 2020 for each of the assumed increases of 3%, 4%, and 6% would be £1435 m (0.91%), £1741 m (1.11%), and £2548 m (1.62%), respectively. For 2030, the modelling would mean that the direct costs of AF and proportion of NHS expenditure would be £2351 m (1.11%), £3141 m (1.48%), and £5562 m (2.63%), respectively. For 2040, the modelling shows that the direct costs of AF and proportion of NHS expenditure would be £3851 m (1.35%), £5668 m (1.99%), and £12 143 m (4.27%), respectively. By far the largest contributor to the total direct AF costs in 2020 was for primary admissions (nearly 60%), with a further 7% with post-discharge outpatient clinic visits. Taken together the total for these two categories in 2020 would cost the NHS between £949 and £1685 m, depending on the projected increase in annual rate of AF prevalence. The full cost of AF related hospitalizations may be underestimated, due to the other admissions associated with a secondary coding of AF, which in 2020 are forecast to cost between £2269 and £4030 m, depending on the annual population increase of AF prevalence. There will be an increasing number of patients discharged to a nursing home after a hospital admission associated with a principal AF diagnosis, with cost estimates for this in 2000 being £111 m, and predicted to rise to between £346 and £614 m by 2020. Focusing on 2020, AF is predicted to directly cost the NHS a total of a minimum of £1435 m and a maximum of £2548 m (depending on AF prevalence); hence, between 0.9% and 1.6% of NHS expenditure, mostly from primary admissions. The total direct costs of AF would increase to 1.35-4.27% of NHS expenditure, over the next two decades. If hospitalizations can be avoided or reduced, we would substantially reduce the healthcare costs of AF to the NHS.

Analyzing drivers' perceived service quality of variable message signs (VMS).

Recent advance in VMS technology has made it viable to ease traffic congestion and improve road traffic efficiency. However, the drivers’ low compliance with the posted information may limit its performance to ease traffic congestion and improve traffic safety. This paper explores drivers’ attitude to the service quality of VMS system resulted from the identified predominant influencing factors. A questionnaire is developed and used for surveying 9,600 drivers in Beijing, China. The collected data are analyzed with a multiple indicators and multiple causes (MIMIC) model considering different driver categories (e.g., private car driver, office car driver, taxi driver). The results show that the causal relationships between latent variables and socio-demographic characteristic is significant. Driving frequency, attitude towards contents of VMS, drivers’ decision-making and the effectiveness of VMS message can directly and indirectly affect driver’s perceived quality of service. The attitude towards formats of VMS indirectly affect their QoS resulting from the effectiveness of VMS message, while there is no indirect impact for taxi drivers. Besides, the drivers’ decision-making directly affects the perceived quality of service for private car drivers and office car drivers, but there is no impact for taxi drivers. The findings of this study can provide guidance and reference for urban authorities to perform the relevant actions required to meet user expectations.

Bayesian regularized artificial neural networks for the estimation of the probability of default

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.

Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs

Active traffic management is the ability to dynamically manage congestion based on prevailing and predicted traffic conditions. Several technologies and algorithms have been employed to develop a set of tools utilised by Traffic Control/Traffic Management Centres with the aim to mitigate disruptions and improve traffic flow in the road networks. One such tool, belonging to the Advanced Traveller Information Systems category is Variable Message Signs. Variable Message Signs are located in major roads and provide real-time traffic information to the drivers so as to modify their behaviour considering the prevailing traffic conditions. To this end, the question arises. Is active traffic management in urban road networks – as currently provided by Variable Message Signs – effective for professional drivers? In plain words, do professional drivers modify their route choice, i.e. divert under the provision of real-time traffic information? A stated preference survey was performed to truck and taxi drivers to identify their response to information provided by Variable Message Signs. Results, in the form of ordered probit models indicated that the provided information is not that relevant to the investigated driver categories, as it is for passenger car drivers.

Three decades and still counting (the wrong things): An analysis of three reports on the possibility and practicality of shifting our academic currency from credit hours to competencies

AbstractThis study examines three milestone reports on competency‐based education (CBE) policy that were produced between 2002 and 2015 to identify whether additional arguments, not made in the three reports, could be formulated to increase the pace of transition from credit hours to competencies as the measure of academic learning. Using content analysis, frequency of topics and categorization within five change driver categories (social–cultural; economy and economics; policy and politics; teaching, learning, and technology; workforce) the study concludes that most arguments were made in the policy and teaching, learning, and technology categories implying that arguments based in the economics (cost) and social–cultural drivers could increase the pace of transition to competencies. The study specifically identifies emphasis on the equity of competency‐based instruction, pacing, and assessment over the current credit hour model as having significant potential to shift and increase the pace of CBE adoption.

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.

