CCC Intelligent Solutions Data Analyst Case Interview — Claims Analytics & STP Impact

What this covers: A realistic, business-first analytics case reflecting CCC Intelligent Solutions’ insurtech domain and interview style. You’ll diagnose the impact of CCC Estimate–STP on auto-claims outcomes across CCC’s network (insurers, DRP repairers, OEMs) and recommend actions to reduce cycle time without increasing leakage. Expect a collaborative, conversational format with SQL-first analysis, clear visuals, and pragmatic business reasoning. Business scenario: You’ve joined CCC’s Product Analytics team supporting claims automation. An insurer piloted CCC Estimate–STP on photo-estimated, repairable claims in three states last quarter. Leadership wants to know: (1) Did cycle time improve vs. human-written estimates? (2) Did supplement rate or severity drift? (3) Are DRP shops seeing different parts utilization or rework? (4) What guardrails should we add to protect estimate quality and policyholder experience? ([cccis.com](https://www.cccis.com/news-and-insights/posts/ccc-announces-plan-to-deliver-on-industry-vision-for-straight-through-processing?utm_source=chatgpt.com)) Data you get (sample schema): - claims(core): claim_id, loss_dt, state, channel(FNOL app/phone/web), drp_flag, tl_flag, close_dt, nps - estimates: claim_id, writer_type(STP/human), estimate_dt, est_lines, labor_hrs, est_severity_usd - supplements: claim_id, supp_ct, supp_usd, reasons(parts_avail/hidden_damage/price_diff) - parts_lines: claim_id, line_id, oem_flag, alt_flag, recycled_flag, price_usd - photo_ai: claim_id, ai_damage_score(0–1), image_cnt - telematics(CCC X): claim_id, delta_v, crash_dt, crash_sev_bin (if available) ([cccis.com](https://www.cccis.com/news-and-insights/posts/ccc-launches-ccc-x-connected-data-exchange?utm_source=chatgpt.com)) Your tasks (case flow): 1) Clarify metrics: Define cycle_time = close_dt − loss_dt; supplement_rate = share of claims with supp_ct > 0; severity drift = Δ median est_severity_usd vs. baseline. Call out data quality risks (missing close_dt, duplicate claim_id, timezone gaps). 2) SQL analysis (whiteboard/pseudocode is fine): - KPI rollups by writer_type, state, and drp_flag: median cycle_time, supplement_rate, median labor_hrs, parts mix (%OEM/%alt/%recycled). - Accuracy proxy: STP vs. final paid severity — build a calibration view (P50/P90 errors). - Risk lens: Segment by ai_damage_score and delta_v to flag cohorts where STP underperforms. ([cccis.com](https://www.cccis.com/news-and-insights/posts/ccc-announces-plan-to-deliver-on-industry-vision-for-straight-through-processing?utm_source=chatgpt.com)) 3) Experiment/readout design: Propose an A/B design or diff-in-diff vs. pre-pilot markets; define success metrics (−10% median cycle_time; ≤+1pp supplement_rate; stable parts mix; no NPS drop). 4) Visualization ask: Sketch a Tableau-style one-pager with: (a) funnel from FNOL → estimate → repair start → close, (b) cohort heatmap (ai_damage_score × drp_flag) for supplement_rate, (c) parts-mix stacked bars by writer_type. Interviewers may ask how you’d productionize this in CCC’s stack. ([static.glassdoor.nl](https://static.glassdoor.nl/Interview/CCC-Intelligent-Solutions-Interview-Questions-E4574_P3.htm?utm_source=chatgpt.com), [simplyhired.com](https://www.simplyhired.com/job/4ZKQ833Vnvh9vXvSK2G7TSynNSsg5-7i-Hlo4aCOrcz5pbZ9zQaCSQ?utm_source=chatgpt.com)) 5) Recommendation & safeguards: Identify 2–3 high-leverage levers (e.g., raise STP eligibility threshold for high ai_damage_score vehicles; route MY<2014 or luxury OEMs to human review; add OEM-parts rule in certain states). Tie to AI governance, privacy, and customer-first culture. ([cccis.com](https://www.cccis.com/about/environmental-social-and-governance?utm_source=chatgpt.com)) How CCC-specific expectations show up: - Domain grounding: Speak the language of claims (cycle time, severity, DRP vs. non-DRP, total loss) and CCC ONE network dynamics across insurers, repairers, and OEMs. ([cccis.com](https://www.cccis.com/collision-repairers/ccc-one?utm_source=chatgpt.com)) - Interview style: Typically begins with recruiter screen, followed by technical (SQL/Python) and a panel discussing your approach; some teams use HackerRank-style questions. Expect resume drill-down plus scenario reasoning rather than trick puzzles. ([glassdoor.com](https://www.glassdoor.com/Interview/CCC-Intelligent-Solutions-Interview-Questions-E4574.htm?utm_source=chatgpt.com)) - Tools/context you can reference: SQL, Python, Tableau/MicroStrategy; data flows from insurers/repairers/OEMs; occasional ETL and cloud context. ([simplyhired.com](https://www.simplyhired.com/job/4ZKQ833Vnvh9vXvSK2G7TSynNSsg5-7i-Hlo4aCOrcz5pbZ9zQaCSQ?utm_source=chatgpt.com)) Scoring rubric (used by panel): - SQL and analytic rigor (35%): Correct metrics, joins, cohorting, and guardrails for bias/noise. - Business insight (30%): Clear story linking STP impact to cycle time, supplements, and experience. - Communication & visuals (20%): Executive-ready readout, clean charts, crisp tradeoffs. - Data ethics & governance (15%): Sensible eligibility rules, privacy-minded handling of telematics/images, alignment with CCC’s AI governance. ([cccis.com](https://www.cccis.com/about/environmental-social-and-governance?utm_source=chatgpt.com)) Logistics used in the case session: 10 min brief + 30–40 min analysis + 15 min readout + 5 min Q&A. Interviewers encourage clarifying questions, thinking aloud, and pragmatic tradeoffs over perfect math.