Magical vs Offerly: full comparison of make-an-offer features, conversions, and pricing, find the best solution for your Shopify store.
Make an offer functionality has become a practical pricing tool for many Shopify merchants, especially those selling pre-owned items, collectibles, high-ticket products, or inventory with flexible pricing. Instead of relying only on discounts or coupons, merchants can allow customers to propose a price and decide how to respond.
Two Shopify apps commonly considered for this use case are Magical: Make an Offer and Offerly: Make an Offer Button!. Both apps solve the same core problem but approach it slightly differently in terms of flexibility, workflows, and scale.
This comparison focuses on how they work in practice, what types of merchants they tend to fit best, and where their limitations appear.
At a high level, both apps enable customers to submit price offers directly from the product page. Merchants can then accept, decline, or counter those offers manually or through predefined rules.
Where they differ is in how much control and structure they give the merchant.
Magical is built around configurable rules that can apply across products, collections, or storewide scenarios. Offerly, on the other hand, focuses on a simpler, more direct “make an offer” experience with fewer layers of configuration.
Neither approach is inherently better — the choice depends on how complex your pricing strategy is and how much automation you want.
Both apps follow Shopify’s standard installation flow and work with Online Store 2.0 themes through app embeds.
Offerly’s setup is intentionally lightweight. Merchants can enable the offer button quickly and start receiving offers with minimal configuration. This makes it appealing for smaller catalogs or stores where negotiation is occasional rather than constant.
Magical introduces more options during setup, especially around rules and conditions. While this requires a bit more time initially, it allows merchants to define how offers should behave across different products or price ranges. For stores with larger catalogs or varied pricing logic, this structure can reduce manual work later.
In short:
Both apps support manual review of offers as well as automated responses based on conditions.
Offerly allows merchants to automatically accept or decline offers based on percentage thresholds. This works well for straightforward pricing rules, such as “accept any offer above X% of list price.”
Magical supports similar automation but adds more granularity. Merchants can use both percentage-based and fixed-amount thresholds and apply them selectively to products or collections. This is useful when margins vary significantly across a catalog.
For stores receiving only a handful of offers per week, manual handling in either app is manageable. For stores receiving offers daily, automation becomes more important, and this is where rule depth starts to matter.
From the shopper’s perspective, both apps integrate directly into the product page and feel native when implemented correctly.
Offerly keeps the experience minimal and focused. Customers see a clear call to make an offer and follow a simple submission flow. This approach works well for products where negotiation is expected, such as vintage items, art, or one-of-a-kind goods.
Magical provides more display options, including different placements and ways to guide customer input. This can help steer offers closer to acceptable price ranges, which may reduce extreme lowball offers and improve efficiency.
The tradeoff is subtle:
When evaluating these apps for your Shopify store, cost structure and expected usage are important considerations alongside features.
Magical: Make an Offer uses a mix of subscription tiers and per-order commission fees:
Offerly: Make an Offer Button! also offers tiered pricing with plans suited to stores of different sizes:
Both apps include core negotiation features like auto-accept, auto-decline, and counteroffer options on all plans, and both provide free install/trial options so merchants can test without upfront commitment.
In practice, smaller stores that receive few offers might stay on free or low-cost plans with nominal commissions, while larger stores with frequent negotiation traffic may find value in the higher flat monthly tiers where commission fees drop or disappear entirely.
Magical has been on the Shopify App Store longer and is used across a wide range of store sizes. This makes it a common choice for merchants who want a more established tool with broader use cases.
Offerly is newer but has gained attention for its focused feature set and ease of use. It tends to appeal to merchants who want negotiation functionality without managing complex rules or configurations.
Neither app is inherently better, they are optimized for different merchant profiles.
While both apps enable negotiation, they are still rule-based systems. They respond to offers using predefined logic rather than adapting dynamically to customer behavior, inventory levels, or demand signals.
This means:
For many merchants, this is sufficient. For others, especially those operating at scale or with tight margins, it can become a constraint.
Some merchants exploring “make an offer” tools eventually look for more advanced negotiation logic, not just accepting or rejecting offers, but actively optimizing them.
This is where tools like BATNA come into the conversation. Unlike traditional make-an-offer apps, BATNA focuses on AI-driven negotiation, adjusting responses dynamically to protect margins, improve conversion rates, and introduce upsell opportunities during the negotiation flow itself.
It’s not a replacement that every store needs. But for merchants who find that static rules either leave money on the table or require constant tuning, AI-based negotiation can become a natural next step.
Magical and Offerly both solve the core problem of enabling price negotiation on Shopify, and they do it well. The right choice depends less on feature checklists and more on how your store operates:
As with any pricing feature, the best approach is to test on a subset of products, monitor margins closely, and expand only when the data supports it.
