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A recommendation system is an artificial intelligence or AI algorithm, usually associated with machine learning, that uses Big Data to suggest or recommend additional products to consumers. These can be based on various criteria, including past purchases, search history, demographic information, and other factors. Recommender systems are highly useful as they help users discover products and services they might otherwise have not found on their own.
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Recommender systems are trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions. These include impressions, clicks, likes, and purchases. Because of their capability to predict consumer interests and desires on a highly personalized level, recommender systems are a favorite with content and product providers. They can drive consumers to just about any product or service that interests them, from books to videos to health classes to clothing.
- In the product discovery phase, you can use more than one tool to show the consumer the most suitable options, offer the right personalized products, and design a satisfying experience. Product recommendations are one of the most powerful of these tools.
- Recommendations for related products:
- Provides a list of related products that are similar to the chosen one, either in use or in price which creates a cross-selling opportunity. For example at the cart step, offering complementary products to the ones already added to the cart, you can encourage your customer to shop a little bit more.
- Recommendations based on past purchases:
- If you already bought a digital camera, a recommendation of its lenses might make sense.
- Recommendations based on search:
- Product recommendation engines may look at search history to suggest products based on terms consumers have used.
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Collaborative filtering algorithms recommend items (this is the filtering part) based on preference information from many users (this is the collaborative part).
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Content filtering, by contrast, uses the attributes or features of an item (this is the content part) to recommend other items similar to the user’s preferences.
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Hybrid recommender systems combine the advantages of the types above to create a more comprehensive recommending system.
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Context filtering includes users’ contextual information in the recommendation process.
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Drive Traffic
- Through personalized email messages and targeted blasts, a recommendation engine can encourage elevated amounts of traffic to your site, thus increasing the opportunity to scoop up more data to further enrich a customer profile.
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Deliver Relevant Content
- By analyzing the customer’s current site usage and previous browsing history, a recommendation engine can deliver relevant product recommendations as he or she shops based on said profile. The data is collected in real time so the software can react as shopping habits change on the fly.
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Engage Shoppers
- Shoppers become more engaged when personalized product recommendations are made to them across the customer journey. Through individualized product recs, customers are able to delve more deeply into your product line without having to dive into (and very likely get lost in) an ecommerce rabbit hole.
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Convert Shoppers to Customers
- Converting shoppers into customers takes a special touch. Personalized interactions from a recommendation engine show your customer that he or she is valued as an individual, in turn, engendering long-term loyalty.
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Increase Average Order Value
- Average order values typically go up when an engine is leveraged to display personalized options as shoppers are more willing to spend generously on items they thoroughly covet.
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Increase Number of Items per Order
- In addition to the average order value rising, the number of items per order also typically rises when an engine is employed. When the customer is shown options that meet his or her interest, they are far more likely to add items to to their active purchase cart.
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Control Merchandising and Inventory Rules
- A recommendation engine can add your marketing and inventory control directives to a customer’s profile to feature products that are on clearance or overstocked so as to avoid unnecessary shopping friction and tone deafness.
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Reduce Workload and Overhead
- The volume of data required to create a personal shopping experience for each customer is usually far too large to be managed manually. Using an engine automates this process, easing the workload for your IT staff.
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A Recommendation Engine Provides Reports
- Detailed reports are an integral part of a personalization system. Accurate and up-to-the-minute reporting will allow you to make informed decisions about the direction of a campaign or the structure of a product page.
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Offer Advice and Direction
- An experienced recommendation provider like Kibo can offer advice on how to use the data collected from your recommendation engine. Acting as a partner and a consultant, the provider should have the industry know-how needed to help guide you and your ecommerce site to a prosperous future.