Empower Your Business With The Magic Of Machine Learning!

blog image

Beyond Transactions: The Art and Science of Emotional E-Commerce.

We will explore here the personalization architecture of esmarket , the new experiences that offer to customers   and the relevant financial gains.

In the second part we will witness a customer journey, like a movie, seeking a perfect home office chair from esmarket.gr.

We will uncover strategies that tern ordinarily transactions into emotionally charged memorable experiences.

Esmarket.gr   predicts what house, garden, office products the customer will buy using mobiplus shopping recommendation  platform!


Is a leading online marketplace with more than 70.000 products related to the house and uses mobiplus shopping recommendation platform to offer personalized experiences to customers.

Customers discover products the love and are delighted from the prediction capabilities   of the  recommender system.

Recommender Systems are essential for ecommerce applications and now all modern e-shops have them built into their functionality.

Customers can thus find products without search and discover products they hadn’t imagined.

Customers in eshops which are not personalized have conversion rate 4% only   and this increases dramatically with the use of recommendation engines.

Esmarket that offers personalized   experience to users has a 30% increase in revenue  and it has:

  • higher conversion rate of new customers
  • larger Average Order Value and
  • more  Repeat customers

 Mobiplus shopping Recommendation platform uses the Shopping Data already within the e-shop and machine learning algorithms and then creates   seven (7) recommendation engines which connect to the e-shop.

So is your entire eshop   is personalized.

The recommendation engines are placed and connected in the form of carousels and are:

1. Recommended products on the first page for guest.

After the user makes a few clicks it recommends products from the cluster that it finds that the user belongs to based on clicks, i.e. the products that the user is now looking to find.

On the first entry of the user it will see products that sale now and then after 2- 3 clicks   goes to User Recommendation engine.

2. Recommended Products First page for user login.

Using user based recommendation architecture we take into account the user’s shopping history to date as well as the clicks he has made on the existing website session to better understand their needs.

Then we predict what products will buy next and recommended it using collaborative filtering architecture.

3. Related Products   on product page for quest.

Below the product he sees now. It recommends products based on content recommendation architecture with word2vec and image embeddings from the cluster of these products.

They are from the same category or close to the category to help the user find the product he wants!

4. Buy together!

Recommended  products in the shopping cart based on item to item recommendation architecture having found the products bought together for your e-shop…e.g. with shoes bought with socks!

5. Retraining   engine.

When new products uploaded we use these data and we train the recommendation engines in real time so we can recommend new products immediately to customers.

 6. Hybrid seasonality model.

Model that takes into account products bought at this time and corresponding seasons in the past and thus recommends seasonal products.

This model is added on top of the previous models.

 To learn how the top companies in the world increase revenue up to 30% from existing customers read the book below.

Let’s see now the customer experience!

Explore the emotional landscape of online shopping as we dissect the cinematic journey of a consumer seeking the perfect home office chair on esmarket.gr.

Uncover strategies to turn ordinary transactions into emotionally charged and memorable experiences.

In the serene digital expanse of esmarket.gr, our protagonist, a discerning consumer in search of the perfect home office chair, steps into a virtual sanctuary of furniture elegance.

The webpage, which is filled with a symphony of couches, armchairs, beds, and kitchen equipment, opens up to her like the first scene of an engrossing film.

It welcomes her into an elegantly designed universe.

The doors of esmarket.gr open in her virtual presence, exposing an entrancing assortment of home furnishings.

The atmosphere is infused with a sense of sophistication   and sets the stage for a delightful shopping experience for the customer.

Our protagonist, with the excitement of a movie’s leading lady, begins her exploration.

 The e-shop, like a skilled director, unfolds a series of thematic collections. 

Each click reveals curated   ensembles (because the recommendation engine predicts better what products will delight her), from cozy armchairs to elegant home office solutions, teasing the potential of her perfect find.

Without a specific chair in mind, our consumer encounters a carousel of “Recommended for you.” 

The carousel, animated with style options, ergonomic wonders, and varied price points, dances across her screen like a scene from a dream.

 Emotions of curiosity and anticipation surge as she allows the carousel to guide her towards her perfect seat.

Armed with a vision and a budget, our protagonist inputs her desired price range. 

The e-shop, much like a skilled scriptwriter, filters out options that align with her financial parameters. 

The journey becomes not just about the chair but about an experience tailored to her preferences and budget.

The journey unfolds with emotionally charged discoveries. As she scrolls, each chair tells a unique story.

 From the plush comfort of executive office chairs to the sleek designs of modern workspaces, she feels a connection with each product, each telling her, “I could be the one.”

And then, like the climactic scene of a movie, it happens—the “Aha!” moment. 

There, nestled within the recommendations , is a chair that beckons to her. 

The perfect blend of style, comfort, and, most importantly, within her envisioned budget.

 A rush of satisfaction and delight washes over her.

The e-shop, akin to a skilled cinematographer, captures the essence of personalization

The recommendations align not only with her budget but with her unique taste and the evolving narrative of her ideal home office. 

Each recommendation feels tailored to her, a testament to the power of a personalized shopping journey.

Inspired by the film’s wardrobe department, the consumer virtually tries on her chosen chair. 

The e-shop employs augmented reality, allowing her to visualize the chair within her own workspace. 

The emotions of excitement and certainty build as she virtually sees her potential purchase seamlessly fitting into her home.

The climax arrives as she moves to the checkout scene. 

The e-shop, her guiding script, ensures a seamless transaction. 

The emotional crescendo of satisfaction, discovery, and joy culminates as she confirms her purchase—the perfect home office chair is hers.

The closing credits roll, and our protagonist, having traversed the cinematic landscape of esmarket.gr, reflects on a journey that transcended mere online shopping. 

It was an emotional experience—an adventure of discovery and delight that left her not only with a chair but with a story to tell.

And so, as the curtain falls on this furniture epic, the protagonist exits esmarket.gr, not merely with a piece of furniture but with an experience etched in emotion—a movie of discovery and delight that unfolded within the virtual aisles of her chosen e-shop.

Read our book and learn why instore personalization and shopping data is the name of the game. 

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

How to create a personalized epharmacy eshop and increase revenues by 30%?

Get 750 extra products in your basket in 10 days!

mobiplus  member  Elevate Greece
Confirmed Innovation.


Start Free for up to 20k euros in revenue from Recommendations!

 Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.

Contact Us