Readers.com Boosts Revenue by +41% with Kimonix’s A/B Testing

Most Shopify brands sort collections manually based on best-sellers, newcomers, and sometimes even instinct.

But what happens when you let the right data do the merchandising instead?

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About Readers.com

Readers.com is a family-founded brand that set out to make reading glasses more affordable, accessible, and enjoyable. What began as a small team with a big mission has grown into a trusted online destination offering stylish, high-quality readers backed by fast shipping and friendly service.

The Challenge

With hundreds of products and a customer base spanning varied reading needs, the Readers.com team faced a growing challenge:

And while their merchandising was clear and user-friendly, it wasn’t always optimized for sales velocity or profit.

They also didn’t want to dive head-first into a new strategy without testing the strategy and automation effects against their current setup.

This was a job for Kimonix’s merchandising collection management, product sorting, and A/B testing features.

The Merchandising Strategy Test

With Kimonix, Readers.com ran two 30-day A/B tests, comparing collections that were:

  1. Managed by Kimonix’s profit-driven merchandising platform

  2. Manually curated by their team

Both versions received a nearly equal amount of traffic.

Therefore, the difference in performance is due to the upgraded merchandising strategy (i.e., automated product sorting) and not outside factors like better or more exposure.

The Results

After running the two A/B tests that compared Kimonix automated vs. manual merchandising, Readers.com found that Kimonix-managed product sorting and collections were substantially better. The Kimonix-managed and tested collections saw:

…than the manually managed collections.

With Kimonix, inventory health was more balanced. This meant:

In contrast, manual management showed a flat inventory health, where mostly best-sellers sold well, while other products stayed stagnant.

This can lead to excess inventory that ties up capital and increases risk.

Conclusion

So, what made the difference?

Kimonix collections factored in:

  • Profit margins

  • Inventory levels

  • Product freshness

  • Smart segmentation

Instead of relying on static lists or guesswork, Kimonix dynamically sorted collections using real-time store data, factoring in profit margins, inventory levels, product freshness, and shopper preferences and behavior.

This gave Readers.com the merchandising optimization they needed to drive smarter conversions, move more inventory, and increase revenue without increasing traffic.  

41%

Higher Revenue!

April 3
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