Case Study: How to Build an AI-powered Recommendation System

Did you know that 88% of consumers trust "word of mouth"? What's more, with the rise of the Internet, word-of-mouth marketing accounts for about $6 trillion in annual consumer spending. People tend to trust recommendations from family and friends for a reason: we're more inclined to trust the personal experiences of people we know well. But what about AI-based recommendations? With the spread of the Internet, e-commerce, and streaming platforms, we are just as likely to encounter and tend to trust AI-powered recommendation systems because those are based on the personal experiences of real people, and moreover, on our personal experiences.

Many companies already reaped significant benefits from incorporating artificial intelligence into product personalization. We know that 75% of Netflix's revenue and 35% of Amazon's revenue comes from product personalization. How do they get there? One way is by building a recommendation system to provide users with personalized content recommendations. After all, it's much more useful than pushing irrelevant cold-calling ads on potential customers.

Would you like to adopt a recommendation system with AI to boost sales? Well, let's cut to the chase and see how to create an AI-driven recommendation system.

What Is an AI-powered Recommendation System?

Once user data such as gender, location, income level, age, and occupation became available to businesses, it triggered the first steps towards sales personalization. This was reflected in the rapid growth of CRM, then ATS, ERP, recruitment software development, etc. Nowadays, AI-powered recommendation systems are already commonplace or even a prerequisite for companies looking to grow.

However, don't think that AI-enabled recommendation systems are only of interest to businesses. Consumers are so used to recommendations in the form of related or similar products, "movies you might like" lists, etc. that 71% of customers expect personalized interaction from companies, and almost 80% of consumers are disappointed when they don't get recommendations.

Imagine building a bespoke assistant for your online business - an AI-driven recommendation system that understands your customers like no one else. This intelligent system would help you provide tailor-made service for each customer. Here's how it works and how you can put it into action:

To create this smart system, you need to develop AI software capable of analyzing customer behavior and preferences. First, you can hire proficient software developers who are specialized in AI and software development. You might also consider onboarding development teams through Staff Augmentation, where experts join the project temporarily to help you reach your goal.

Leveraging Agile Project Management, the engineering team will create a Minimum Viable Product (MVP), a basic version of the system to test its effectiveness. This MVP will be either a web-based product or a mobile development solution. Think of it as the first version of the project.

Now it's AI's turn.

With Machine Learning techniques such as GPT-3 or GPT-4, the system learns from customer interactions and provides intelligent product suggestions. For instance, if you run an online store, AI could recommend products based on what customers have purchased before.

IT outsourcing services may be the way to go.

It is especially the case if you are not sure about estimating software development time or need specific mobile development expertise.

Remember, it's not just about creating software, it's about creating a solution that is going to revolutionize your business. By utilizing AI-powered recommendations, you'll make your customers feel valued and understood, which can lead to increased sales and engagement. So, whether you're starting from scratch or improving your existing systems, an AI-based recommendation system can be the key to taking your business to the next level.

Smart AI Recommendation System: How To Get It Done?

Creating an AI-based recommendation system may seem like a daunting task, but let's go through it step by step. Imagine you want to create an intelligent system that offers things users might like, such as movies, products, or music.

Here's what the step-by-step process of building an AI-based recommendation system might look like:

1. Why do you need it? Indeed, why do you need a recommendation system with artificial intelligence? Do you want to drive sales, engage users, or improve their experience? This step will help you clearly define your project's direction.

2. What kind of data do you have? Recommendations are like matchmaking, and data is your matchmaker. Collect data about your users and the products they interact with. This includes data such as their preferences, past interactions, and product attributes.

3. Pick a technique. Remember those awesome techniques that underpin different types of AI-based recommendation platforms? You need to decide which ones fit your goals. If you're new to this, collaborative or content filtering might be a good place to start.

4. Develop algorithms. This is where the magic happens. The developers you hired create algorithms - sets of rules that help the recommender system recognize patterns in the data. These algorithms learn from user behavior and use it to deliver intelligent product suggestions.

