Credit Direct Limited is a non-bank finance company with its Head-Quarters in Lagos, Nigeria. The company was established in 2006 and is focused on providing Payroll based consumer loans to eligible individuals. The Company currently operates in 25 states in Nigeria including the Federal Capital Territory– Abuja. With a staff strength of over 1000 employees and an active customer base in excess of 300,000, Credit Direct Limited is positioning itself to become the dominant market leader in the unsecured micro-lending (payroll lending) space in Nigeria and indeed Sub-Saharan Africa.
Job Summary
We are seeking a data-savvy, customer-focused Data Analyst to join our Customer Experience (CX) Unit. In this role, you will leverage data analytics and business intelligence to produce actionable insights, build predictive data models, and create impactful reports that enhance the customer journey. Your work will drive strategies to improve customer satisfaction, engagement, and retention while optimizing the effectiveness of our CX programs. Ideal candidates bring a solid understanding of Artificial Intelligence (AI) and a passion for using data to inform strategic CX decision-making and elevate operational efficiency.
RESPONSIBILITIES:
Risk Assessment and Analysis:
- Design and maintain data models and interactive dashboards using BI tools (e.g., Tableau, Power BI) for real-time insights into CX metrics such as NPS, CSAT, loyalty, resolution rates, customer segments, channel activities, feex engagements, contact centre CRM interactions and drop-offs.
- Conduct complex analyses on customer data (behavior, transactions, feedback) to identify trends, patterns, and root causes of key CX issues, providing actionable insights.
- Map and analyse customer journeys to identify high-impact touchpoints, friction points, and key behaviours across channels to enhance engagement, reduce churn, and personalize CX strategies.
- Develop predictive models to forecast customer needs, churn, and loyalty, enabling proactive interventions by anticipating potential CX issues.
- Collaborate with the tech team to train, optimize, and expand AI chatbot capabilities. Enhance customer experience by analysing failed bot responses, retraining for First Contact Resolution (FCR), reviewing customer drop-off points, and creating self-service flows that improve resolution.
- Analyze customer drop-off points or pain points on channels using various data sources. Partner with the tech team to implement AI-driven self-service solutions, automating responses and simplifying customer interactions.
- Evaluate the ROI of CX programs (e.g., loyalty programs, referrals, events, SMS blasts) by analysing costs, income, and net impact on engagement and retention to inform resource allocation.
- Track and analyze contact center metrics, including call volume, resolution rates, and response times, creating dashboards to monitor performance and project service volumes.
- Develop and implement a BI strategy aligned with CX objectives, collaborating with CX leads to identify analytical needs and deliver data solutions that drive CX improvements.
- Automate reporting for key KPIs such as customer referrals, loyalty, dormancy reactivations, and cross-selling income to enhance efficiency and data reliability.
- Analyze customer sentiment data from NPS, CSAT, surveys, and feedback channels, identifying trends and providing insights for CX improvements.
- Examine dormancy and churn patterns to identify risk factors and provide insights for effective reactivation campaigns, improving customer retention.
- Partner with Collections, Engagement, and Technology teams to monitor deduction patterns, refunds, and transaction data, providing CX-related insights for proactive issue resolution.
Requirements
- Bachelor’s degree in data science, Computer Science, Statistics, Business Analytics, or a related field.
- Professional certifications in Business Intelligence (e.g., Microsoft Power BI, Tableau), Data Analytics, or Machine Learning, SQL are a plus.
- >/=3years in data analytics, science, machine learning.
- Experience in a similar role, preferably within a customer experience (CX) or data analytics function.
- Experience working with chatbots, digital products (web, mobile app, etc).
COMPETENCIES REQUIREMENTS:
Technical:
- Data Analysis & BI Tools: Proficiency in Tableau, Power BI, or similar tools for data visualization and dashboarding.
- Data Modeling: Skilled in designing and managing data models for reporting and advanced analytics.
- SQL: Advanced knowledge of SQL for querying and managing data in relational databases.
- Statistical & Predictive Analytics: Ability to apply statistical analysis and predictive modeling techniques for forecasting and customer behavior analysis.
- Programming Skills: Proficiency in Python or R for advanced data analysis and automation.
- Data Automation: Experience with ETL processes and automation to streamline reporting.
- AI Chat Bot: Beginner or advance knowledge of implementing AI driven customer support chat bots.
- Customer Data Management: Familiarity with CRM systems, customer journey analytics, and CX metrics (e.g., NPS, CSAT).
- Data Quality & Governance: Strong understanding of data governance principles to maintain data accuracy, quality, and compliance.
Behavioural:
- Analytical Thinking: Strong problem-solving skills, with the ability to identify root causes and trends within complex data sets.
- Communication: Ability to translate complex data findings into actionable insights and communicate them effectively to non-technical stakeholders.
- Collaboration: Work well with cross-functional teams, particularly within CX, Technology, Digital Transformation and Engagement, to achieve shared objectives.
- Attention to Detail: High attention to data accuracy and quality in all reporting and analytical tasks.
- Proactivity: Ability to anticipate customer needs, CX team needs, provide timely insights, and recommend strategic improvements.
- Adaptability: Able to adjust to changing CX strategies and project needs, while continuously updating technical skills.
Method of Application
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