IVARIO: Complex product data intelligently structured on Shopify

With the support of tante-e, IVARIO benefits from a sophisticated Shopify setup that clearly presents complex product data. The new store allows customers to transparently compare different test kits, thus reducing purchasing uncertainty.

IVARIO uses intelligent data structures to implement complex product comparisons and create transparency for customers.

To the shop

  • The initial situation

    IVARIO offers test kits and laboratory analyses in four main areas: water, soil, mold, and asbestos. Private individuals and service providers can purchase the test kits, take samples at home, and send them to the laboratory for detailed results and classifications. Target groups range from households with an urgent need for information to B2B customers such as dental practices, for whom water quality is particularly relevant.

  • The challenge

    IVARIO's product range includes numerous test kits with different test parameters. It was difficult for customers to identify which test covered exactly the required parameters. This led to purchasing uncertainty, increased support costs, and potential incorrect purchases. The company sought a solution that would create transparency and facilitate the purchasing decision without overwhelming users with excessive information.

  • The solution approach

    IVARIO turned to tante-e to develop a structured and user-friendly presentation of its complex product data. The tante-e team designed a sophisticated UX/storefront concept. The focus was on implementing an intelligent meta-object structure that efficiently manages product data and presents it consistently in the store.

The implementation

Intelligent metaobject system
The tante-e team set up four meta-object databases—one for each test group. These databases contain element groups, individual elements, and knowledge links to blog articles. Each product links to the corresponding database and contains only the parameters that the specific test actually checks.

Dynamic comparison tables
Customers can select products for comparison on the collection pages, which opens a dynamic comparison table. On the product pages, static comparison tables display fixed reference products from the same category, facilitating decision-making.

Informative product pages
The product pages present concise bullet USPs above the fold. Info drawers display the total test results and break them down by element groups. The mobile implementation was deliberately designed to be scroll- and swipe-friendly, with sticky elements for context.

Cross-sell and up-sell functionality
Tante-e's integrated cross-sell and up-sell functions in the shopping cart suggest complementary products and thus increase the average order value.

The implementation

01

Intelligent metaobject system

The tante-e team set up four meta-object databases—one for each test group. These databases contain element groups, individual elements, and knowledge links to blog articles. Each product links to the corresponding database and contains only the parameters that the specific test actually checks.

02

Dynamic comparison tables

Customers can select products for comparison on the collection pages, which opens a dynamic comparison table. On the product pages, static comparison tables display fixed reference products from the same category, facilitating decision-making.

03

Informative product pages

The product pages present concise bullet USPs above the fold. Info drawers display the total test results and break them down by element groups. The mobile implementation was deliberately designed to be scroll- and swipe-friendly, with sticky elements for context.

04

Cross-sell and up-sell functionality

Tante-e's integrated cross-sell and up-sell functions in the shopping cart suggest complementary products and thus increase the average order value.

The results

IVARIO now has a shop that presents complex product data in a transparent and user-friendly manner. Centralized data maintenance via meta objects ensures consistency and minimizes ongoing maintenance effort. Customers can more easily identify which test is suitable for their needs, reducing incorrect purchases and lowering support costs.

Lessons Learned

The collaboration between IVARIO and tante-e demonstrates the importance of a well-thought-out data model prior to implementation. Careful structural planning resulted in a future-proof solution that is easily expandable. The focus on decision-relevant data and the avoidance of manual processes through centralized data maintenance have proven particularly valuable. Early consideration of mobile usage also ensured a consistently good user experience across all devices.

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