Customer Insights - Data Analyst

As a Customer Insights - Data Analyst at M-KOPA, your key responsibilities include leading data analysis projects, capturing customer and agent feedback, cleaning and interpreting data using Excel, generating insights and recommendations, designing research reports and dashboards, and presenting findings to management. You'll also collaborate with cross-functional teams to answer critical business questions while ensuring the quality and accuracy of data.

To apply for the Customer Insights - Data Analyst position at M-KOPA, you should have a bachelor's degree in a quantitative field or equivalent experience, along with at least two years in a similar role. Proficiency in Excel is essential, and familiarity with BI platforms like Looker or Power BI is a plus. Strong written communication skills and a knack for insightful analysis will greatly benefit your application.

Customer Insights - Data Analysts at M-KOPA primarily use Excel for data cleansing, interpretation, and analysis. Knowledge of BI platforms like Looker or Power BI is also beneficial for creating impactful visualizations and reports, allowing analysts to derive actionable insights from complex data sets.

Collaboration is central to the Customer Insights - Data Analyst position at M-KOPA. You'll work alongside various technical and business teams to address important questions and drive data-driven decision-making across the company, ensuring that insights are effectively translated into actions that benefit the organization.

At M-KOPA, Customer Insights - Data Analysts have access to diverse development programs, coaching partnerships, and on-the-job training that empower employees to grow in their careers. With a commitment to building skills and encouraging continuous learning, there are numerous pathways for advancement within the organization.

When answering this question, highlight your proficiency with tools like Excel and any BI platforms such as Looker or Power BI. Share specific projects where you've used these tools to analyze data and draw insights, emphasizing the impact of your analysis on business decisions.

To ensure data quality, I implement validation checks at various stages of the data collection and analysis process. I regularly cross-reference data sources and maintain clear documentation of my methods. This attention to detail helps in producing accurate insights that stakeholders can trust.

Share a specific scenario where your data analysis provided actionable insights that influenced a business decision. Discuss the data you used, your findings, and how the decision taken as a result positively impacted the organization.

I focus on simplifying the data without losing its essence. By using visual aids such as graphs and charts and summarizing key points in bullet form, I make it easier for the audience to grasp insights. Providing context and relating findings to business goals also helps engage non-technical stakeholders.

I prioritize projects by assessing their impact on business goals and urgency. Utilizing project management tools, I keep track of deadlines and milestones, allowing me to stay organized. Regular communication with stakeholders also helps to align priorities and expectations.

Both quantitative and qualitative analyses are essential for comprehensive insights. Quantitative analysis provides hard numbers and patterns, while qualitative data offers context and motivations behind those numbers. Integrating both approaches leads to a fuller understanding of customer behavior and market trends.

I stay current by subscribing to relevant industry publications, attending webinars and conferences, and participating in online courses. Engaging with data analysis communities and forums also allows me to learn from peers and share insights about emerging tools and methodologies.

When faced with unexpected challenges, I first assess the issue and gather additional information to understand the root cause. If needed, I collaborate with colleagues for fresh perspectives. It's essential to remain adaptable and open to adjusting my approach to find effective solutions.

Storytelling is crucial in data analysis because it helps communicate insights in a relatable way. By weaving data into a narrative that highlights the problem, analysis, and recommendations, I can engage the audience and emphasize the impact of findings on decision-making.

I start with clearly defined objectives related to the business goals. I then formulate open-ended questions that encourage exploration of the data. Collaborating with cross-functional teams also helps me understand different perspectives and ask the right questions that matter to our stakeholders.