In the initial phase of our project, we conducted a comprehensive exploration of MiLife Insurance's internal processes and customer experiences.

This involved organizing and facilitating 7 intensive workshops with a diverse group of over 20 internal stakeholders. These sessions provided crucial insights into the company's operations and challenges.

During these sessions we also collaboratively defined and mapped the 5 key phases of the retail customer journey. This process helped visualize the entire customer experience from initial contact to long-term engagement.

We run an initial primary research scoping it to only 12 customers. We employed a multi-faceted research approach to identify and prioritize pain points and opportunities. Utilizing a combination of user interviews and data analytics, 58 distinct areas for improvement were uncovered.

These findings were then systematically evaluated and ranked based on their potential impact and the estimated effort required for implementation. This strategic prioritization ensured focus on high-value, actionable solutions that align with both user needs and business objectives.

Building on Phase 1 insights, we implemented a mixed-methods study to validate and expand our understanding:

Quantitative: Deployed a large-scale survey (n > 500) to gather statistically significant data on user preferences and pain points.

Qualitative:
Facilitated 3 focus group discussions to uncover shared experiences and group dynamics;
Conducted 20 contextual inquiry sessions, observing and interviewing customers in their natural environments to capture authentic behaviors and unarticulated needs;
Shadowed dozens of field agents, gaining first-hand exposure to frontline challenges and opportunities.

This multi-faceted approach allowed for triangulation of data, ensuring robust insights that account for both stated and observed user behaviors. The synthesis of these findings informed a nuanced understanding of user needs, leading to data-driven, user-centered recommendations for product and service improvements.

Analysis In Detail
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For Phase 2, our analysis and synthesis process was rigorous and multi-layered, designed to extract maximum value from the rich data collected.

Our quantitative analysis began with cleaning and preprocessing the survey data from over 500 respondents. We applied descriptive statistics to identify broad trends and patterns in user behavior and preferences. This was followed by inferential statistical analyses to test hypotheses and uncover significant relationships between variables. We employed cluster analysis to segment users based on their behavioral and attitudinal characteristics, revealing distinct user groups with unique needs and pain points.

Simultaneously, we dove deep into our qualitative data. Transcripts from the focus groups and contextual interviews were subjected to thematic analysis, identifying recurring patterns and emerging themes. We used affinity diagramming as a collaborative method to organize and categorize these insights, allowing us to see connections across different data sources.

The observational data from shadowing field agents was particularly valuable, providing context to user-reported issues and revealing pain points that users themselves might not have articulated.

Patterns & Trends Summary

The synthesis phase was where the magic happened. We created a comprehensive insights framework that mapped quantitative trends to qualitative findings, providing a holistic view of the user experience.

This framework allowed us to validate and expand on our Phase 1 findings, adding depth and nuance to our understanding of user needs. Based on this framework, we developed detailed personas that represented our key user segments. These personas went beyond demographic information to capture behavioral patterns, motivations, and pain points, serving as powerful tools for empathy and decision-making in the design process.

We then translated our insights into actionable design opportunities and requirements through the creation of "How Might We" statements. These open-ended questions framed our findings as springboards for innovation, setting the stage for ideation.

Finally, we prioritized our insights and potential opportunities using an Impact-Effort matrix. This involved a collaborative session with stakeholders to assess each insight's potential impact on user satisfaction and business goals, balanced against the estimated effort required to address it.

Key Finding: A significant mismatch was identified between the premium deductions customers agreed to and the actual amounts deducted from their accounts.

Context: This insight emerged from the triangulation of quantitative survey data, qualitative interviews, and behavioral observations. It provides a concrete explanation for the observed 35%+ cancellation rate within the first month of sign-up.

Impact: This discrepancy is likely a primary driver of customer dissatisfaction and early policy termination, particularly affecting the growing segment of blue-collar workers who may be more sensitive to unexpected financial fluctuations.