What's recommendation systems in eCommerce?
Recommendation engine – the brain of your store
Thousands of products. Multiple channels like app, in-store, and platform. How do you know what every customer wants? That’s the job of a recommendation engine. Like how YouTube recommends videos, a recommendation engine suggests products to users, curating their page with products they are likely to buy, based on browsing activity, products in cart, past purchases, and more.
Difference between static web pages and personalized recommendations
From wild guesses to recommendations that gain clicks & purchases
| One, non-personalized view for all customers | Personalized pages with right products for each customer |
|---|---|
❌ No cross-selling and up-selling opportunities. Customers don’t spot products they want unless they search. | ✅ Automatically shows ‘frequently bought together’ or ‘complete the look with’ options. High order value from one customer. |
❌ Static sessions that don’t change as per user browsing behavior. | ✅ Dynamic, personalized content curated depending on user preferences (without them telling you). |
❌ Scaling only leads to discoverability issues; customers get lost among products and categories. | ✅ No more overlooked products. Pages optimized with products that match user interests, even with tons of datasets. |
❌ Generic marketing campaigns that don’t lead decent ROIs. | ✅ Highly targeted campaigns that lead to higher returns. |
❌ High bounce rates and low profitability. | ✅ Users spend more time, as they are shown products likely to click and engage with. |
One, non-personalized view for all customers
❌ No cross-selling and up-selling opportunities. Customers don’t spot products they want unless they search.
❌ Static sessions that don’t change as per user browsing behavior.
❌ Scaling only leads to discoverability issues; customers get lost among products and categories.
❌ Generic marketing campaigns that don’t lead decent ROIs.
❌ High bounce rates and low profitability.
Personalized pages with right products for each customer
✅ Automatically shows ‘frequently bought together’ or ‘complete the look with’ options. High order value from one customer.
✅ Dynamic, personalized content curated depending on user preferences (without them telling you).
✅ No more overlooked products. Pages optimized with products that match user interests, even with tons of datasets.
✅ Highly targeted campaigns that lead to higher returns.
✅ Users spend more time, as they are shown products likely to click and engage with.
Why does your eCommerce platform need recommendation system?
Recommendation system & its benefits
To personalize marketing campaigns
Make user experience better
More clicks & engagement
More conversion rates
Revenue growth
Benefits that are trackable
From clicks to suggestions, what happens behind the recommendation engine?
Collaborative filtering systems
The type of recommendation system that recommends items based on relationships between user-user or item-item. For example, if user A and B has similar shopping patterns, they would like same products too. Pros: ✅ Doesn’t require detailed product metadata. Cons: ❌ There might be irrelevant recommendations.
Content-based filtering systems
This type of filtering system match user’s past purchase preferences to product attributes, answering to the question - what feature or attribute the user likes/prefers. For example, a user interested in vegan skincare products, is recommended similar vegan and cruelty free brands. Pros: ✅ Good when there is less user preference data. Cons: ❌ Similar and repeated recommendations.
Hybrid recommendation systems
Hybrid recommendation system is a mix of two powerful yet contrasting recommendation filtering: content-based & collaborative filtering. Hence, the results are more accurate and comprehensive, as it blends scores of both easily. A user who interacts with a fitness brand gets recommendations on products from same brand & similar fitness products. Pros: ✅ No need to fear cold start & data sparsity issues. Cons: ❌ Needs more computational power.
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Clear answers to your complex questions
What is the best algorithm for recommendation engine?
How to use AI to recommend products?
What are the methods of recommendation engine?
How to build a recommendation engine with AI?
Recommendation system and its applications across industries
Retail and eCommerce
Retail industry can use personalization to create product pages, personalized homepage or app experiences, email campaigns. This shows users ‘frequently bought together’, ‘you might also like’, ‘recently viewed’. Personalization benefits the retail by increasing first session engagement, email conversion rates, AOV, and customer retention.
Media and entertainment
Media and entertainment industry can apply personalization to drive content and genre-based recommendations for OTT, music apps, and tv programs schedules. It allows user to explore more content in the form of ‘Trending in your region’, curated lists, recommendation rows, etc. By using personalization, OTT apps and media can reduce retention rates, more subscription and long watch hours. Less churn as users discover more content to watch.
Travel & hospitality
Travel and hotel industry can use personalization to create dynamic packages, loyalty-based offers, personalized upgrades, next activity planner based on hotel booking, and more. This shares useful suggestions on ‘popular destination this season’, ‘you may also like', and preference-based content. This increases repeat bookings, loyalty plans, increase trip per revenue, simplifies booking flow, and expands CLV (Customer Lifetime value).
BFSI
Finance and insurance industry can use personalization to employ robo advisors, investment advisory dashboards, in-app tips, and email campaigns. This way, they can auto-suggest eligible finance products, stocks, and portfolio aligned to risk profile. Intimate possible fluctuations in offerings they might be interested in. This helps finance institutions build trust and promote finance literacy. Improves wallet share and product penetration.