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Coherent · 2022

Insurance tooling for non-engineers

Designed Coherent Spark's authoring surfaces so actuaries could ship product changes without an engineering ticket.

Role

Product Designer

Users

Actuaries · Product owners

Region

APAC, EMEA

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Insurance tooling for non-engineers — hero visual

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TL;DR

  • Translated spreadsheet-native workflows into a guided authoring experience.
  • Cut product-change cycle from quarters to days for pilot customers.
  • Reduced support escalation rate by ~40% during pilot.

02 — Scope

What I owned on Coherent

Research, product design and authoring patterns for the Coherent Spark tooling — respecting the actuary's spreadsheet mental model.

Embedded research with actuarial teams across APAC to learn what could change about the spreadsheet metaphor — and what had to stay.

Contextual inquiry

On-site sessions with three actuarial teams across APAC to map real authoring rituals.

Co-design workshops

Tested which review and approval patterns were welcomed versus actively resisted.

Prototype benchmarking

Quantified error rate against free-form editing to validate guided authoring.

Pilot analysis

Tracked product-change cycle time and support escalations through customer pilots.

03 — Context

The spreadsheet was the product

Actuaries lived in Excel. Coherent's pitch was to keep that intelligence but free it from manual deployment. The design problem was respecting the existing mental model while introducing version control, review and observability.

04 — Research question

What we needed to learn

What can we change about the spreadsheet metaphor without breaking the actuary's confidence — and what must stay identical?

05 — Research design

Methods, phase by phase

Contextual inquiry with three actuarial teams across APAC.

Co-design sessions to test which review and approval rituals were welcomed vs. resisted.

06 — Key findings

What the research surfaced

Qualitative

Cell-level trust is non-negotiable

If a cell changed without an audit trail, actuaries lost trust in the whole tool.

3 contextual inquiries

Qualitative

Review rituals must look familiar

PR-style review felt alien; spreadsheet-style change tracking landed instantly.

Co-design workshops

Quantitative

Guided authoring cut error rate by ~35%

Field-level guidance reduced authoring errors compared to free-form editing.

Prototype benchmark

Quantitative

Cycle time dropped from quarter → days

Pilot customers shipped product changes in a release window measured in days, not quarters.

Customer pilot data

07 — Design decisions

Finding → intervention

Finding
Intervention
Cell-level trust
Every value carries provenance: who changed it, when, with what test result.
Familiar review rituals
Spreadsheet-style diff view with inline approve/comment.
Guided authoring
Structured editors for tariff and rule logic with live validation.

08 — Outcomes

Measured impact

Metric
Before
After
Δ Impact
Product-change cycle time
1 quarter
3–5 d
≈ 95% faster
Authoring error rate
100
65
−35%
Support escalations
100
60
−40%

09 — Reflection

Looking back

What worked

Respecting the spreadsheet mental model instead of fighting it.

What I'd improve

More embedded sessions with non-pilot customers earlier — pilots self-selected for tolerance.

Key learning

In expert tooling, familiarity is a feature.

Stakeholder skill

Selling 'guardrails' to actuaries who had never accepted them before.

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