Empower Your Business With The Magic Of Machine Learning!

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Every eshop and store from now on will offer  personalized experiences to the customers.

2 out of 100 customers buy when enter your eshop and 20 out of 100 in store because they find it difficult to discover products they desire.

Retail Industry is going in the direction of customer personalization in e-commerce and in store.

Every e-shop from now on will offer a very personalized customer experience.

Every store will use Intelligent shopping assistants that  communicate with incoming customers with speech , recommend  personalized products on digital in store displays  and will collect customer contact and shopping data.

In  eshops each customer sees something completely different from the previous or next one.

Using Artificial Intelligence and Machine Learning Predict the products that each customer wants to buy individually and recommend them.

When he logs in we can understand who he is and the experience he will have will be really impressive.

So practically from now on when the customer enters your eshop you will have to convince him to log in.

He will be able to discover without any effort products that he had not imagined that you had in your eshop and even more impressive that he himself had not imagined that  wanted them.

It was somewhere in his subconscious that he needed it to take his life a little further.

The technology of Recommendation engineering discovers for your customer products they hadn’t thought to search for.

Until now eshops have not tried to a large extent to make the user login because they had nothing impressive to offer , a  significant benefit.

But using the Recommendation engine when the user logs in he will see:

  • Products he wants to buy
  • New arrivals that interest him, if they interest him.
  • Offers that interest him, if he is interested.
  • Proposals that interest him, a there is something to interest him.

These will be different from customer to customer.

So when he logs in and logs in the Home page will be different from user to user.

When entering the product category the category will be different from user to user.

He will have products that he wants to buy.

When he clicks on a product below he will be shown Recommended products that he wants to buy.

When he puts products in his shopping cart, products that he wants to buy or are bought together with this product and he hadn’t thought about  will appear underneath.

Not in general but specifically how your business works.

Don’t forget that recommended products are created by the recommendation engine by learning from your special customer purchase data.

They are products that your customers already buy or want together but  until today you haven’t used them because you didn’t have Artificial Intelligence to discover them.

So below we will give some examples of how your eshop will be using recommendation engineering, offering an impressive user experience and increasing your revenues up to 30%.

The user will now be able to enter your eshop and discover products and services that make him happy without any effort of thinking.

You won’t need multiple emails every month to every user to catch the one who wants to buy something.

You’ll give him an exciting experience and he will come back again and again!

Consumers and all people don’t like to think too much.

They want to use thinking (system 2 based Kahneman ) for things that matter.

They want to be offered products and services that satisfy them and they simply choose them.(system 1 Kahneman )

We’ll use Amazon as an example here who knows recommendation engineering very well but there are thousands of others you can see like YouTube , Netflix , TikToc , Alibaba ,Facebook , Linkedin , Spotify etc.

It therefore offers a unique , special and amazing experiences to every customer.

Every customer finds products that interest him without much thought and effort and makes his life more beautiful.

 

          Design  e-shops with Recommendation Engine.

 

                                The Journey Begins.

 

Looking at Amazon’s design we see the following:

The recommendations are practically visible on every page, tools and devices  taking endless shapes and forms.

In the mobile version of the eshop  45 different recommendation widgets were identified on the homepage alone.

These recommendation widgets present the products that the user will see using advance machine learning with several variables per user, such as:

・ Location

・ Recent purchases

・ Clicks per products , by category, shopping trends  and seasonal trend

・ Stored items or lists by consumer

・ Offers or discounts

・ Purchases completed by other customers after viewing similar products

・ User Reviews

・ Top choices  for customers

Both the  eshop and the app mimic each other, and each features lengthy  pages with dozens of unique recommendations.

Some recommendations promote product discovery, while others encourage users to buy previously purchased products.

Content recommendations often take the form of advertising banners and also appear in promotional messages.

In general, Amazon uses the homepage as one of the main beacons of discovery.

A unique use case of recommendations that Amazon is developing is on product detail pages (PDPs).

The recommendations, presented below, are almost unlimited.

Amazon does not limit the number of recommendations per widget on these pages and tailors the discovery experience across devices.

On the desktop, it lists the number of products per widget in the top right corner, presumably using its algorithm to display as many product recommendation as possible that match the predefined criteria for that widget.

