Using analytical CRM system to reduce churn in the telecom sector: A macine learning approach

Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019Customers are considered to be the most valuable assets of any business, and thus their loyalty is key to profitability as they indulge in repeat purchases and attract their colleagues through word-of-mouth. In competitive markets such as telecommunications, customers have a lot of flexibility due to the variety of service providers available and the introduction of mobile number portability (MNP) thus they can easily switch services and service providers. Customer churn is, therefore, a major problem among telecommunication companies hence their quest to reduce customer churn rate and retain an existing customer. Customer relationship management systems have been used over the years to track patterns within the customer data, but this could be improved notably with the technological advances hitting the universe on a daily basis. We have moved past the age of innovations around steam engines, electricity, computers, mobile, internet to the current technology trends in artificial intelligence and big data. We are at the cusp of a new wave where enterprises have embraced the application of machine learning in streamlining different business processes. Telecom companies have the advantage of mining large customer datasets that can be leveraged on for predictive analysis using data science. This project explores the use of analytical CRM system in reducing customer churn in the telecom industry using machine learning algorithms to predict customer behavior in order to retain them. Its goal is to analyze all relevant customer data and develop focused customer retention programs. This is on the focus that if you could somehow predict in advance which customers are at risk of leaving, you could develop focused customer retention programs to reduce customer churn.Ashesi Universit

Embracing counseling and psychotherapy in Kenya

This paper looks at the status of mental health in Kenya with specific focus on counseling and psychotherapy. It looks at the history of counseling and psychotherapy in Kenya, counselor educations programs, accreditation, licensure and certification, current counseling and psychotherapy theories, processes and trends, and research and supervision. Its purpose is to examine how counseling and psychotherapy is developing in Kenya. It explores how Western methods of psychotherapy are being incorporated in treatment of individuals with mental illness. It also looks at possible ways in which traditional methods of healing can be incorporated into the treatment of mental illness. This study is a literature review of recently published works by various professionals involved in mental health research and training in Kenya. The counseling profession in Kenya is in its formative years, but a lot of research and training is being implemented to meet the mental health needs of Kenyans

Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey

The Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues

COVID-19 Prevention Behaviours and Vaccine Acceptability, and Their Association with a Behaviour Change Campaign in Somalia: Analysis of a Longitudinal Cohort

Somalia experienced its first wave of COVID-19 infections in March 2020 and has experienced fluctuating infection levels since. Longitudinal data on suspected cases of COVID-19, attitudes, and behaviours were collected by telephone interviews of cash-transfer programme beneficiaries from June 2020–April 2021. A multi-media Social and Behaviour Change Communication (SBCC) campaign was designed and implemented from February 2021 to May 2021. Between the end of the first wave and the onset of the second the perceived threat from COVID-19 increased, with the proportion of respondents viewing it as a major threat increasing from 46% to 70% (p = 0.021). Use of face coverings increased by 24% (p < 0.001) and hand shaking and hugging for social greeting decreased, with 17% and 23% more people abstaining from these practices (p = 0.001). A combined preventative behaviour score (PB-Score) increased by 1.3 points (p < 0.0001) with a higher score in female respondents (p < 0.0001). During wave 2, vaccine acceptance was reported by 69.9% (95% CI 64.9, 74.5), overall. Acceptance decreased with increasing age (p = 0.009) and was higher in males (75.5%) than females (67.0%) (p = 0.015). Awareness of the SBCC campaign was widespread with each of the 3 key campaign slogans having been heard by at least 67% of respondents. Awareness of 2 specific campaign slogans was independently associated with an increased use of face coverings (aOR 2.31; p < 0.0001) and vaccine acceptance (aOR 2.36; p < 0.0001. Respondents reported receiving information on the pandemic from a wide range of sources with mobile phones and radio the most common. Trust in different sources ranged widely

Data innovation in response to COVID-19 in Somalia: application of a syndromic case definition and rapid mortality assessment method.

BACKGROUND: During the COVID-19 pandemic, the importance of reliable public health data has been highlighted, as well as the multiple challenges in collecting it, especially in low income and conflict-affected countries. Somalia reported its first confirmed case of COVID-19 on 16 March 2020 and has experienced fluctuating infection levels since then. OBJECTIVES: To monitor the impact of COVID-19 on beneficiaries of a long-term cash transfer programme in Somalia and assess the utility of a syndromic score case definition and rapid mortality surveillance tool. METHODS: Five rounds of telephone interviews were conducted from June 2020 - April 2021 with 1,046-1,565 households participating in a cash transfer programme. The incidence of COVID-19 symptoms and all-cause mortality were recorded. Carers of the deceased were interviewed a second time using a rapid verbal autopsy questionnaire to determine symptoms preceding death. Data were recorded on mobile devices and analysed using COVID Rapid Mortality Surveillance (CRMS) software and R. RESULTS: The syndromic score case definition identified suspected symptomatic cases that were initially confined to urban areas but then spread widely throughout Somalia. During the first wave, the peak syndromic case rate (311 cases/million people/day) was 159 times higher than the average laboratory confirmed case rate reported by WHO for the same period. Suspected COVID-19 deaths peaked at 14.3 deaths/million people/day, several weeks after the syndromic case rate. Crude and under-five death rates did not cross the respective emergency humanitarian thresholds (1 and 2 deaths/10,000 people/day). CONCLUSION: Use of telephone interviews to collect data on the evolution of COVID-19 outbreaks is a useful additional approach that can complement laboratory testing and mortality data from the health system. Further work to validate the syndromic score case definition and CRMS is justified

Achieving Enterprise Cyber Resilience Through Situational Awareness

A Report Authored by Paula Musuva Kigen, a Lecturer at the School and Science and Technology at USIU-Africa, Centre for informatics Research and innovation (CIRI

Neonatal mortality in Kenyan hospitals: a multisite, retrospective, cohort study

Background Most of the deaths among neonates in low-income and middle-income countries (LMICs) can be prevented through universal access to basic high-quality health services including essential facility-based inpatient care. However, poor routine data undermines data-informed efforts to monitor and promote improvements in the quality of newborn care across hospitals.Methods Continuously collected routine patients’ data from structured paper record forms for all admissions to newborn units (NBUs) from 16 purposively selected Kenyan public hospitals that are part of a clinical information network were analysed together with data from all paediatric admissions ages 0–13 years from 14 of these hospitals. Data are used to show the proportion of all admissions and deaths in the neonatal age group and examine morbidity and mortality patterns, stratified by birth weight, and their variation across hospitals.Findings During the 354 hospital months study period, 90 222 patients were admitted to the 14 hospitals contributing NBU and general paediatric ward data. 46% of all the admissions were neonates (aged 0–28 days), but they accounted for 66% of the deaths in the age group 0–13 years. 41 657 inborn neonates were admitted in the NBUs across the 16 hospitals during the study period. 4266/41 657 died giving a crude mortality rate of 10.2% (95% CI 9.97% to 10.55%), with 60% of these deaths occurring on the first-day of admission. Intrapartum-related complications was the single most common diagnosis among the neonates with birth weight of 2000 g or more who died. A threefold variation in mortality across hospitals was observed for birth weight categories 1000–1499 g and 1500–1999 g.Interpretation The high proportion of neonatal deaths in hospitals may reflect changing patterns of childhood mortality. Majority of newborns died of preventable causes (>95%). Despite availability of high-impact low-cost interventions, hospitals have high and very variable mortality proportions after stratification by birth weight