Almost every successful social media website, online store, or content portal uses the power of ‘recommendations’ to increase page views, sales, and user engagement in general.
Understanding content and product recommendations
Recommendation technologies are built on the core of machine learning, data mining, and big data analytics. Recommendation technologies began with focus on content, helping users find text posts, images, videos, articles, search queries, books, news stories, and social tags relevant for them. Content recommendations slowly improved, and were fine tuned to take the form of product recommendations. Let’s understand both with an example each.
#1. Content Recommendations
NetFlix is the liveliest example of content recommendations. In 2006, Netflix Prize took the world by storm, with the company offering $1mn to anybody who could devise and design a recommendation system that could improve movie rating predictions on NetFlix by 10%. The winning team put together 107 algorithms to deliver an 8.43% improvement.
Over the years, Netflix has built upon the two best performing algorithms, and scaled them to handle the mammoth load of billions of user ratings on NetFlix. Today, users trust NetFlix’s recommendations to buy DVD’s, plan movie outings, and discover more multimedia content.
#2. Product recommendations
Amazon has often been cited as the most comprehensive example of how recommendation technology can be leveraged for revenue maximization. Amazon uses personalized recommendations (based on user’s past behavior), social recommendations (based on past behaviors of similar users), and item recommendation (recommendations based on item). Check out New Releases, Related Items, and Others Purchased blocks to see how these 3 recommendation approaches have been used by Amazon.
Opportunities and Challenges
Studies show how at least 31% of e-commerce revenue was generated by personalized recommendations alone, in the fourth quarter of the last year. The Click-Through rates of personalized “Top Seller” lists, the visitor conversion rates of personalized websites, all of them share the same story: choosing a recommendation service to work within your website can be the difference between a hugely successful business, and just a moderate one. So what are the things you should look out for when selecting the right recommendation service? We’re here to help!
Recommendations based on Social Media Sharing/Integration
Today, the best recommendation services out there are leveraging social network media which effectively act as a promotional platform. While on-line ads are easily available, a simple Facebook message circulated (shared) by multiple users can be a great way of getting feedback on what your customer desires, and also help attract a wider but connected audience. There are two ways of using social media: a) sharing posts, so that you can tell which products are popular with which customers, and b) offering social media log-in options which gives you access to basic details of your customers, and also tap into what they are looking at.
Recommendations based on user uploaded content
Apps and websites including “Delectable” offer users the chance of uploading their own photographs, tagging and saving the kind of products they like. There are recommendation services which can then make use of photo-recognition algorithms to find matching products, generating personalized results which suit the tastes of your customers. Simple, but very effective. So check if your recommendation service provider can deliver this awesome feature.
Services offering personalize-able Profiles for Individual Users
Netflix is one company that benefits largely from the use of personal profiles. Because families do not often share the same tastes, there are individual accounts for individual members and each user can express their own individual tastes.
A good recommendation algorithm can then make use of a user’s data, including the kind of products they generally subscribe to, or buy, and provide adequate suggestions from the same category. Moreover, users with similar tastes often choose to browse similar things; when customer profiles match, a “What other customers are looking at” feature comes into handy. Look for services that allow you to offer such personalized fields for your customers to enhance their shopping experience.
Recommendation Algorithms that can adapt to Specific Needs
Different businesses have different models of business, because of which recommendation services cannot be generic. Services such as Softcube.com can offer users high performance recommendation models adapted to your specific needs and industry. It can work with very small amounts of data, and still provide users with the kind of personal touch you’d expect from a store assistant, fulfilling the needs of both users and retailers. Your website markup is what determines the customized recommendation block, so check for recommendation services that use JavaScript that work best at performing this task efficiently.
Engines utilizing Innovative Content Analysis Technology
Recommendation engines often make use of the content analysis technology to provide suggestions. Just think of Google suggestions, and you’ll get the gist. They generally work on the basis of finding similar images from the database on your website, matching what the user’s looking at with other products explored by similar users. Such analysis technology has been shown to improve sales by at least 20%, so this is definitely one of the must-have features of any recommendation service.
Recommendation Services using Collaborative Filtering Options
Collaborative filtering works in this way: if 50 customers look at both tan boots and tan handbags, then recommendation services will make use of that data and show customers browsing for boots, handbags, and vice versa.
For e-retail websites, this has huge benefits in terms of user convenience. This falls under the “What Customers Ultimately Buy” category, which statistically has huge implications for your business. It works a little like attuning your customers to think like other people with similar tastes do, but it has the potential of increasing your business manifold. In a recent study, in fact, this was the most compelling of 20+ recommendation categories shown to the study group.
Service Providers offering In-depth Analysis
This is an added bonus of employing a recommendation service. By monitoring user behavior and tabulating it in the form of bars and charts, this aspect of a recommendation service can help you sell better. Not only do you get to measure revenue boosts with the recommendation KPIs, you can get reports on the number of recommendations shown, number of clicks on the recommendation, total revenue generated through recommendations, etc.
Some services even offer trial runs, which can be a great idea for startups at least. Add this to your checklist for better results.
In conclusion, we can sum up the necessary features of the recommendation services as follows: they should be easy to implement, simple to install, compatible with a wide range of platforms and businesses, have social media integration, and should be able to analyze data for the betterment of sales. Whether you manage a content portal, or an e-store, you can leverage recommendation for more success. Make sure you extract more revenues out of your existing customer base, and more subscriptions to your content blogs, with a professional recommendation service.
Images: “recommended top quality product review recommendation for best choice optimal solution/Shutterstock.com“
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