5. Feed your AI recommendation engine with data. Training an AI recommendation engine is like training a pet. You would use historical data to show the engine how users have interacted with products in the past. This will help it learn what users like and don't like.

6. Start with an MVP. To avoid being overwhelmed, start with a simple version of the recommendation system. This minimum viable product (MVP) allows you to test whether the system works and how users respond to it.

7. Fine-tuning. Based on user feedback from MVP testing, fine-tuning of the AI-enabled recommendation system can be performed. This may involve adjusting algorithms, adding additional data, or changing the way suggestions are presented.

8. Consider NLP and Context. With NLP or context (such as location or time), your recommendations are likely to be even more intelligent and personalized.

9. Evaluate and Improve. Like a chef tasting his or her dish, evaluate how effective your recommendations are. Are users interacting more? Are sales up? Use data-driven metrics to measure success.

10. Keep growing. Your AI-powered recommendation system is not a one-time thing. It is a dynamic system that gets better over time as it learns from new interactions. Keep monitoring its performance and improving it constantly.

Remember, you can always seek help from developers if any steps seem overwhelming. IT outsourcing or hiring qualified engineers can help you save time and ensure that best practices are applied. And with AI-driven recommendation features, you're not just building software - you're crafting an experience that keeps users coming back again and again.

Why Do You Need an AI-based Recommendation System for Your Business?

We know you're facing the daunting task of deciding whether to develop an AI-based recommendation system for your e-commerce business. It's hard to decide without a clear picture of its strengths or, conversely, its weaknesses.

For our part, we can say the following. We will utilize the full power of your data to deliver a recommendation system based on Artificial Intelligence. Our system will be able to adapt to your data, using every available piece of information to create tailor-made recommendations for your users.

Plus, you'll get the benefits of an ever-evolving model. We know your offerings are dynamic. That's why our system uses dynamic learning models to personalize content in real time. This ensures that we are always up to date with new products and the ever-changing tastes of your expanding customer base.

Now let's turn to why else a business may need to create an AI recommendation system:

• to reduce costs and optimize resource allocation
• to increase labor efficiency and productivity
• to better organize and leverage valuable business data
• to improve customer service and engagement
• to stay ahead in the marketplace
• to identify and minimize potential risks
• to simplify and improve business processes.

What’s the Idea Behind the Project?

Behind every technological innovation in e-commerce is the need to fine-tune processes and increase profits. The introduction of Artificial Intelligence into e-commerce is no exception. AI-based recommendation systems that use text classification for personalized content delivery, word embeddings for recommendation algorithms, contextual language understanding for recommendations, and other AI-driven techniques can revolutionize the selling and buying process.

For instance, a conversational AI that generates user-specific sentences could be like a salesperson who knows the buyer's problems and desires inside and out. AI can shorten the path from the idea of purchasing to paying for the product.

How else can an AI-based recommendation system help your business:

• ensures market activities are measurable
• enables recommendation-based purchasing
• provides in-depth analytics and data-driven reports
• facilitates better decision-making by buyers

With all of this in mind, DDI Development's IT outsourcing team working under the Staff Augmentation model set out to develop a machine learning-based recommendation system to deliver data-driven suggestions along with all of the above benefits.

The Process of Developing an AI-based Recommendation System

The process of developing an AI-driven recommendation system went from conceptualization to implementation, which is outlined in detail below:

Team

It goes without saying that the quality of the final output was determined by the qualifications of the specialists of our IT outsourcing engineering team. The team consisted of:

• 1 Business Analyst (BA)
• 1 Project Manager (PM)
• 1 UI/UX Designer
• 2 Front-end Engineers
• 2 Back-end Engineers
• 2 Quality Assurance (QA) Engineers

Methodology

We adopted an Agile project management approach to create an AI-powered recommendation system. And here's why:

• The principles behind this methodology put the customer's business needs first;
• Any changes in customer requirements could be made without compromising the quality of the product and the pre-defined software development plan;
• Each sprint brought the results planned;
• We managed to meet the planned 18 sprints.

