Product Recommendation Software

Retail businesses use product recommendation software to suggest goods to a company’s customers based on their past behavior. This system aims to better serve customers and increase the average order value

 

About E-commerce Product Recommendation System

In a nutshell, this system predicts and displays the goods a user would like to buy based on their preferences and previous interactions. An e-commerce product recommendation engine works by using algorithms that customize your customers’ shopping experience. This tool can make use of one of three types of filtering: customer-based, content-based, or combined. 

The tool can help increase sales by suggesting relevant products to customers who are already interested in making a purchase. By displaying complementary products and bundles, companies can increase their average order value and boost revenue. Additionally, an e-commerce product recommendation system can help increase customer loyalty and retention, as users are more likely to return to a website that provides relevant and personalized suggestions.

Personalized product recommendation in e-commerce is also beneficial for inventory and stock level management. By actively promoting products that are not selling well, businesses can clear out their inventory and reduce waste. Furthermore, by highlighting popular products and accurately predicting demand, companies can adjust their stock levels accordingly and avoid running out of stock or overstocking certain goods.

If the suggested goods are tailored to an individual customer, businesses can reduce bounce rates and increase engagement. That’s because users spend less time searching for the items they’d like to buy. This all contributes to building brand loyalty. Tools for product recommendation e-commerce can be integrated with marketing automation tools and CRM software to offer a more seamless experience. 

Features

Product recommendation system for e-commerce



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Work Process

Product recommendation software development consists of several stages, each involving its own team of specialists. As a rule, teams pay special attention to data analysis and Big Data.

1


Project planning

We estimate tasks, plan our resources, and set priorities.

Team:

  • Project Manager


2


Business analysis

We analyze the client’s current platform, data quality, and quantity, and create task specifications.

Team:

  • Project Manager
  • Business Analyst

3


UI/UX design

We develop convenient interfaces for different groups of users.

Team:

  • Project Manager
  • Business Analyst
  • UI/UX Designer


4


Back-end development

We build the server side of a web solution.

Team:

  • Project Manager
  • Business Analyst
  • UI/UX Designer
  • Back-end Engineers
  • Manual QA Engineers

5


Front-end development

 We build the user side of a web solution.

Team:

  • Project Manager
  • Business Analyst
  • UI/UX Designer
  • Front-end Engineers
  • Manual QA Engineers


6


Integrations

We analyze the systems we need to integrate and implement two-side integrations.

Team:

  • Project Manager
  • Business Analyst
  • UI/UX Designer
  • Front-end Engineers
  • Back-end Engineers
  • DevOps Engineers

7


Testing

We perform manual, automated, unit, and integration testing.

Team:

  • Project Manager
  • Manual QA Engineer
  • QA Automation Engineer
  • Back-end Engineers

8


Implementation and training

We develop training materials and implement the solution in the client’s company. 

Team:

  • Project Manager
  • Technical Writer
  • DevOps Engineers
  • Support Engineers

Our Clients

We develop large projects for clients across 27 countries, a significant number of which are featured on the Fortune 500 list.

Microsoft
Microsoft
Hyundai
Hyundai
Mazda
Mazda
Pepsi
Pepsi
Kia
Kia
Tennet
Tennet
Preston Baker
Preston Baker
Thomas Cook
Thomas Cook
Recipe Plus
Recipe Plus
Danone
Danone
Pivdenny bank
Pivdenny bank
Gravitec
Gravitec

Our company specialises in projects for
Retail & E-commerce

Why Do You Need It

Businesses in the e-commerce industry strive for personalization because their audiences are more likely to buy from those who understand their needs, and suggest relevant goods. Listed are some of the cases during which product recommendation engine development would be helpful.

Conversion optimization

Your online store has low conversion rates and high levels of cart abandonment

Inventory issues

You constantly deal with overstocking or out-of-stock items in your inventory

Low website engagement

Your potential customers don’t spend much time on your website

Acquisition costs

The costs of customer acquisition are very high

Customer insights

You have difficulties identifying customer interests and preferences

Marketing effectiveness

Your marketing strategies don’t achieve the desired results

Deeper audience analysis

You want to analyze the audience’s behavior and preferences better

Clear sales forecasts

You lack accurate sales predictions

Low average check

Your average check and repurchase rates are low

Wide range of products

You have a wide assortment of goods with many substitutes

A/B testing

You want to perform A/B tests to increase user engagement.

Our Standards

In our work we follow such international approaches and standards:

Management:PMP
Design:ISO 9241-210
Programming:Coding conventions, MDN Web Docs, Naming convention
Python:PEP 8
JS/TS:ECMA, JavaScript Standard Style, Google TypeScript Style Guide, ESLint
PHP:PSR
С#:ReSharper
HTML/CSS:W3C
Security:GDPR
Testing:ISTQB

Technologies

We specialize in specific technologies, with the technology stack chosen based on the project’s purpose and requirements

Programming languages:

Python
Python
Javascript
JavaScript
Java
Java
PHP
PHP
C#
C#

Web / Frameworks:

Django
Django
Fastapi
FastAPI
Spring
Spring
Hibernate
Hibernate
.Net
.NET
Node.js
Node.js
Express
Express
NestJS
NestJS
Laravel
Laravel
Yii
Yii
Symfony
Symfony
React
React
Vue.js
Vue.js
Angular
Angular
Ext JS
Ext JS

Databases / Data storage:

MySQL
MySQL
PostgeSQL
PostgeSQL
MongoDB
MongoDB
Redis
Redis
A. Casandra
A. Casandra
Elasticsearch
Elasticsearch
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3

DevOps Containers:

Docker
Docker
Kubernetes
Kubernetes
AWS ECS/EKS
AWS ECS/EKS

DevOps Automation:

Ansible
Ansible
Chef
Chef

DevOps CI/CD:

GitLab CI/CD
GitLab CI/CD
Jenkins
Jenkins
AWS
AWS

DevOps Monitoring:

Zabbix
Zabbix
Grafana
Grafana

Testing:

Selenium
Selenium
Postman
Postman
Swagger
Swagger
Apache JMeter
Apache JMeter
Cypress
Playwright
Playwright

Clouds:

AWS
AWS
Azure
Azure
Google Cloud
Google Cloud

Results

Providing a personalized shopping experience

Increased upselling and cross-selling rates

Reduced cart abandonment rates

Reduced cost of customer acquisition
Improved marketing strategies

More accurate and relevant suggestions over time

Marketing strategies tailored to trends and patterns

Average check and repurchase rates are higher

Managing a wide range of SKUs more efficiently

Making informed decisions based on sales predictions

About Us

70+

Employees

Clients

from Fortune 500

5

Locations

27

Countries we serve

82%

Senior experts

200+

Completed projects

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