Background

Open University of Kenya (OUK) is an equal opportunity employer and, therefore, applicants of either gender, youths, persons living with disabilities and those from marginalized areas are encouraged to apply.

Responsibilities

• Apply Predictive Modeling: Guide students in using regression analysis and forecasting models to predict sales trends and customer lifetime value based on historical data.
• Conduct Customer Segmentation: Teach the application of clustering techniques (e.g., RFM analysis) to divide customer bases into actionable segments for targeted marketing efforts.
• Visualize Data Dashboards: Train students to use tools like Tableau or Power BI to create interactive dashboards that effectively communicate marketing KPIs to stakeholders.
• Execute A/B Testing: Design experiments for students to run A/B tests on website elements or email campaigns, analyzing statistical significance to make data-driven decisions.
• Interpret Marketing Mix Models: Instruct on how to decompose the impact of various marketing channels (TV, digital, print) on overall sales to optimize budget allocation.
• Manage Customer Journeys: Oversee student projects that use path analysis to map and analyze the digital customer journey, identifying key drop-off points and optimization opportunities.

• Guide students in using regression analysis and forecasting models to predict sales trends and customer lifetime value based on historical data.
• Teach the application of clustering techniques (e.g., RFM analysis) to divide customer bases into actionable segments for targeted marketing efforts.
• Train students to use tools like Tableau or Power BI to create interactive dashboards that effectively communicate marketing KPIs to stakeholders.
• Design experiments for students to run A/B tests on website elements or email campaigns, analyzing statistical significance to make data-driven decisions.
• Instruct on how to decompose the impact of various marketing channels (TV, digital, print) on overall sales to optimize budget allocation.
• Oversee student projects that use path analysis to map and analyze the digital customer journey, identifying key drop-off points and optimization opportunities.

• Predictive Modeling
• Regression Analysis
• Forecasting Models
• Customer Segmentation
• Clustering Techniques (e.g., RFM analysis)
• Data Visualization
• Tableau
• Power BI
• A/B Testing
• Statistical Significance
• Marketing Mix Models
• Path Analysis

• Expertise in marketing analytics principles and methodologies.
• Proficiency in data analysis tools and software (e.g., Tableau, Power BI, statistical software).
• Strong understanding of predictive modeling, customer segmentation, A/B testing, and marketing mix modeling.
• Excellent communication and presentation skills to effectively teach and guide students.
• Ability to manage student projects and provide constructive feedback.