Development

The process of developing an AI recommendation system kicked off with the Discovery phase, where our business analyst first gathered the customer's requirements and then drafted the relevant documentation.

Once this pre-discovery work was done, our team was able to define the main user roles within the Artificial Intelligence recommendation system. A functional roadmap of the system was also drawn up. The main features included the following:

• Triggered Messages
• Email Marketing
• Reports and Analytics
• Forecasting

We estimated the software development time and outlined a roadmap for the development process, after which we submitted our proposals to the client.

After the customer approved the proposed options, our engineers and project manager got to work:

• They compiled a list of tasks (Backlog)
• Divided the planned volume of tasks into sprints, resulting in 18 sprints.

To avoid fuss and misunderstandings, the team of engineers and designers started working only after all the goals and objectives of the upcoming sprint had been agreed upon.

The project manager was responsible for minimizing risks, competent task management, and timely reporting to the client and all stakeholders.

The well-coordinated cooperation between developers and QA engineers also brought clarity to the AI system development process. Thus, it was possible to eliminate possible shortcomings in time. As a result, the AI for recommendations was used as intended, and the client received a well-functioning AI-enabled recommendation system.

User Roles

Let’s discover User Roles within the AI-based recommendation system for eCommerce:

• Marketing Manager: A user who has access to marketing tools, sets up advertising campaigns, and maintains a customer base.
• Digital Analyst: This user has advanced access to analytics and reporting; the digital analyst processes data, interprets it, and delivers structured analysis results and reports to management.
• Administrator: Admin has access to all functions and capabilities of the AI-based recommendation system; they can add, change, or delete other users, set and change access, and make any changes to the system configuration.

Administrator Dashboard

Once you log in to the AI recommendation system as an Administrator, you will see a list of available sections on the left. These are the following sections:

• Dashboard
• Audience
• Items
• Marketing
• KPIs
• Reports and Analytics
• Settings

Let's go through some of these sections to get a deeper idea of what an Administrator can do within the system.

Audience

This section provides the Administrator with full access to all users of the site. The audience data is presented in the form of a table with columns ID, Country, Gender, Language, Last Action (purchase, abandoned cart, other), and Last Visit. You can add any other type of audience classification, as well as export audience data for marketing or analytical purposes.

Marketing

From here, the Administrator can navigate to any subsection related to marketing activities, such as Email Marketing, Push Notifications, Website Template Settings, Website Personalization, and Recommendation Strategies. The Administrator can not only view the settings and results of strategies and marketing campaigns but also make changes.

KPIs

Key Performance Indicators (KPIs) are key metrics that measure the effectiveness of marketing activities. With access to KPI data, an Administrator can track metrics such as recommendation funnel, purchases, purchases from recommendations, referrals, and more. This advanced KPI monitoring allows the Admin to monitor the effectiveness of marketers.

Marketing Manager Dashboard

When you log in as a Marketing Manager, you will enter the Dashboard page. As it turns out, you have a wide range of access to the system's marketing functions:

• Audience
• Items
• Website Personalization
• Email Marketing
• Push Notifications
• Recommendation Strategies
• Product Feed
• Widgets

Let's take a closer look at what you can find in the Marketing Manager Dashboard.

Audience

The Audience section is designed to shed light on understanding the behavior of customers and website users. The entire audience is organized in a table and segmented. The table also contains data on such categories as Unique Users (number of unique users in each group), Conversion Rate, Revenue per User, and AOV. As a Marketing Manager, you can apply some actions to the data in the table.

Website Personalization

There are dozens of ready-to-use content templates. Since the AI-powered recommendation system is flexible and customizable, it makes it easy to add content recommendations anywhere on your website or in any digital content channel. As a result, you get campaigns based on different conditions, such as user behavior or context.

Email Marketing

With the proper email campaign tools, you, as a Marketing Manager, can make your emails personalized, predictable, and engaging. Every email element can be customized and built with a drag-and-drop editor without any hassle or code. These are ready-made email templates that you can choose from.

