Product recommendations are everywhere in ecommerce. You see them on homepages, product pages, cart pages and in follow-up emails.
But let’s be honest. Many still feel generic, repetitive or oddly disconnected from what the shopper actually needs. That matters because a weak recommendation can add noise to the journey, increase choice overload and make customers work harder to find the right product.
The strongest product recommendations are built on a deeper understanding of customers. They combine behaviour, product data and shopper intent to suggest products, bundles or next steps that fit the moment.
In this guide, we’ll break down what ecommerce product recommendations are, why they often fail, and how to build a more effective recommendation strategy that supports product discovery, conversion and customer loyalty.
What are product recommendations in ecommerce?
Product recommendations in ecommerce are personalised suggestions shown to shoppers based on their behaviour, preferences, purchase history, product data or shopping context. You’ll usually see them as sections such as “Recommended for you”, “You may also like”, “Frequently bought together” or “Complete the look” across different journeys. When done well, they help shoppers find relevant products faster and make the path to purchase feel easier, more useful and more personal.
Why do product recommendations matter for ecommerce brands?
Product recommendations help ecommerce brands turn existing traffic into more valuable shopping journeys.
When recommendations are relevant, they do more than fill space on a product page. They help shoppers find the right products faster, reduce friction caused by too many choices, and encourage them to add more relevant items to their baskets.
That impact can be significant. According to Salesforce Shopping Index data you collected, only 7% of shoppers click a product recommendation, yet those shoppers can generate 26% of total ecommerce revenue. At the same time, 54% of online browsers abandon their search when they struggle to find what they want quickly.
This is where strong product recommendations become more than a conversion tactic. Contextual suggestions, such as Frequently bought together or relevant bundle recommendations, can increase basket size by 68% and lift average order value by 28%.
For ecommerce brands, better product recommendations help shoppers move from browsing to buying with less effort, while giving brands more revenue from the traffic they already worked hard to attract.
Why do your ecommerce product recommendations fail?
If your store already has product recommendations but they aren’t effectively encouraging shoppers to complete a purchase, here are some reasons why they might not be performing as well as expected:
- Clicks do not always reveal intent: Browsing history can show what someone looked at, but it does not always explain why they are shopping today. A customer buying a gift, comparing options, or solving a specific need may all behave similarly, even though they need very different recommendations.
- Thin data leads to weaker matches: If your customer or product data is incomplete, your recommendations have very little to work with. Missing product attributes, unclear categories, limited preference data, or no purchase history can all make suggestions feel less relevant.
- More products can create more hesitation: A product carousel is only helpful if it simplifies decision-making. If shoppers are already comparing too many similar options, showing more products without clear guidance can increase decision fatigue rather than reduce it.
- Recommendations often sit outside the wider journey: A shopper might click on a product, answer a preference question, or show interest in a category, but that signal often disappears after the session. When product pages, cart prompts, emails and loyalty journeys do not work together, recommendations can feel fragmented rather than personal.
Common types of ecommerce product recommendations
Not every product recommendation works the same way. The right format depends on where the shopper is in the journey, the data you have, and the decision you want to help them make. Here are some common examples:
| Common Types of Ecommerce Product Recommendations | ||
|---|---|---|
| Product recommendation type | Best used for | Why it works |
| Best sellers and trending products | New visitors, homepage sections and category pages | Helps shoppers quickly spot popular products when you do not yet know much about their preferences. |
| Similar products | Product detail pages | Gives shoppers relevant alternatives when they like a product but want to compare colour, price, style, size or features. |
| Frequently bought together | Product pages, cart pages and checkout | Encourages useful add-ons, bundles or complementary products, helping brands increase basket value without forcing a hard sell. |
| Recently viewed products | Returning visitors and retargeting journeys | Makes it easier for shoppers to return to products they already considered, reducing the effort needed to continue their journey. |
| Personalised recommendations | Logged-in experiences, email journeys and returning shoppers | Uses customer behaviour, preferences or purchase history to suggest products that feel more relevant to each shopper. |
| Replenishment recommendations | Post-purchase emails, subscription journeys and loyalty campaigns | Works well for products customers buy again, such as skincare, supplements, pet food, beauty products or household essentials. |
| Preference-based recommendations | Product finders, quizzes and guided shopping journeys | Uses declared shopper preferences to recommend products, bundles or next steps that match what the customer actually needs. |
Where to use product recommendations across the ecommerce journey
1. Discovery stage: help shoppers find a starting point
At the discovery stage, shoppers may not yet know exactly what they want. Recommendations should help them explore your range without feeling overwhelmed.
