AI for ecommerce.
Increase your turnover

The main service provider of AI-powered solutions, STACC is developing a variety of artificial intelligence solutions for the field of eCommerce. These smart solutions can bring more visitors to the online shop, increase turnover by increasing the cost of the shopping cart and, in the end, improve the customer experience.

Increase your turnover!

Improve user experience!

Grow your customer satisfaction!

Recommendation system advantages

  • 1-5% turnover growth affected by recommendations
  • Personal recommendations are 2.2 times more effective than general bestsellers
  • 19% higher shopping cart value due to recommended products
  • 26% customers are more likely to open a newsletter if its headline is personal
  • 35% of Amazon’s sales were generated by cross-sale and upsale products
  • 56% of users return to a website that suggests alternative products
  • 550% more likely a visitor who clicks on the recommendations will get to the purchase.

We built the best recommendation system for personalizing shopping carts.

We created a superb offline recommender system for personalized email marketing campaigns.

Product-based recommendation algorithm that finds the most suitable alternative for any product.

Each innovative idea or new technology costs money, so it is reasonable to assess its profitability before making an investment. That is why our team has developed a profitability calculator for STACC`s recommendation system, which you can use to try out different scenarios and see how it would work for your business.

Calculate the ROI of your project

RECOMMENDATION SYSTEM TO INCREASE YOUR TURNOVER

Have you ever wondered how Amazon recommends products you might be interested in purchasing?

Recommendation systems study patterns of behavior such as customers’ clicks on products and/or previous purchases to know what the customer of the online shop prefers from among a collection of products they either have an experience with before or not.

PERSONALISED EMAIL CAMPAIGN

A large number of marketers segment their customers based on specific features and send a personalized email with different content to each segment.

Imagine taking personalisation to a level where each customer is a segment on his own and will receive special offers directed specially to him in the mailbox. 74% of mail recipients are annoyed when receiving irrelevant marketing content.

DIFFERENT MACHINE LEARNING SOLUTIONS

Any online shop that wants to stand out in the market should consider the implementation of machine learning!

STACC can help you with intelligent solutions like supply chain management, demand forecasting, machine learning pricing models, business process optimization, credit scoring, etc. Create personalized customer experiences and scale your business!

What makes STACC different

FeatureNo recommender systemSTACC recommender system
Personalised recommendations for products or usersNoYes
Personalised e-mail campaignNoYes
Engage shoppersNoYes
Increase average order valueNoYes
Increase number of items per orderNoYes

Our proven approach

“STACC has been a Coop partner in developing different recommendation system solutions. What we value most in this cooperation is the enthusiasm and creativity of the team at STACC. We recommend STACC if you need help with building AI or machine learning solutions for your enterprise!”

Maarika Haavistu, Ecommerce development at Coop Estonia

“The cooperation, its intensity and quality were at a very high level and it continues to be a role model for other (RIA) projects. STACC’s participants took part in the project actively, presented their proposals for additional functionalities that were suitable with the project’s schedule and budget. I feel happy and recommend it!“

Toomas Möder, project manager at RIA

“In the field of art, it is often not possible to collaborate with professional IT people. It is an extremely mind-broadening experience. Additionally, STACC’s employees Karl-Oskar Masing and Dage Särg are extremely enthusiastic and creative partners. I look forward to the first performance of our common artificial intelligence on the stage.”

Liina Keevallik, theater artist at MEDIT

STACC Recommender System through the lens of architecture

STACC built the first recommender system back in 2016. It was an e-mail recommender that was built to help create campaign e-mails by generating personalized recommendations from a selection of campaign products.

Explore the case study