Widgets

In the Widgets section, the Marketing Manager can customize tools and methods to display certain recommendations on the site. You can build or adjust any widget based on your marketing goals.

Digital Analyst Dashboard

Once a Digital Analyst is on the Dashboard, he or she has access to detailed analytics and advanced customizable reports. All of this can be accessed by going to the following sections:

• Audience
• Items
• KPIs
• Reports and Analytics
• Settings

Here's a sneak peek at some of them to get an idea of what Digital Analyst sees and what options they have access to.

Audience

This section provides detailed insights into user base segmentation to better understand user motivations, how qualified this traffic is, and how well it fits the products offered. From here, Digital Analysts can get as much information about the audience as they need. Traffic sources, device type, countries, events, income, and other types of data are available for processing.

KPIs

To get comprehensive or even superficial insights on the main metrics, just check out the KPIs section. You can access all the previously set metrics, and if they are not enough to make further decisions on AI-based recommendations, you can generate new metrics there as well.

Reports & Analytics

This is the place where the Analyst must be having a field day! With the Reports and Analytics feature, you can dive into detailed information about recommendations-based purchases on the site.

Direct revenue, assisted revenue, pages viewed before purchase through recommendations - all this data is available for analysis. With it, you can not only easily access all kinds of data, but also create reports based on specific actions and data. This will help you identify patterns in user behavior and quickly make data-driven changes.

Business Benefits of AI-powered Recommendation Systems

Let's face it: all sales process improvements and business process tweaks are aimed at one thing: increasing profits. No matter how many times you try to list the benefits of implementing data-driven suggestions or a user-product interaction matrix, they all revolve around the idea of making more money. Isn't that right?

For this to happen, you need to take care of users. So, the benefits of implementing machine learning-based recommendations can look like this:

• Enhanced Customer Engagement: Imagine your business offering personalized product recommendations and content that meet the needs of your customers. This kind of intelligence retains customers by directing them to products or content they will enjoy.
• Customer Satisfaction: An AI-powered recommendation system acts like an experienced guide, making personalized suggestions. This level of attention to individual preferences can increase customer satisfaction, leading to long-term loyalty.
• Efficiency Growth: With automated personalized offers, you save customers time and effort in finding what they need. This optimized experience can lead to repeat transactions and positive word of mouth.
• Proactive Insights: Consider the impact of a predictive recommendation system. It not only satisfies current desires but also anticipates future needs. This proactive approach can lead to higher conversion and customer retention.
• Tailored Marketing: Machine Learning-driven recommendations provide valuable insights into customer behavior. Imagine being able to target your marketing efforts based on this knowledge, resulting in more cost-effective campaigns.
• Measurable Impact: Recommender system metrics help you quantify success. You can track key metrics such as click-through rates and conversions to gauge the productivity of your system.
• Boosted Revenue: Having a smart recommendation engine can lead to increased sales as customers find products that relate to them.

Types of Systems for AI-driven Recommendations

When it comes to Artificial Intelligence-based recommendation systems, there are a plethora of approaches targeting different aspects of personalization. These systems are like digital consultants, analyzing users' behavior and preferences to make them offers they are unlikely to refuse. Which means sales are almost inevitable.

Below you can see the key methods and principles for building various types of recommendation systems based on Artificial Intelligence.

Content-Based Filtering Methods

Take a look at music streaming apps such as Spotify. When the app suggests songs similar to your favorites, it leverages content-based filtering. By analyzing the features of your favorite songs, such as genre or tempo, the system suggests tracks that match your music preferences.

Collaborative Filtering Algorithms

Think of movie streaming platforms like Netflix. When you receive recommendations for movies and shows based on what other users with similar tastes have watched, you're experiencing collaborative filtering in action. This method finds its strength in finding connections among users and suggesting items you might enjoy based on collective preferences.

