Gain insight into the most talked about topics related to AI and their Marketability
Remember that festive season when you were in need to buy a cake oven because your friends would be coming to your place for a Christmas party!! Then you logged on e-Bay on your mobile phone and that oven was popping up on the home screen. You immediately clicked on the Buy Now option, got it in a few days and served your friends with your signature cake on Christmas.
But have you ever thought how did that perfect product walk to your screen at that particular time of the year! Well, its the power of a recommendation engine that aptly recommends the ideal product at the ideal time.
So, Time-based recommendation system has been instrumental in the e-commerce business. It not only helps in the discovery of products but also by showcasing the seasonal/timely products on your screen, greatly reduces the average time spent by a user for purchase. In this blog, you will have a closer look at the Time-Based Recommendation and how you can thrive on it to get the maximum of your site traffic. Read on and share your comments below.
What is a Time-Based Recommendation Engine?
Simply put, a time-based recommendation is just another module of the traditional recommender system but more powerful in terms of identifying the preference of visitors at a particular time. Here the effort is at faster conversion and quicker purchase. And the good part is the recommendations only get svelte over time by virtue of machine learning.
While personalized recommendations are inherent with any recommendation engine, for the time-based recommendation, the data source is gathered by combining the maximum user search for products during a particular period of time or for the matter of a month or a week, with the inclusion of individual preference gathered from their previous purchase/search history.
Streaming platform owners and Time-based Recommendations
Digital content consumption is of late the hotcake among streaming platform owners and everybody is working hard, seeking the help of data scientists and spending millions to decipher the content consumption habits. The importance of content browsing and consumption habit holds a lot of importance especially in the age of live streaming and an era where billions of dollars are invested only for content production. To make them discoverable, relatable and then recommending them as per user interest is an uphill task less traveled by commoners considering the time and resource it demands. That’s why online store owners often resort to frivolous recommender systems available in the market whose credibility is largely questionable.
Time-based Recommendations across OTT Platforms
Alie, an AI-based recommender system deploys machine learning algorithm to recommend content to individual users with impeccable accuracy. The accuracy it hits is also due to the deep learning involved in the recommendation process that takes into account several factors out of which time-based recommendation is one.
While browsing pattern and content consumption habits are yet to be fully decoded by streaming platform owners, there are a lot of obvious use cases of time-based recommendation. For example, spiritual podcast and devotional content are often recommended by music streaming services in the morning hours. Although recommendations are curated by user persona and individual preferences there is some universal thought process involved in picking content at a particular time. For example, an Eminem song you would often find on your playlist while returning home after a hectic day. Similarly, Netflix’s binge-worthy series often adorn your recommendation list on weekend. Because on weekdays it is highly unlikely for a user to binge-watch an eight-episode series at one go.
Alie and Time-based Recommendation on e-commerce sites:
Are you recommending smartwatches, office stationeries, formal attires or lunch boxes on your e-commerce sites during regular office hours? If not, then you are losing out a lot of sales. Because this is where time-based recommendation come to the scene and steal the deal in your favor. Research says people aged 25-40, take suggestions from colleagues and friends while ordering office attire or some funky collections for the weekend party. Now, you can understand, when you open Amazon on your phone in the morning, why it shows ads of a health drink or gymming equipment or a new organic green tea for that matter. Machines understand that the idea of leading a healthy lifestyle starts with workout and healthy consuming habits.
[Alie dedicates a whole module for the time-based recommendation. Take a look at Alie Features.]
Make the most of your every visitors especially in the festive season or special occasions. From Valentine’s day to Thanksgiving, gift your customers an AI-curated shopping environment and bring that enviable user engagement to your platform which you often see on A-list platforms. Having an AI-based recommendation engine is inevitable since the powerful algorithm searches the best product suitable for each visitor by taking a number of browsing parameters into account. The result is a more refined recommendation pattern and indulgent browsing experience.
[Wanna know How Alie Works? Check out.]
Traditional recommendation engines provide recommendations as per user purchase history. But Alie recommends content as per your visitor’s purchase behavior too. Your visitor zeroes in on a particular product after a lot of thinking. Alie follows this trajectory and takes cues from every click a user makes in the course of its final purchase. The deep learning algorithms deployed in Alie builds a specific user persona for every visitors and recommends the product based on their buying persona.
Why Time-Based Recommendation Engine is Important?
Well, the act of your own-purchase says it all, isn’t it! When users don’t have to take much pressure for consuming their favorite contents or buying their favorite products, it is quite evident that they will use the relevant service more than the others. According to different statistics, 85% of the total consumers prefer being recommended, where time-based recommendation engine holds a major part. It saves users’ time and brings growth to your business.