On mobile, it deploys a horizontal infinite scrolling feature, allowing users to endlessly switch between products that are similar to the current product in view.

                                     

                                     

1.Homepage.

The fixed homepage menu on the desktop site is optimized for logged-in users , displaying more menu options , such as “Buy Again” and “Browsing History” as a push towards product discovery and creating a better user experience.

These menu items do not serve primary/anonymous users because they have no purchase or browsing history accessible.

Instead, they are served exclusively with the basic menu items

(Amazon.com,” “Today’s Deals,” etc.).

Encourage repeat purchases in the home page menu bar for known users

 “Buy Again” is a purpose-built page that is extremely useful for Amazon users.

They are able to browse through previously purchased products with ease and repeat purchases, which is unusual in the world of e-commerce retailing.

   

Home page recommendations by category.

Platform:Web, Application

On the homepage, Amazon recommends  items based on your browsing/shopping history on the homepage, sorted by category.

 

Home page recommendations based on offers.

Platform: mobile web, app

Amazon gives personal recommendations based on ongoing agreements that meet the user’s requirements,correlations and interests.

Content recommendations for known users on the desktop home page.

Platform: Desktop

Amazon offers personalized content recommendations for returning users (music, videos, content, etc.) and graphical recommendation elements  based on previous user history.

CTA item  control  on the home page.

Platform:  Web  and mobile

The home page on the mobile web includes a flow with an endless scroll, featuring several, personalized graphical elements.

This encourages users to review a recent purchase from their home page feed  feed.

Product tracking messages!

Platform: mobile web

After reviewing an exhaustive list of personalized recommendations on the home page, users are provided with a message explaining that this is the end of the initial proposal experience and are then encouraged to browse the product categories.

 

 

                                 

           

 

Buy Again.

Recommendations based on repeat purchase for logged in users.

 Platform.web , mobile ,app

Users are served with a very specific type of recommendations in Buy Again.

Recommended products   are  those that other Amazon customers frequently buy again, as well as products that are “frequently repurchased” in specific categories.

Buy Again recommendations in the menu.

 Platform: Desktop

In the drop-down menu on the home page, users are served up to 3 products recommendations items they have purchased along with menu options, giving users easy access to repurchase these items (“Buy it again” section).

  

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/

  

2.Category pages.

Recommendations on category and section pages for registered and first-time/anonymous users

Platform: desktop, Mobile Web, App

Within category and section pages, Amazon serves up category-specific recommendation images to all site visitors.

Amazon implements a number of recommendation strategies through these widgets, which include well-known offers and best sellers.

The recommendation graphics are in carousel format, with over 20 products per carousel and 4 carousels per page.

On both section pages and category pages, recommendations appear almost or completely below the box, and are stacked on top of each other, across all channels.

 

 

Numerous recommendations appearing in the  category pages.

 Platform: Desktop

Connected users are provided with product recommendations in the category and   section.

Each recommendation  component has a different strategy, such as

  • “Recommended for you”,
  • More top options for you” and
  • “Most wanted.”

 

In-category recommendations for anonymous users.

Platform: desktop, Mobile Web, App

Anonymous, new users receive recommendations for

“Best seller,”

“Most wanted for,” and

“Top rated” on category pages.

In the app, up to 6 recommendation widgets are displayed.

Up to 5 recommendation widgets are displayed on the desktop.

And on the mobile web, users can see up to 6 recommendation widgets.

               

Seasonal product category.

Recommendations for all users on all channels

Platform: desktop, Mobile Web, App

Within the category pages, Amazon  recommends products that have seasonal relevance, i.e. Halloween costumes on the Clothing section page, holiday gifts in many categories during the month of December, etc.

 

  

 

 

3.Product Description Pages.

Product comparison  widget in PDPs for some products for all users

Platform: desktop, Mobile Web, App

On some PDPs, Amazon displays a widget below the fold with side-by-side comparisons of similar products to the product in question.

These widgets are compared upwards of 6 product variants, the first of which is the primary product.

 

 

 

 

Bunners  PDPs for all visitors.

Platform: desktop, Mobile Web, App

Amazon shows bunners on the pages following some PDPs in Amazon’s desktop eshop.

