Cold Start Recommendations: How to Fix Them on Shopify

Cold start is what happens when a recommendation engine has nothing to go on: a visitor with no history, or a product with no clicks or sales. On a Shopify store you fix it by layering signals. Start with product data (titles, attributes, collections, images), add curated and popular picks for first-time visitors, and let behavioral models take over only once real interactions exist. This guide covers what Shopify does for you by default, where it falls short, and a practical plan you can run this month.

TL;DR

• Cold start comes in three forms: a new user, a new item, and a new store or community. Each needs a different fix.
• Shopify’s built-in related products fall back from purchase history to product descriptions to related collections. That fallback is simple mapping, not magic.
• Use non-behavioral signals (attributes, taxonomy, semantic content) to give new products a head start, then hand over to behavior as data builds.
• Curate your top 20 SKUs by hand, show popular items to first-time visitors, and balance proven sellers with new arrivals.
• Cold start never fully ends. Seasons, trends and shifting intent keep cooling items down, so treat it as ongoing work.

Table of Contents

• What cold start actually means in a store
• Why the vicious circle is worse than it looks
• It is not a one-time event
• What Shopify does by default
• A staged plan for cold start recommendations
• Solving the new product problem
• Search is a cold start surface too
• Perspective: what we see in Shopify catalogs
• How to measure whether it is working
• Mistakes that keep stores cold
• A four-week action plan
• Where Indexa fits
• FAQ
• Sources

What cold start actually means in a store

The cold start problem) is a recommender system’s inability to make accurate predictions for new users or items because it lacks historical interaction data such as clicks, ratings or purchases. It appears in three forms:

Most merchants only notice the first one, because “personalization” is usually pitched at returning customers. But the second is the one that quietly costs money. A new product that never gets shown never gets clicked, and a product that never gets clicked never earns the data that would get it shown.

Why the vicious circle is worse than it looks

Relying only on clicks and post-purchase data disadvantages new products by design: low visibility produces low interaction, which produces even lower visibility. The same research warns about popularity bias. If you optimize only for short-term engagement and recommend whatever already sells, discovery is suppressed and new items never get the traction they need.

The fix the authors describe is to balance exploration (showing new or cold items) with exploitation (showing proven hits). In a large-scale e-commerce A/B test covering 50 million queries, a hybrid approach using Empirical Bayes modeling improved new product impressions by 13.53% and new product purchases by 11.14%. That test was run on a marketplace-scale product search, so do not expect those exact figures on a small store. The direction is what matters: give new items a deliberate, measured boost.

It is not a one-time event

Many merchants treat cold start as a launch problem. It is not. Researchers describe a continuous cold start problem in e-commerce: items and users “cool down” through seasonal shifts, changing fashion trends or changes in shopper intent. A wool coat that sold well in January has almost no signal in July. A returning customer who bought a wedding gift is not telling you anything about what they want next.

This matters for fashion and specialty retailers in particular. Your catalog turns over by the season, so a meaningful share of your products is always cold. When recommendations feel stale or random, the cause is usually lifecycle management, not a broken app.

What Shopify does by default

Before adding anything, know what you already have. Shopify’s documentation says its related recommendations follow a fallback chain. It uses purchase history first, then product description analysis (English stores), and finally related collections when neither behavioral nor description data is available.

Two details are worth underlining:

1. Only related recommendations are auto-generated. Complementary recommendations (the “goes well with” kind) generally need manual setup or an app. The Storefront API exposes both through an intent argument.
2. The collection fallback is plain mapping. Practitioners at one Shopify agency point out that merchants often assume an AI model is working when it is just collection mapping. If your collections are messy, your cold start recommendations are messy.

That last point is the cheapest win available. Before you buy anything, open your five biggest collections and ask whether the products in each one would honestly substitute for each other.

A staged plan for cold start recommendations

Personalization is a sequence of escalating specificity, not one algorithm. One Shopify personalization guide lays out the idea: treat visitors with no history with popular and trending products, then move to collaborative filtering after a threshold of interactions, for example 3 to 5 page views. Treat that threshold as a starting point to test, not a law. If you want a broader view of this staged thinking, this piece on staged personalization for new visitors covers the logic for people with little or no history.

