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ILM Assignment Help > ILM Level 4 > 8605-410 Assignment Example

8605-410 Assignment Example

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Unit 8605-410 · Credit Value: 4 · Level 4 · Guided Learning Hours: 16

Managing the analysis of secondary data is about making better decisions by finding and using existing information systematically rather than relying on gut instinct. Unit 8605-410 asks you to identify a research question relevant to your organisation, gather secondary data from credible sources, analyse it to draw conclusions, and lead a discussion with colleagues to evaluate the findings and their implications.

This assignment example follows a customer services manager in a 250-person insurance brokerage investigating why customer complaint volumes had increased 34% year-on-year — using existing organisational data, industry benchmarks, and published research to diagnose the root causes.

Contents

Learning Outcomes 1-2 — Choose a Research Subject and Gather Data

AC 1.1 — Identify a Research Subject Relevant to the Organisation

The research question: ‘What are the primary causes of the 34% increase in customer complaints in 2024-2025, and which causes are within the organisation’s control to address?’ This question is strategically relevant because complaint volumes directly affect client retention (the brokerage’s renewal rate dropped from 82% to 76% in the same period), FCA regulatory compliance (persistent complaint trends trigger regulatory scrutiny), and staff morale (advisors handling increased complaints report higher stress and lower job satisfaction).

AC 2.1 — Gather Secondary Data from Academic, Official, and Commercial Sources

Four categories of secondary data were collected. Internal organisational data: the complaint database (categorised by complaint type, product, and resolution outcome), customer satisfaction survey results (quarterly, n=450 per wave), and staff absence data. Industry data: the Financial Ombudsman Service annual report showing sector-wide complaint trends; the British Insurance Brokers’ Association member benchmark report on complaint ratios. Regulatory data: FCA guidance on complaint handling standards and the FCA’s annual data publication on complaint volumes by firm size. Academic sources: published research on complaint management, service recovery, and the relationship between employee engagement and customer experience (Heskett et al., 2023 — the service-profit chain model).

Assessor tip: Use at least three different types of source — internal data, industry/official data, and academic research. The assessor wants to see that you can find, evaluate, and triangulate data from multiple sources, not just rely on one.
Learning Outcomes 3-5 — Analysis, Discussion, and Evaluation AC 3.1 — Analyse Collected Data to Draw Conclusions Analysis of the internal complaint data revealed three findings. First, 62% of the increase was concentrated in one product category — motor insurance renewals — where pricing algorithm changes had generated unexpected premium increases that advisors could not adequately explain to customers. Second, complaint resolution times had increased from an average of 8 days to 14 days, driven by a 22% increase in

in complaint volumes without corresponding increase in complaint-handling staff. Third, correlation analysis between the staff absence data and complaint volumes showed that weeks with the highest absence rates also had the highest complaint volumes — suggesting a link between staffing pressure and service quality. Industry benchmark data confirmed that the brokerage’s complaint ratio (4.2 per 1,000 policies) was above the sector average (2.8 per 1,000) — indicating an organisation-specific problem rather than a market-wide trend. The service-profit chain model (Heskett et al., 2023) provided a theoretical framework: employee dissatisfaction (manifested as increased absence) degrades service quality, which generates customer complaints, which further increases employee stress — a reinforcing negative cycle. AC 4.1-5.1 — Group Discussion and Outcome Evaluation I presented the findings to a group of six colleagues — the complaints team leader, two senior advisors, the motor insurance underwriter, the HR business partner, and my line manager. The discussion validated the data findings and generated three actionable recommendations: (1) retrain advisors on explaining pricing algorithm outputs in plain language (addressing the motor renewal complaint driver), (2) recruit one additional complaints handler to reduce resolution times to the 8-day target (addressing the staffing gap), and (3) implement a monthly complaint trend review replacing the current quarterly ...

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8605-400 — Understanding the Management Role8605-414 — Information-Based Decision MakingILM Level 4 — All Units