Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
Usually we buy products that are well known, we have heard about them from our friends, we have seen them on TV, in an advertisement on the internet or on the front page of the e-shop.
But many times we discover , after a lot of search , products that we did not know and they are impressive.
Then dopamine comes down in our body and we are very happy about this discovery.
Wouldn’t it be impressive to offer this feature in your e-shop?
To have customers who walk in to discover impressive products that they hadn’t even thought to look for.
Thousands of e-shops and leading companies in the world today such as Amazon , Home Depot , Netflix , YouTube, wall mart, alibaba, spotify and thousands of other e-shops have understood how important it is to offer the ability for customers to discover products they never imagined and so they come back again and again.
This is where exposure (e.g., word of mouth, media coverage, recommendation algorithms) is higher for popular products than for less popular products.
More popularity leads to more exposure and more exposure leads to more popularity.
This is known as the Matthew effect or the Pareto principle.
The way this plays out in your e-shop is how you present the most popular products to customers, they choose them and they become more popular.
E-shops have the ability to recommend products to the customer automatically using Recommender systems based on the Shopping Data of your customers you have so far.
Depending on the customer’s interaction, it can be continuously and automatically improved and better learn their needs.
But these product recommendations are based on your customers’ habits to date and have been influenced by the popularity of the products.
But your e-shop requires exploring the desires of your customers and your customers need to discover products that they didn’t know .
This requires a constant exploration of your customers’ desires, individually and automatically for each one.
Your e-shop should automatically show the customer products that are not popular and learn from customer interaction.
Learn whether this new product he sees and hadn’t thought about is something he wants or not.Finding out if this new product he sees and hadn’t thought of is something he wants or not.
And learn from that.
However, such exploration, especially when the set of products available may change frequently, can lead to suboptimal experiences for users.
Explore Exploit is a framework in the field of Artificial Intelligence designed to collect and use the actions of your customers in your e-shop which gives a solution to the above problem.
They are algorithms that decide the percentage of new products that your eshop will recommend to the customer , minimize interactions with the customer, increase the customer’s satisfaction to find what they want, discover products they hadn’t imagined, complete their purchase, increase your revenue from that customer and keep them coming back again and again.
These Artificial Intelligence algorithms provide a set of tools for learning the needs of each of your customers individually , exploring deeper into their needs for personalized service.
Simply using your customers’ Impact Data with the products they request (known as an “exploit”) is not enough.
It is very important to collect additional information about our customer’s needs, which is hidden in products that they rarely or never see.
To learn how the top companies in the world increase revenue up to 30% from existing customers using Recommendation Systems and Personalization read the book below.
https://mobiplus.co/ebooks/customer-rediction/
This forms the basis for product discovery by the e-shop customer , with the addition of a meaningful mechanism that measures and learns from whether he clicked , or bought, did nothing with this new products recommended to him.
Studies have shown that with the right balance between exploration and exploitation, i.e. presenting products that we are sure they want, the performance of an e-shop improves significantly.
Exploration (exploit) focuses on capturing customer interaction by gathering new evidence about their needs and deeper desires.
Then all the data obtained during the exploration (exploit) such as views, clicks or not, and purchases are exploited to improve recommendations for other customers in the exploit set.
The exploration-exploitation strategy (Rellias et al., 1996) is a way also to cope with the shifting of fashion and trends in the market as well as and the inadequacy of some customers’ shopping data , since product recommendations in the e-shop are now performed based on and recent collected data.
On the other hand, an important aspect that significantly affects the effectiveness of the approach is the decision on which customers will compose the explore and exploit sets.
The key factor is the acquisition of new data during the exploration phase and the optimisation of the selection of recommendations based on existing data during the exploitation phase.
Therefore, it is necessary to improve the recommendations based on the knowledge already gained through the collected feedback, while new actions will be undertaken to further increase and update the knowledge.
It is clear that neither the pure exploration approach nor the pure exploitation approach works best in general, and a good compromise is needed.
They introduce the concept of exploration to reduce uncertainty about the importance of a Product.
Subject to judgment on product relevance until enough data is collected, Bandit is able to discover more relevant Products.
To understand Bandit’s role in all of this is that it helps introduce the concept of uncertainty in Product recommendations.
When all a recommender can do is either exploit (i.e. recommend) or ignore (i.e. not recommend) a product, the recommendation system ends up ignoring potentially relevant products.
This is due to the fact that it only has access to finite data about customers’ purchases and that they usually buy products that are known.
So use Bandit algorithms together with mobiplus shopping recommendation platform to help customers in your e-shop discover products they didn’t know they wanted, become very happy, come back to your e-shop again and again and increase your revenue up to 30%.
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