You can use product recommendations on:
- Homepages to show best sellers, trending products or seasonal picks
- Category pages to highlight popular products, new arrivals or curated edits
- Product finder experiences to guide unsure shoppers towards relevant options
At this stage, the aim is to make product discovery feel easier and more inviting.
2. Consideration stage: help shoppers compare and decide
Once shoppers start browsing specific products, recommendations should help them narrow their choices and feel more confident.
You can use product recommendations on:
- Product detail pages to show similar products, alternatives or complementary items
- Comparison journeys to suggest products based on price, style, size or use case
- Guided recommendation flows to match shoppers with products based on their needs
This is where recommendations can reduce hesitation, especially when shoppers are comparing too many similar options.
3. Purchase and retention stage: help shoppers complete the journey
Product recommendations matter even after a shopper has shown clear purchase intent. At this stage, they can support basket value, repeat purchases and loyalty.
You can use product recommendations in:
- Cart pages to suggest bundles, add-ons or “frequently bought together” products
- Post-purchase emails to recommend refills, next-best products or care guides
- Loyalty and re-engagement journeys to bring customers back with more relevant product suggestions
Used well, recommendations can turn a single purchase into a more connected customer journey, helping brands drive higher order value and stronger repeat engagement.
What data do you need for effective product recommendations?
To suggest products that feel relevant, ecommerce brands need data that explains what shoppers looked at, what they care about and what they are trying to achieve. Here are some key metrics to help you build highly effective product recommendation strategies:
- Behavioural data: Pages viewed, products clicked, search terms, cart activity and browsing patterns
- Purchase history: What customers bought before, how often they buy and what they usually spend
- Product catalogue data: Product category, price, colour, size, ingredients, features, stock level and compatibility
- Shopper preferences: Style, budget, goals, product needs, favourite categories or preferred formats
- Shopping context: Whether someone is buying for themselves, buying a gift, comparing options or solving a specific problem
- Constraints and sensitivities: Size, allergies, skin type, dietary needs, ingredient preferences or price limits
- Business rules and merchandising data: Stock availability, margins, campaign priorities, bundles and product relationships
How to build an ecommerce product recommendation strategy
A strong ecommerce product recommendation strategy starts before you choose what to recommend. It starts with understanding where shoppers get stuck, what they need help deciding, and how your recommendations can move them to the next step.
For many ecommerce brands, one of the most practical ways to do this is through an interactive product recommendation pop-up. Here is the most comprehensive guide you can follow:
Step 1. Choose the moment when shoppers need help
Start by deciding where the recommendation experience should appear. A product recommendation pop-up works best when it supports a real decision moment, rather than interrupting the journey too early.
Good moments to test include:
- A shopper spends time on a category page but does not click a product
- A visitor scrolls through multiple similar products
- Someone shows exit intent before adding to cart
- A customer returns to the site but does not continue browsing
- A shopper lands from a paid campaign and needs a faster route to the right product
For example, a skincare brand might use a pop-up on a moisturiser category page that asks, “Need help finding your match?” A wine brand might trigger a short pairing quiz after someone has browsed several red wine products.
Step 2. Define what the recommendation should achieve
Before building the quiz, decide what action you want the shopper to take after receiving their result.
That could be:
- Visiting a specific product page
- Exploring a recommended product category
- Adding a bundle to cart
- Joining an email list in exchange for personalised results
- Saving their preferences for future recommendations
This step matters because the quiz logic should lead to a clear next action. If the result page gives shoppers too many options, it can create the same choice overload you were trying to solve.
Step 3. Ask for the data that changes the recommendation
A good product recommendation quiz should feel quick and useful. Avoid asking questions just because the data might be interesting later on.
Focus on answers that directly improve the recommendation. For example:
- Shopping goal: What are they trying to solve or achieve?