Context-Aware Suggestions

Imagine a travel app that recommends nearby restaurants or attractions based on your current location and time of day. That's context-aware recommendations in action. By taking into account factors such as location, time, and even weather, the app ensures that its recommendations are timely and relevant.

Machine Learning-Based Recommendations

Amazon is a vivid example of a platform that uses machine learning-based recommendations. As you browse products, the system analyzes your behavior and preferences, and leveraging this data, suggests products that match your tastes. The more you interact with the store, the smarter the recommendations become.

Deep Learning Recommendation Models

Think of YouTube video suggestions. By analyzing your viewing history as well as likes and dislikes, deep learning models identify complex patterns and relationships, suggesting videos that fit your interests. These models excel at capturing subtle nuances and delivering highly relevant content.

Predictive Recommendation Systems

Weather apps are a great illustration of predictive recommendations. By analyzing historical weather data and your previous actions, these apps predict weather conditions and provide timely forecasts to help you plan your actions.

Cross-Domain Recommendation Techniques

Amazon's "Customers who bought this also bought this" feature employs cross-domain recommendations. For instance, when you buy a camera, the system may suggest photography books, thus demonstrating how it mixes data from different domains to provide you with a variety of suggestions.

Cold-Start Problem Solution Systems

The cold start issue arises when a new user with no experience interacting with the platform or when a new product with limited information is introduced to the system. Solving the cold start problem involves being creative in providing meaningful recommendations even with limited historical data. By utilizing demographics, content attributes, and other contextual data, AI-based recommendation systems aim to bridge this gap and offer valuable suggestions to both new users and products.

All of these recommender systems are related to natural language processing (NLP) in one way or another. Consider movie review platforms, for example. Using sentiment analysis of user reviews, NLP helps the system understand whether users liked the movie or not, leading to more accurate and personalized movie recommendations.

In the dynamic world of Artificial Intelligence-driven recommendations, these strategies as well as NLP-based enhancements provide users with relevant and compelling suggestions, improving their overall experience.

Features of the AI-powered Recommendation System

An AI-based recommendation system has a bunch of features making it a powerful tool for personalizing experiences. Here is an outline of some of these features:

• Forecasting Features: Through a predictive recommendation system, the engine not only responds to users' current interests but also predicts their future preferences. This way, you can proactively engage by making offers before users start actively looking for them.
• Audience Management and Segmentation: Beyond recommendations, the AI-driven platform allows for effective audience management. It segments users based on their preferences and behavior, which allows you to send targeted and personalized messages tailored to specific user groups.
• Triggered Messages and Timely Outreach: With triggered notifications, the system responds to certain user actions or events, providing timely AI recommendations. This dynamic interaction keeps users engaged and encourages them to take the desired actions.
• Email Marketing: By leveraging email marketing personalization, an AI recommendation engine expands its influence on users' inboxes. By tailoring the content of emails to individual preferences, it boosts the likelihood of users interacting with the products or content offered.
• KPIs: Evaluation metrics are used to accurately gauge the effectiveness of the recommendation system with AI. Metrics such as click-through rates and conversion rates provide valuable insights that help with ongoing optimization.

Bottom Line: Looking to Boost Your Business with an AI-powered Recommendation System?

In this case study, we showed how to create a powerful AI-based Recommendation System, leveraging cutting-edge technology such as GPT-3 and GPT-4. Our approach combines Agile project management, onboarding a skilled development team through Staff Augmentation, and employing efficient web-based and mobile development methodologies. By focusing on the MVP, we efficiently developed a robust recommendation system, enhancing user experiences with AI-driven suggestions.

If you're interested in exploring similar AI-driven solutions for your business, our experienced software development team is here to help. Feel free to reach out for a consultation, and we can discuss how to tailor such a system to your needs. If you're interested in exploring similar AI-based solutions for your business, our experienced software development team is ready to help. Don't hesitate to reach out for a consultation, and we can discuss how to tailor such a system to your needs. Based on our experience, we can together create a powerful AI recommendation platform that aligns with your objectives.