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. It suggests relevant products to customers who are already interested in placing an order. Displaying complementary products and bundles helps companies increase their average check 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 also benefits inventory and stock level management. Businesses can clear out their inventory and reduce waste by actively promoting products that have low sales. Highlighting popular products and accurate demand prediction help companies adjust their stock levels. Thus, they 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

Web behavior tracking
Customer behavior analytics 
User feedback analysis
Integrations with third-party tools
Concurrent editing 
A/B testing
Cross-device recommendations
Custom filters 
Reporting 
Location-based recommendations
Email marketing analytics 
Requirements management
Content-based filtering
Customer data platform
Social media analytics 
Discounts and special offers
Cross-channel data exchange
User segmentation
Demand predicting

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

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Project planning

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

Team:

  • Project Manager

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2

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Business analysis

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

Team:

  • Project Manager
  • Business Analyst

3

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UI/UX design

We develop convenient interfaces for different groups of users.

Team:

  • Project Manager
  • Business Analyst
  • UI/UX Designer

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4

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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

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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

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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

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Testing

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

Team:

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

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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
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Preston Baker
Preston Baker
Thomas Cook
Thomas Cook
Recipe Plus
Recipe Plus
Danone
Danone
Pivdenny bank
Pivdenny bank
Gravitec
Gravitec

Our company specializes in projects for
Retail & E-commerce

When do you need Product Recommendation Software?

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.

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Conversion optimization

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

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Inventory issues

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

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Low website engagement

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

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Acquisition costs

The costs of customer acquisition are very high

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Customer insights

You have difficulties identifying customer interests and preferences

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Marketing effectiveness

Your marketing strategies don’t achieve the desired results

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Deeper audience analysis

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

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Clear sales forecasts

You lack accurate sales predictions

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Low average check

Your average check and repurchase rates are low

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Wide range of products

You have a wide assortment of goods with many substitutes

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A/B testing

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

Need a product recommendation solution?

Develop a system that provides intelligent product recommendations by partnering with our experienced software engineers.

Our Software Development Standards

In our work, we follow international approaches and standards such as:

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

Why Choose SECL Group for Product Recommendation Software Development?

Our team has experience building internal corporate solutions for retail and e-commerce. We can help you build a product recommendation solution that enhances the shopping experience and increases customer engagement.

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Corporate platforms

Our company specializes in building internal corporate solutions

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AI integration

We can automate processes with AI integration

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Enterprise clients 

We have built custom internal solutions for Kia, BAT, and Tennet

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Retail and e-commerce

We focus on creating systems for retail and e-commerce

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30+ million SKUs

We have built e-commerce projects with 30+ million SKUs

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10+ million users monthly

We have developed e-commerce solutions with 10+ million users per month

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Vast portfolio

More than half of our projects are in retail/e-commerce

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Out-of-the-box platforms

We built recommendation systems based on out-of-the-box platforms

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Team of 70+ engineers

We offer a full-time team of 70+ software engineers

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Integrations

We can integrate the software with other corporate systems you use

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Third-party systems

We have expertise integrating systems like SAP and Salesforce

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Fortune 500 clients

We have experience working with Fortune 500 clients

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Presence since 2005

We have been active in the software development market since 2005

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82% senior engineers

We have an 82% seniority level in our team

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Tech stack selection

Having worked with many technologies, we can help you choose the most suitable ones

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Software integration 

We have integrated Clerk.io, Nosto, Dynamic Yield

Our Awards

Awwwards Award

Authoritative Design Award

Behance Award

Behance Interaction Award

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

Industries

Core domains we specialize in

VIEW ALL

Additional domains we have experience in

FINANCE

REAL ESTATE

TRAVEL & HOSPITALITY

AGRICULTURE

MEDIA & PUBLISHING

And experience in over 20 other industries!

Results

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Providing a personalized shopping experience

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Increased upselling and cross-selling rates

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Reduced cart abandonment rates

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Reduced cost of customer acquisition
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Improved marketing strategies

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More accurate and relevant suggestions over time

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Marketing strategies tailored to trends and patterns

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Average check and repurchase rates are higher

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Managing a wide range of SKUs more efficiently

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Making informed decisions based on sales predictions

FAQ

Here are our answers to some frequently asked questions about building product recommendation software. If you have another question on this topic, please get in touch with us.

What is product recommendation software?

Product recommendation software is used to suggest relevant products to the customers, and improve sales and user experience. Such systems leverage advanced algorithms to analyze customer behavior and preferences.

What is the cost of implementing product recommendation software?

The cost of implementing product recommendation software can vary depending on the complexity of the solution, the size of your business, and the specific features you require. It also hinges upon the size and composition of the development team engaged.

What data is used to generate recommendations?

Product recommendation systems generally use a combination of user data, such as purchase history, browsing behavior, demographics, and product or service data (characteristics, categories, reviews) to generate tailored suggestions.

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