Question 1

What is a product recommendation engine?

Accepted Answer

A product recommendation engine is software that decides which products to show a given visitor and where to show them: a related-items block on a product page, a cart upsell, a module in an email. Classic engines are algorithmic. They read behavioral signals (what this visitor viewed, what similar visitors bought, what is trending) and rank the catalog against them. Conversational engines work from a different input entirely: the shopper describes what they need and the engine answers that. Dialog is the second kind, an agent connected to your full catalog that asks what the shopper is trying to accomplish and recommends accordingly.

Question 2

What do recommendation engines actually do?

Accepted Answer

Three things, in order. They collect signals, from clicks, purchases, catalog attributes or, in a conversational engine, from what the shopper writes. They rank the catalog against those signals. And they render the result somewhere a shopper will see it. Most of the difference between tools is in the first and third steps rather than the ranking: what the engine is allowed to know, and whether the recommendation appears as a passive row of thumbnails or inside a conversation the shopper started.

Question 3

What is an example of a recommendation engine?

Accepted Answer

The familiar ones are behavioral: Amazon's "frequently bought together", Netflix's homepage, the "you may also like" block most Shopify themes ship with. In commerce specifically, the category covers Shopify's native Search & Discovery recommendations, personalization suites such as Nosto, Dynamic Yield and Bloomreach, and search-led platforms such as Algolia and Constructor. Dialog sits in a newer group of conversational engines, where the recommendation is the answer to a question rather than a block rendered before the shopper says anything.

Question 4

What is the difference between a recommendation engine and an AI shopping assistant?

Accepted Answer

A recommendation engine ranks products from past behavior and displays the result. An AI shopping assistant holds a conversation: it asks questions, understands intent, explains why one product suits and another does not, and can change its recommendation mid-conversation when the shopper adds a constraint. In practice the assistant also does things the engine cannot, like answering a question about pregnancy safety or compatibility, or recommending nothing at all when your catalog genuinely has no fit.

Question 5

Do AI product recommendations increase conversion rate?

Accepted Answer

In the Dialog benchmark of 100 live clients (May 2026), visitors who interact with the agent convert several times better than visitors who reach a product page and do not: 9.94% against 2.43% in cosmetics, 13.65% against 4.29% in food supplements, 11.84% against 1.84% in wellness. Average order value moves too, up 74% in jewelry and 52% in kitchenware. These are segment comparisons rather than randomized tests, so some of the gap comes from engaged shoppers being more motivated to begin with. Electrotodo ran a two-week controlled test and measured an 8.4% conversion uplift, which is the more conservative number to plan against.

Question 6

How much does a product recommendation engine cost?

Accepted Answer

It splits into two markets. Self-serve tools publish their pricing: Dialog has a free tier with 200 conversations a month so you can test on your own traffic, and paid plans from $249 a month with a defined 30-day POC and revenue targets tracked in your dashboard. Enterprise personalization and discovery suites do not publish pricing and are quote-based, typically four to five figures a month, with an implementation project attached. The question worth asking either way is what you are billed on, since visitors and conversations are forecastable while total pageviews are not.

Question 7

How long does it take to set up, and who has to do it?

Accepted Answer

On Shopify, under 30 minutes, and by whoever runs the store rather than a developer: install the app, catalog and stock sync on their own, then place the assistant. On other stacks it is generally under two days of light integration work. What takes real time is not the installation, it is deciding what you want the agent to say, and that is a copy exercise more than a technical one.

Question 8

Do we have to tag products or maintain recommendation rules?

Accepted Answer

No, and that maintenance is what sinks most engines. Classic tools need product relationships, tags and merchandising rules, so every catalog change becomes a task someone has to remember. Dialog treats the catalog as the source of truth: a product published today can be recommended today. What you want prioritized is written as an instruction in a prompt, not kept in a table of rules.

Question 9

Can we give it knowledge that is not in the catalog?

Accepted Answer

Yes, and the best deployments do. Beyond catalog and live stock, you can point the agent at your SEO articles, upload PDFs, and paste anything else into a dedicated metafield. The most effective single upload we see is the manual brands give new in-store staff. It is exactly the knowledge that makes a good advisor good, and it almost never made it onto a product page.

Question 10

How many products can it recommend at once?

Accepted Answer

In a conversation on a product page, one or two, because someone reading an answer does not want a grid. At the end of a guided flow, where the agent has collected a real brief, it can rank and show five to ten products by fit. The number is a design decision rather than a limit: more options after a diagnosis, fewer in the middle of a conversation.

The right product, every single time

From the first question to the recommendation

A recommendation engine that asks before it answers

We ingest everything you know

We encode your sales playbook

We advise, then recommend

Intent beats behavioral scoring

No rules to maintain

You find out what your pages are missing

Works with any store

Powerful AI. Real Results.

Frequently asked questions.

Explore other Dialog features

Further reading