These banners promote Amazon gift cards and promote high-quality video content to both known and unknown users.

Some PDPs display third-party advertisements in place of an Amazon home banner.

 

Promotional offers on PDP for known and unknown users.

Platform: desktop, Mobile Web, App

Amazon presents different offers in the PDP descriptions to promote loyalty programs, sponsorships, and more.

This method allows them to highlight different financing tools, such as Amazon Reward Signature Cards.

In this way, they try to integrate more paying users into their credit card programs, as well as offer discounts at the time of purchase.

  

                       

Trending products.

Recommendations from popular products

Platform: desktop, Mobile Web, App

Users are provided with personalized recommendation  within categories, by segment, popularity and trends for that category.

             

Recommendations by  personal shopping trend.

Platform: desktop, Mobile Web, App

Users are provided with recommendations based on their “shopping trends” -these are usually items they have not seen before and instead fill in products they may be interested in.

 

 

 

Promotional Offers and Incentives.

One of the first promotional offer encounters in the eshop is displayed within the top menu bar.

“Today’s Deals” directs all site visitors to a home page with selected products that are discounted especially on that particular day, along with a countdown counter in the description of each product.

 

 

 

Session-based recommendation  for unknown users on all channels.

Platform: desktop, Mobile Web, App

Users are served direct widget recommendations depending on the segment they are in and what they have seen so far.

 

 

Social proof recommendations graphic on product detail pages.Social proof  Craffia element of recommendations for social proof.

Platform: Application

Amazon encourages users to mimic the buying behavior of customers who have bought an item that the user is currently viewing .

  

4.Cart page.

Presentation of personalized Recommendations to all users across all channels on the cart page.

Platform: desktop, Mobile Web, App

Logged-in users have a rich set of recommendations with different strategies per user and per channel.

In addition, logged out users see a location booking symbol for where recently viewed items would be located, with a call to action to sign up and log in to see personalized recommendations.

Strategies developed in this section include

  • Sponsored products related to items in your cart”,
  • “Related to items you saw”,
  • “Inspired by your browsing history”, and
  • “Customers who bought items in your cart also bought.”

Save for later option for all users  on all channels.

 

Platform: desktop, Mobile Web, App

On the cart page, users can access their stored items.

In this section, users are encouraged to click on “Move to Cart” if an item is in stock, and are served with “Prime”messaging, if available, and CTA for “comparison with similar data”.

 

 

   

Comparison of CTA products for all userson all channels.

Platform: desktop, Mobile Web, App

On the shopping cart page, Amazon presents an option to “Compare with similar items” below the product descriptions.

Users can click on this CTA and see similar products with an item in their shopping cart in case they want to edit or add another item to their shopping cart before checking out.

On mobile, the “Similar Items” widget is a dimmer that appears at the bottom of the screen.

On the desktop, an overlay widget appears when the user clicks on the CTA.

 

 

                             

5.Customized search results pages  for all users on all channels

Platform: desktop, Mobile Web, App

The search results pages are modified for different users making the same search query.

The products are sorted differently, but the number of products displayed per page is the same.

 

Recommendations via search queries for all users.

Platform: desktop, Mobile Web, App

When a user arrives at the app/eshop and starts typing a query, a drop-down menu appears, consisting of product recommendations (simply the names of products) based on how many letters the user has typed.

                                   

6.Emails -Product recommendations in emails.

 Platform: Email

Amazon uses several recommendation strategies when sending purchase confirmation emails.

These strategies shall include recommendations based on

  • Popular products,
  • Offers,
  • Market opportunities ,
  • They are bought together,
  • Others bought,
  • Best sales and more.

Each email has an average of 2 recommendations, and these are developed exclusively in sending emails.

  

How to create a personalised e-shop. Household Equipment  with Artificial Intelligence and increase revenue by 30%?

Lartigiano increases revenue and cart using Artificial Intelligence!

750 extra products in the basket in 10 days!

See how he did it here!

See how  the Lartigiano’s  chain is increasing revenue and basket with Artificial Intelligence !

Contact us now to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.

mobiplus  member  Elevate Greece
Confirmed Innovation 

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