Drivers of Sustainable Supply Chain Management: Identification and Classification

With the increase in awareness of environmental and social issues associated with the development and the use of products, stakeholders—especially consumers—are showing more concern regarding these issues. To address new developments and changing trends, organizations are now compelled to identify and implement innovative and sustainable solutions, not only within their organizations’ boundaries, but also across the whole supply chain network. The primary goal of this paper was to identify and analyze drivers of sustainable supply chain management (SSCM) that influence or encourage organizations to undertake sustainability initiatives and implement sustainable solutions throughout their supply chains. For this purpose, a systematic literature review was conducted and 1559 drivers of SSCM were identified from 217 journal articles. Precise interpretation, clear definitions, restructuring, and classification into external and internal driver categories produced a list of 40 unique drivers of SSCM. The results revealed that regulatory and market pressures, with reference to the number of citations, are the most prevailing drivers of SSCM for the implementation of sustainability practices. Classification of the drivers of SSCM into primary and secondary driver categories may assist practitioners and decision makers in prioritizing sustainability-related initiatives and adopting sustainability practices across the whole supply chain network.

Effects of texting on accident risk during a sudden hazardous event: Analysis of predetection and postdetection phases

Objective: The present study aims to quantify the effects of texting and driver behavior on the accident risk associated with a sudden event. Further, the study attempts to compare the effects of driving behavior of inexperienced young and professional drivers on risk during predetection and postdetection phases of the event.Methods: Forty-nine drivers from 2 categories—inexperienced young drivers and experienced professional drivers—took part in simulated experiments. The participants drove in a free-flow road environment under 3 driving conditions: no distraction (baseline) and writing short and long texts while driving. The participants were exposed to a sudden hazardous event during each drive. Accident probability during the sudden event was modeled with a generalized linear mixed model (with a logit link function).Results: As expected, both texting tasks increased accident risk, and the risk was much higher for inexperienced young drivers than for professional drivers. Time lapsed in reducing speed increased the odds for accident risk significantly. A comparative analysis of the driver categories showed that impairment in driving behavior due to the texting tasks was similar for both groups during the predetection phase. However, the risk associated with the texting tasks was higher for young drivers during the postdetection phase. A possible reason could be that young drivers had 65% and 75% higher approach speeds (than the professional drivers) during the short and long text tasks, respectively.Conclusions: The results provide statistical evidence that increased speed is expressed as increased risk-taking behavior among young drivers, which subsequently is the main reason for their higher accident risk during texting tasks. Moreover, the results confirm that professional drivers are not able to mitigate the increased accident risk associated with texting tasks due to late detection of the event during the tasks.

DbCPM: a manually curated database for exploring the cancer passenger mutations.

While recently emergent driver mutation data sets are available for developing computational methods to predict cancer mutation effects, benchmark sets focusing on passenger mutations are largely missing. Here, we developed a comprehensive literature-based database of Cancer Passenger Mutations (dbCPM), which contains 941 experimentally supported and 978 putative passenger mutations derived from a manual curation of the literature. Using the missense mutation data, the largest group in the dbCPM, we explored patterns of missense passenger mutations by comparing them with the missense driver mutations and assessed the performance of four cancer-focused mutation effect predictors. We found that the missense passenger mutations showed significant differences with drivers at multiple levels, and several appeared in both the passenger and driver categories, showing pleiotropic functions depending on the tumor context. Although all the predictors displayed good true positive rates, their true negative rates were relatively low due to the lack of negative training samples with experimental evidence, which suggests that a suitable negative data set for developing a more robust methodology is needed. We hope that the dbCPM will be a benchmark data set for improving and evaluating prediction algorithms and serve as a valuable resource for the cancer research community. dbCPM is freely available online at http://bioinfo.ahu.edu.cn:8080/dbCPM.

Effects of phone use on driving performance: A comparative analysis of young and professional drivers

Phone use during driving is quite prevalent particularly among young drivers because of their risk-taking attitude and overconfidence on driving skills. Young drivers are at an increased risk because of their psychosocial status and inexperience. Therefore, performing secondary tasks during driving could be more dangerous for this group of drivers. This study aims to find the impact of conversation and texting on the performance of inexperienced young drivers (n = 25) and professional drivers (who are employed as drivers in a transport company; n = 24) with the help of driving simulator experiments. The distraction tasks are presented with two difficulty levels. A critical and comparative analysis of the driver categories is carried out with respect to their longitudinal (speed and acceleration) and lateral vehicle control (lane positioning and steering movements). Results show that young drivers compensate less and have lower longitudinal control during distracted driving. Young drivers manage to keep lesser steering reversals compared to professional drivers; but, show higher variations in lane positioning. The findings suggest that there is a higher deterioration in performance of the inexperienced young drivers during phone use. However, performance degradation of the experienced drivers is also significant, showing that higher experience cannot nullify the effects of increased workload during distracted driving.

Segmentation of business saving customer to improve average balance based on structural equation modeling (SEM) and recency, frequency, monetary (RFM): Case study Bank XYZ in Indonesia

As business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. RFM is a segmentation method based on past data and create an index on a client where the high loyalty and assume the behaviour of customers in the index will be the same in the future. Certainly, customers with high RFM score were customers who become the target of the Bank because these customers have high loyalty and valuable for the Bank. In this study, segmentation performed based on transactions which affect the increase in average balance using Structural Equation Model (SEM). The objects of RFM segmentation is to identify the customer in order to build a marketing strategy for each segment with different levels of loyalty. As the segmentation results we found three driver categories, High Recency, Middle Recency and Low Recency customer category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary. Middle Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered Churn customer where campaign category is reactivation