Here is a version adapted for a Shopify store.

Stage 1: A visitor with zero history

You know almost nothing, but you do know the page they landed on, the referral source and the device. Use those.

• On product pages, show related items based on the current product, not the person. Shopify already does this.
• On the home page and empty cart, show best sellers and new arrivals, clearly labeled.
• Skip “Recommended for you” wording. You have not earned it yet.

There is also a trust angle. Industry practitioners warn that over-personalization can feel creepy and make shoppers disengage. They suggest framing picks as curated, such as “our picks”, rather than algorithmic, and leaning on first-party behavior over third-party tracking. For a first-time visitor, “Staff favorites” is both more honest and more effective than a pretend-personal label.

Stage 2: A visitor with a few signals

After a handful of views, the session itself becomes data. A shopper who viewed three linen shirts has told you something. Shift the home page and category sorting toward that category, price band and size range. Keep one slot for something outside the pattern, so the session does not narrow too quickly.

Stage 3: A known customer

Purchase history now works, and it is the strongest signal Shopify itself uses. This is where complementary recommendations pay off: the belt that matches the shoes, the care kit for the leather bag. Our guide on quick adds that improve cross sell recommendations shows how to set up two or three of them in a week.

Solving the new product problem

The new item is where you can do the most good, because you control the inputs. The research is clear that practitioners succeed by using product attributes, taxonomy and semantic content as a prior that boosts new items until behavior accumulates. In plain terms: describe the product so well that the system can place it before anyone has bought it.

Fix the product data first

• Titles that say what it is. “The Marlowe” tells a model nothing. “Marlowe Wool Overcoat, Charcoal, Women’s” tells it everything.
• Consistent attributes. Material, fit, occasion, season and color should use the same vocabulary across the catalog. “Navy”, “midnight” and “dark blue” in three products split your signal.
• Clean collections. New products should land in every collection where a shopper would look for them on day one.
• Real descriptions. Shopify’s description-based fallback applies to English storefronts, so thin copy directly weakens the fallback.

This ties tightly to search. If shoppers type a phrase and your catalog uses different words, new products stay invisible in search as well. A synonym strategy that fixes zero results and our notes on fixing search relevance data first are good next reads.

Give new arrivals a guaranteed slot

Do not wait for the algorithm. Reserve a small, fixed share of exposure for new products: one or two positions in the first row of a collection, a “Just in” strip on the home page, a new-arrival badge. Then watch whether those products earn clicks. If a new product gets impressions and no clicks, you have learned something real about the product, its image or its price. If it gets neither, you have a visibility problem.

Merchandise your top 20 by hand

One practical recommendation from Shopify practitioners is that effective cold start management means manually merchandising your top 20 SKUs rather than relying on automatic discovery alone. For each of those products, pick the three to five items you would suggest if a customer stood in front of you. Include at least one newer item. Revisit the list monthly.

Search is a cold start surface too

A new visitor often has no browsing history, but they do have a query. Search is the one place where a shopper tells you exactly what they want, and it works from the first second. Research on cold start in product search treats it as a problem in its own right, separate from recommendation, because new products need to rank for queries before they have sales.

On Shopify this shows up in familiar ways:

• A shopper misspells a brand or product name and sees nothing. See our guide to fixing fuzzy search misses.
• A shopper uses natural language (“something to wear to a beach wedding”) and keyword matching finds nothing. Here, semantic search matters.
• A product is out of stock and the results page looks empty. We cover that in fixing out of stock search.

For a new visitor, a good search experience is the best personalization you can offer, because it answers the question they actually asked.

Perspective: what we see in Shopify catalogs

We work on search and discovery for Shopify stores, and the pattern repeats. Merchants ask about “AI recommendations” when the real issue is upstream: inconsistent attributes, collections built for the navigation menu rather than for shoppers, and new products that launch with a title, one photo and two lines of copy.

Our view is that cold start is mostly a data and merchandising discipline, and the algorithm is the last ten percent. A system can only place a new product as well as it understands it. That is why our managed service starts from your own store data, tunes relevance continuously, and gives merchants a no-code console and a visual merchandising grid, so a human can pin, boost or reorder products the moment a launch needs it. We think the best results come from combining a model with a merchant’s own judgment, not from replacing one with the other.

