Question 1

What are product recommendations for eCommerce?

Accepted Answer

Product recommendations are the suggestions a store shows a visitor to help them reach a purchase faster: related items on a product page, a "customers also bought" row in the cart, a personalized block in an email. Traditionally they are generated by an algorithm reading click and purchase history, which means every visitor with a similar browsing pattern sees a similar list. The newer family is conversational: the shopper describes an intent in their own words and the recommendation is produced from that, live. Dialog's AI PDP Assistant belongs to this second family. It sits below the Add to Cart button, answers a shopper's actual question in real time, and recommends alternative products from your catalog if the one they are viewing does not fit their need.

Question 2

How do you implement product recommendations on an eCommerce website?

Accepted Answer

With a behavioral engine, implementation means installing a script, waiting for enough traffic to train the model, then placing widgets on the pages you want and maintaining the product relationships behind them. With Dialog you do not touch your product page templates at all. The widget connects to your catalog, your real-time stock and any PDF you upload, including staff training manuals, so it recommends with the same knowledge your best in-store advisor has. Setup takes under 2 days on a standard stack and under 30 minutes on Shopify Plus, and everything the agent knows beyond the catalog is edited from the back office rather than in code.

Question 3

What is the difference between personalization and recommendation?

Accepted Answer

Personalization adapts what a visitor sees based on who the system thinks they are: segment, past orders, browsing history, location. Recommendation is narrower, it picks which products to show. The two are usually bundled in the same tool, and both are inferences drawn from behavior. A conversational agent works from a third source that neither can reach: what the shopper says out loud. Someone who types "I am pregnant, can I use this?" has told you more in one sentence than a month of click data would.

Question 4

Do product recommendations actually increase conversion rate?

Accepted Answer

They do, though the size of the effect depends heavily on your vertical and on how the recommendation is delivered. Across 100 live Dialog clients (benchmark as of May 2026), conversion uplift ranges from about 3x to over 15x when comparing visitors who interact with the agent against those who do not: 13.65% versus 4.29% in food supplements, 9.94% versus 2.43% in cosmetics, 9.61% versus 1.63% in fashion. Note that these are segment comparisons, not a randomized A/B test, so part of the gap is self-selection: shoppers who engage were already more interested. The honest way to read it is that engaged shoppers convert far better, and that the lever you control is how many of them engage.

Question 5

How do you measure the revenue a recommendation engine generates?

Accepted Answer

Segment visitors who interacted with the agent against visitors who reached a product page and did not, then compare conversion rate and average order value between the two groups. Incremental revenue is (agent-user conversion rate − baseline conversion rate) × agent-user volume × average order value. The common mistake is subtracting the two conversion rates and applying the result to total site traffic, which inflates the number several times over. A time-period comparison, 30 days with the agent against 30 days without, is a reasonable second method.

Question 6

What does a recommendation engine cost?

Accepted Answer

Self-serve conversational tools publish their pricing and start low; Dialog has a free tier covering 200 conversations a month and paid plans from $249 a month, so you can test on your own traffic before committing. Enterprise personalization suites (Bloomreach, Dynamic Yield, Nosto, Constructor) are quote-based and typically land in the four- to five-figure monthly range. Book a 30-minute demo and we will walk through the numbers for a brand similar to yours → https://demo.askdialog.com/?lang=en

Smart 1-to-1 recommendations

Recommendations built on a conversation, not a click log

We connect your catalog

We encode how you advise

We recommend in context

One shopper, one recommendation

Cross-sell that makes sense

Recommendations that reduce returns

Works with any store

Powerful AI. Real Results.

Frequently asked questions.