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
Monitoring user actions on a website
Customer behavior analytics
Analyzing user interactions to understand preferences
User feedback analysis
Understanding user opinions and suggestions
Integrations with third-party tools
Connecting with other software
Concurrent editing
Multiple users working on the same document
A/B testing
Comparing versions to optimize performance
Cross-device recommendations
Recommendations across different devices
Custom filters
Creating specific criteria to refine data analysis
Reporting
Generating visualizations and insights from data
Location-based recommendations
Suggesting products based on user location
Email marketing analytics
Measuring the effectiveness of email campaigns
Requirements management
Defining, documenting, and tracking requirements
Content-based filtering
Recommending content similar to past preferences
Customer data platform
Centralized management of customer data
Social media analytics
Tracking social media performance
Discounts and special offers
Providing incentives to encourage purchases
Cross-channel data exchange
Sharing data across different marketing channels
User segmentation
Dividing users into groups based on characteristics
Demand predicting
Forecasting future demand for products or services
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.
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.
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
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.
Corporate platforms
Our company specializes in building internal corporate solutions
AI integration
We can automate processes with AI integration
Enterprise clients
We have built custom internal solutions for Kia, BAT, and Tennet
Retail and e-commerce
We focus on creating systems for retail and e-commerce
30+ million SKUs
We have built e-commerce projects with 30+ million SKUs
10+ million users monthly
We have developed e-commerce solutions with 10+ million users per month
Vast portfolio
More than half of our projects are in retail/e-commerce
Out-of-the-box platforms
We built recommendation systems based on out-of-the-box platforms
Team of 70+ engineers
We offer a full-time team of 70+ software engineers
Integrations
We can integrate the software with other corporate systems you use
Third-party systems
We have expertise integrating systems like SAP and Salesforce
Fortune 500 clients
We have experience working with Fortune 500 clients
Presence since 2005
We have been active in the software development market since 2005
82% senior engineers
We have an 82% seniority level in our team
Tech stack selection
Having worked with many technologies, we can help you choose the most suitable ones
Software integration
We have integrated Clerk.io, Nosto, Dynamic Yield
Our Awards
Authoritative Design 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:
Web / Frameworks:
Databases / Data storage:
DevOps containers:
DevOps automation:
DevOps CI/CD:
DevOps monitoring:
Testing:
Clouds:
Industries
Core domains we specialize in
Additional domains we have experience in
FINANCE
REAL ESTATE
TRAVEL & HOSPITALITY
AGRICULTURE
MEDIA & PUBLISHING
And experience in over 20 other industries!
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
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.