We also push back on one habit: measuring recommendations only by what they already sell. If every widget is judged by click-through on proven bestsellers, your system will learn to show bestsellers forever. Give new products a protected, measured share and judge them on a different yardstick.

— Barikreativa

How to measure whether it is working

Pick a small set of numbers and review them weekly for a month.

1. New product impression share. What percentage of recommendation and collection impressions go to products under 30 days old?
2. New product click-through and add-to-cart rate. Compare against your catalog average, not against bestsellers.
3. Days to first sale. How long does a new SKU take to sell once? This is the cleanest cold start metric a store owner can track without extra tools.
4. Zero-result and low-result search rate. If new products are invisible in search, this number shows it. Our piece on ecommerce search metrics explains which ones to watch.
5. Revenue from recommendation widgets, split by new versus established products.

An important caution on interpretation: a short test on a small store gives noisy results. Run each change for at least a couple of full weeks, change one thing at a time, and do not declare a winner from a handful of orders.

Mistakes that keep stores cold

• Treating the collection fallback as AI. If it is only mapping, fix the collections.
• Labeling everything “recommended for you”. For anonymous visitors this feels false. Use “best sellers” or “our picks”.
• Letting bestsellers crowd every slot. That is popularity bias in practice.
• Ignoring the lifecycle. Seasonal items cool down. Rotate them out of prominent slots and bring them back with the season.
• Adding widgets that slow the page. Recommendation blocks that load late shift the layout and frustrate shoppers. Developers can read how to avoid layout shift in product recommendations and lazy load related products.

A four-week action plan

Week 1: audit the inputs. Review titles, attributes and collections for your 100 newest products. Fix the worst offenders. Run a free Shopify search test to see how your store handles real queries.

Week 2: set the structure. Hand-merchandise the top 20 SKUs, add a new arrivals strip, and rename anonymous-visitor widgets to something curated.

Week 3: add complementary logic. Set up cross-sell pairs for the products that carry most of your revenue.

Week 4: measure and adjust. Compare days to first sale and new product click-through against the weeks before. Keep what moved the numbers.

Where Indexa fits

If you would rather not run all of this yourself, Indexa is a fully managed AI search and discovery service for Shopify, with typo-tolerant and semantic search, recommendations, synonym mapping and a visual merchandising grid. There is no setup work on your side, and it keeps tuning relevance on your real store data. You can start with a free audit to see where search and discovery are leaking sales, or look at how the 30-day pilot, setup fee and monthly retainer work.

FAQ

What is cold start in a recommender system?

It is the situation where a system cannot make reliable recommendations because it has too little interaction data about a user or an item. It can affect new visitors, new products or a whole new platform. The usual remedy is to start from non-behavioral signals such as product attributes and popularity, then shift to behavior as data builds.

How does Shopify handle recommendations for new products?

Shopify generates related recommendations from purchase history first, then product descriptions on English storefronts, and finally related collections. A brand-new product with no sales therefore leans on its description and collections. Complementary recommendations generally need manual setup or an app.

How many page views before I can personalize?

There is no universal number. One Shopify personalization guide suggests moving to behavior-based recommendations after roughly 3 to 5 page views, which is a reasonable place to begin testing. Compare it against your own click and conversion data before committing.

Is cold start a one-time problem?

No. Products and shoppers cool down over time because of seasons, fashion cycles and changing intent. Plan for it as an ongoing merchandising task, with a regular review of which products are getting too little exposure.

Sources

• Cold start (recommender systems), Wikipedia)
• Addressing Cold Start in Product Search via Empirical Bayes
• The Continuous Cold Start Problem in e-Commerce Recommender Systems
• Show related products on product pages, Shopify developer docs
• productRecommendations query, Shopify Storefront API
• Ecommerce Personalization on Shopify: What Works, BLKDG
• Personalization on Shopify: Product Recommendations, Dynamic Content, and More

Recommended

• Fix Shopify No Results: UX, Search & Discovery, or Managed Search
• Measure First, Fix Fast: Shopify Search Speed and Relevance
• Free Shopify Search Audit Checklist
• Developers: Avoid Layout Shift in Shopify Product Recommendations