- Product preference: What style, flavour, colour, format or finish do they prefer?
- Budget range: What price point feels right?
- Use case: Are they buying for themselves, gifting, replenishing or comparing?
- Constraints: Do they have size, ingredient, dietary, skin type or compatibility needs?
For most pop-up recommendation journeys, 3 to 5 questions are usually enough. The shopper should feel they are getting closer to the right product with every click.
Step 4. Map answers to products, bundles or categories
Once you know what to ask, build simple recommendation logic.
Each answer should help filter or prioritise the final result. For example, a beauty brand might link “sensitive skin” to fragrance-free products, while a food and drink brand might link “dinner party” to sharing bundles or premium gift sets.
Your recommendation does not always need to send shoppers to a single product. Depending on your catalogue, the result could be:
- A single hero product
- A curated bundle
- A product category page
- A personalised collection
- A “best match” result with 2 to 3 options
The key is to make the result specific enough to be helpful, but not so narrow that shoppers feel boxed in.
Step 5. Build the pop-up experience with the right tools
To launch this kind of journey, you will usually need a few tools working together:
- An interactive quiz or gamification platform, such as Odicci, to build the pop-up, questions, logic and result experience
- Your ecommerce platform, such as Shopify or another CMS, to connect the journey to product pages, categories or add-to-cart actions
- Your CRM or ESP, such as Klaviyo, HubSpot, Braze or another email platform, to store preferences and use them in future campaigns
- Analytics tools, such as GA4 or your platform dashboard, to track starts, completions, clicks and conversions
At this stage, keep the setup focused. You do not need a complex recommendation engine to get started. A clear quiz flow, smart answer mapping and a useful results page can already create a more guided product discovery experience.
Step 6. Design the result page around action
The result is where the recommendation strategy becomes commercial.
Do not end the journey with a generic “Thank you” message. Show shoppers what to do next and why the recommendation fits.
A strong result page should include:
- A personalised headline, such as “Your best match”
- A short reason behind the recommendation
- Product image, name and key benefit
- A clear CTA, such as “Shop your match”, “Add to cart” or “Explore this collection”
- Optional supporting products or bundles, if they genuinely fit the result
This helps shoppers feel understood, while giving them a clear route from answer to action.
Step 7. Track what happens after the recommendation
Once the experience is live, measure whether it helps shoppers move forward. Tracking these metrics:
- Pop-up view rate
- Quiz start rate
- Completion rate
- Result page click-through rate
- Product page visits from results
- Add-to-cart rate
- Conversion rate
- Average order value
- Email opt-in rate
- Revenue influenced by the quiz journey
Look for both friction and wins. If many shoppers start but do not complete the quiz, it may be too long. If completion is strong but product clicks are weak, the result page may need a clearer CTA or a stronger product match.
Turn shopper intent into personalised product recommendations with Odicci
Ready to publish your first product recommendation journey but feeling overwhelmed? It’s hard, right? You need the right questions, the right logic, the right product mapping, and a results page that actually moves shoppers to the next step.
That is where Odicci can help.
Odicci is an interactive gamification platform that helps ecommerce, CRM, and loyalty teams turn static touchpoints into engaging product discovery journeys. Brands such as JD Sports, ASDA, Sephora and Ferrero use Odicci to create interactive experiences that capture shopper intent, increase participation and guide customers towards more relevant products, bundles or next-best actions.
With Odicci, you can build product recommendation journeys feel useful for shoppers and practical for your team:
- 100+ Ready-to-use templates for different campaign goals
- Modern UI and easy journey creation: Build a branded product-recommendation quiz without extensive development. You can create the questions, logic, and result experience in a smooth interface, with journeys that launch quickly.
- Data you can use beyond the first click: Capture preferences, needs and shopping intent, then connect those insights to your CRM, email or loyalty journeys for more relevant follow-up.
- Support from gamification experts: Work with a team that understands how to design interactive experiences that feel enjoyable, on-brand and commercially focused.
Ready to see what interactive product recommendations could look like for your brand?
Explore how leading brands use Odicci in our customer success stories, or book a demo to launch your first product recommendation journey.