Why shoppers miss the right products on your ecommerce store

A shopper finds a pair of running shoes on your website. The shoes look right, but they also need socks. They might want to compare another pair. If neither option is easy to find, they may buy just the shoes or leave to keep looking.

Now multiply that small moment across thousands of visits. Your store may have the products people want, but shoppers do not always see them at the right time.

Product recommendations help close that gap. They put relevant items beside the product a shopper is viewing, inside the cart or wherever the next choice feels natural. AI can make those suggestions more relevant as it picks up signals from products and shopping behaviour.

What are AI product recommendations?

AI product recommendations are suggestions that help shoppers find products they may want to view or buy. They can use signals such as the item on the page, product categories, previous views, cart activity and past purchases. The suggestions might change as the shopper moves through the store.

Think of a good salesperson in a physical shop. If someone picks up a jacket, the salesperson might show them another size, a similar style or a shirt that goes with it. Online recommendations serve a similar purpose when the suggestions are useful and the products are available.

You have probably seen them under labels such as “Similar products,” “Complete the look” or “Frequently bought together.” The label matters less than the question it answers for the shopper.

Why shoppers need help finding the next product

An ecommerce catalogue gives customers a lot of choice. That is helpful until finding the right item starts to feel like work. A shopper may not know your category names, notice a product buried several pages deep or think to search for an accessory they need.

Here are three common moments:

• They want another option. The colour is right, but the size is sold out. A similar product gives them a way forward.
• They have forgotten a useful extra. Someone buying a phone may also need a case. Showing a compatible one makes the next step easier.
• They are building a complete purchase. A shopper buying a kurta may want matching bottoms, but might not know where to find them in a large catalogue.

The goal is to help people finish the purchase they came to make, including related items when they genuinely fit.

Where recommendations can help

Different pages call for different suggestions. Showing the same popular products everywhere can miss what the shopper needs in that moment.

These suggestions do not all have to be personal to one shopper. A first-time visitor may see useful recommendations based on the current product or what is popular in a category. As the visitor browses, the store has more context to work with.

How can recommendations affect average order value?

Average order value is the total value of orders divided by the number of orders. If your store receives ₹10,000 from 20 orders, its average order value is ₹500.

Relevant recommendations may increase that number when a shopper adds a useful item to an order they were already planning to place. A matching belt with trousers or a refill with a skincare product can make the basket more complete.

That does not mean every recommendation should push a higher-priced item. If a customer sees unrelated or unavailable products, the suggestions add clutter. A smaller, relevant recommendation can be more helpful than a large carousel that asks the shopper to start their search again.

What makes a recommendation useful?

Start with the shopper’s immediate need. The best suggestion often answers one of these simple questions:

1. Is there another version of this product that suits me better?
2. What goes with the product I chose?
3. Is there something I need to use or maintain it?
4. Where is the item I was considering earlier?

Product details matter too. A recommendation should fit the context and reflect what is actually in stock. A phone case for the wrong model is not helpful, even if customers often buy phone cases with phones.

Brands also need room to guide what appears. A fashion retailer may want to show pieces from the same collection. A beauty brand may need to avoid pairing products that are a poor match for a particular routine. AI can spot patterns, while the people who know the catalogue can set sensible boundaries.

Start by fixing one shopping moment

You do not need to put recommendations on every page at once. Look for a place where shoppers regularly need help making the next choice.

If they reach a sold-out product, show available alternatives. If they add a main item to the cart, show one or two useful additions. If shoppers return often, make it easy to find products they viewed before.

Then look at what happens. Do shoppers click the suggestions? Do they add the recommended products to the cart? Do completed orders include useful additions? Look at average order value alongside conversion, so a larger basket does not hide a harder buying journey.

Product recommendations work best when they feel like help, not another sales pitch. Make the next relevant product easier to find, and give shoppers a clearer path from browsing to buying.

For teams ready to compare tools and features, read AI product recommendation software buyer’s guide.