Viscore knowledge centre
How to measure the return on a 3D configurator
Configurator success is not measured by opens alone. More accurate product choices, better-prepared sales enquiries and a simpler quotation process require several data sources to be read together.
Start with the business objective
Reducing wrong size or fabric choices may matter most to a furniture brand, while an industrial manufacturer may prioritise quote preparation time. One metric does not describe success for every project.
Set a primary outcome, secondary outcomes and guardrail metrics before the first release. A conversion increase should not hide worse support demand or page performance.
- Commercial: conversion, quote requests, order value
- Customer: completion, correct choice, support need
- Operational: quote time, duplicate data entry
- Technical: loading, interaction and error rate
Create a comparable measurement design
A pre-launch period, comparable product pages or a controlled test can provide a baseline. Record concurrent changes in campaigns, price, stock and seasonality.
Opening, making a meaningful choice, completing and requesting a quote are separate events. Opens alone do not demonstrate effective use.
- Define the baseline period and target group
- Document events and outcomes
- Test data quality
- Compare for a sufficient period
- Report context and limitations
Create an event plan for the configuration funnel
Analytics events should not be added at random after the interface is built. Define which business question each event answers, when it fires and which parameters it carries.
Do not send personal information or free text as analytics parameters. Controlled values such as product family, step, option group and error type make reporting more reliable.
| Event | Trigger | Example parameter |
|---|---|---|
| configurator_start | First meaningful interaction | product_family |
| option_select | An option is applied | step, option_group |
| configuration_complete | A valid selection is completed | configuration_type |
| quote_request | The request is sent successfully | source, product_family |
| configuration_error | A rule or service fails | error_type, step |
Separate measurement layers
Behaviour shows where a journey succeeds or fails; commercial outcomes help assess business impact. Read them together rather than compressing them into one percentage.
| Layer | Example metrics |
|---|---|
| Usage | Start, selection, completion, error |
| Sales | Quote, conversion, order value |
| Operations | Preparation time, re-entry, correction |
| Customer | Support, wrong choice, return reason |
Connect results to a regular decision cycle
A dashboard does not create improvement on its own. Product, sales and technical teams need to review the same definitions on a regular cadence and choose the next action.
First-week behaviour and three-month commercial results should not be interpreted at the same frequency. Technical errors and abandonment signals can be reviewed quickly; conversion and operational impact need sufficient volume.
- Weekly: errors, loading and step abandonment
- Monthly: completion, enquiry quality and sales follow-up
- Periodically: conversion, quote time and order outcome
- Data scope and concurrent changes in every report
- Named action and owner after each review
Interpret external case studies responsibly
Shopify’s Gunner Kennels case reports conversion and return changes in a context that included 3D and AR. Threekit’s Tailored Brands case concerns in-store visual configuration. Conga’s manufacturer example describes a broader CPQ transformation.
These examples indicate possible areas of impact; they are not Viscore outcomes, industry averages or proof of causality. Do not use a percentage as a target when period, sample and concurrent changes are not available.
- Preserve technology and channel context
- Separate percentage change from percentage points
- Do not combine unrelated cases into one ROI figure
- Do not promise results without your own baseline
Sources and further reading
- Shopify — Gunner Kennels
Provider-published case study involving 3D and AR.
- Threekit — Tailored Brands
Provider-published in-store visual configuration case study.
- Conga — Industrial Manufacturer
Provider-published CPQ and quotation transformation case study.
- Google Analytics — Events
Official guide to measuring web interactions with events and parameters.
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