In articles on our blog, we dive deep into the transformative power of custom AI solutions developmenе services, as more and more businesses are benefiting from the innovation. This time, we will shift our focus on how machine learning systems can elevate the retail industry, focusing on practical use cases that benefit the companies in 2024.

According to the report by Cognitive Market Research, the global Artificial Intelligence market was valued at USD 4951.2 million in 2023 and is expected to grow with a CAGR of 31.4% in a period from 2024 to 2031. While other subdivisions of Artificial Intelligence, like deep learning in retail, are a big part of it, still it’s hard to overestimate the significance of machine learning here.

Why Using Machine Learning Is Crucial for Modern Retail Businesses

The retail industry’s future is being shaped by the evolution of AI and ML capabilities as they influence every aspect of it, from inventory management to customer interactions. These technologies are no longer optional for businesses seeking to stay competitive, and below we describe why.

Staying Competitive in a Highly Saturated Market

Introducing ML in retail operations means enabling algorithms to sift through massive amounts of data and offer discoveries about trends, patterns, and customer insights. Based on these findings, retailers get the chance to refine product assortments, tailor marketing campaigns, and deliver personalized customer experiences. Equipped with this capability, retail companies can act on real-time data and align strategies with consumer choices.

Maximizing Profit Margins Amid Rising Operational Costs

While operational expenses connected to supply chain disruptions to labor cost are escalating, ML becomes essential in pinpointing inefficiencies and optimizing resource allocation. The most powerful asset ML introduces here is predictive analytics. With it, retailers can forecast demand with greater accuracy, strategically plan inventory, or set up dynamic pricing – all to squeeze the most value out of every investment and minimize wasteful spending. 

Adapting to Rapid Changes in Consumer Trends

Consumer preferences can change almost overnight due to factors like social media influences, cultural shifts, or economic fluctuations. However, ML allows retailers to quickly grasp these changes by constantly monitoring data streams. In this manner, algorithms spot consumer behavior (feedback, point-of-sale metrics, interaction with the retail website) and analyze them. Retailers, in turn, can respond to these trends by adjusting their offerings in real time. 

15 Advanced Applications of Machine Learning in Retail

From catering to customer satisfaction to ensuring stronger security measures, ML in retail covers an array of use cases.

1. Personalized Recommendations

According to a survey by Exploding Topics, 60% of consumers say they’ll become repeat customers after receiving a personalized shopping experience, and machine learning can be a great help with this. 

For over 25 years, Amazon has been a prime example of this use case with their recommendation algorithm and during these decades, they improved upon their solution, setting a great example for the retail sector. Their system leverages various machine learning algorithms to analyze customers’ browsing and purchase history, along with other data points, to deliver highly accurate product recommendations. The core techniques they use include collaborative filtering, content-based filtering, as well as hybrid models. 

This recommendation algorithm brings the company several benefits, including:

  • Boosted sales, as a result of encouraging users to discover and purchase additional products.
  • Increased customer satisfaction due to catering to individual needs. 

More efficient inventory and supply chain management, as Amazon can anticipate the demand patterns of popular products.

2. Customer Segmentation

This essential marketing process of dividing a customer base into distinct groups that share similar characteristics can be dramatically improved by utilizing powerful machine learning algorithms. With the capabilities of machine learning, it is possible to analyze large datasets and detect hidden patterns that may not be visible to human experts, quite similarly to how eCommerce fraud detection works. 

A notable example in this category is software by Optimove. Their solution uses all available sales data and employs complex clustering models to perform advanced customer segmentation. The technology results in many finely sliced micro-segments and in addition to that, a solution by Optimove recalculates the segmentation of every customer and tracks how customers move from one micro-segment to another over time.

3. Demand Forecasting

This is a critical aspect of any business in the retail industry, and combined with machine learning, anticipating future demand can be more accurate than ever. We already discussed the business impact of Big Data, which is also felt here, as ML models are capable of analyzing vast amounts of historical and real-time data at an impressive pace, overshadowing all traditional approaches.

SAP Integrated Business Planning for Supply Chain (SAP IBP) solution is a great real-world example of this. Its key business benefits include:

Among their customers are industry-leading companies, as SAP already helped Microsoft, Hyundai Mobis, DMK Group, and ZF Friedrichshafen.

Want to find out more information on Artificial Intelligence for retail?

We have another article with more valuable insights on how to improve customer satisfaction in your organization with the help of cutting-edge technology.

4. Dynamic Pricing

Machine learning-powered dynamic pricing provides businesses in the retail industry with powerful tools to optimize their pricing strategies in real time, taking into consideration market demand prediction, current competitor pricing, and changes in customer behavior. The main benefit of accurate and timely adjusted pricing is that companies can maximize their profits from each transaction. 

PROS Pricing Solutions has an outstanding tool for dynamic pricing strategies for airlines, aimed at serving passengers with the offers that they will actually pay for. The company promises at least a 2-3% bump in incremental revenue for the clients, after starting using this solution by taking the right input data, building context around the passenger, and optimizing the results.

5. Customer Service Chatbots

The usage of machine learning-powered chatbots and virtual assistants in the retail industry can already be considered mainstream, as there are estimations of $142 billion in sales through virtual assistants in 2024, as reported by Springs. This exponential growth is expected since the key functionalities of virtual assistants include:

  • 24/7 availability
  • Automated handling of common requests
  • Guiding customers through the buying process
  • Complementary items recommendations. 

In one of our recent case studies, we developed a high-load support AI chatbot for an online fashion store. Our custom solution can process 99% of queries in under 10 seconds, ensuring consistent availability and significantly improved customer satisfaction.

With the advancements of natural language processing for eCommerce business and retail, the Artificial Intelligence/machine learning functionality in these solutions will only exceed expectations, improving customer engagement and maximizing revenues from each customer interaction.Take LivePerson, as an example, a conversational machine learning platform that promises tangible outcomes of implementing their solutions, including:

  • 30% reduction in operating costs
  • 25% customer satisfaction boost
  • 90% automation rate during customer interactions. 
Serhii Leleko : ML & AI Engineer at SPD Technology

Serhii Leleko

ML & AI Engineer at SPD Technology

“Implementing machine learning in Retail to power up chatbots and virtual assistants is not limited to the retail industry, in fact, it can be a vital part of your bigger Intelligent Automation strategy that our experts can help you with. We have the know-how and experience to leverage machine learning algorithms, Natural Language Processing, and Deep Learning to develop tailored solutions that will secure the leading market positions for your organization!”

6. Fraud Detection

The effectiveness of machine learning models for Fraud Detection is well-known across a variety of industries, and the protection of sensitive customer data during online sales in retail is not an exception. 

The capabilities of machine learning systems to effectively identify and prevent fraudulent transactions in real-time can add to creating a safe retail environment, similar to how Artificial Intelligence in FinTech provides an additional level of security.

One of the solutions that stands out is Fraud.net, a world-leading AI-powered Fraud Detection platform. Trusted by Microsoft, Mastercard, and Amazon Web Services, among many others, users of Fraud.net claim to have:

  • 5% revenue increase from approving more good transactions
  • 80% fraud decrease due to more accurate and timely detection of potential threats
  • 92% lower false-positive rate. 

We know about the importance of effective Fraud Detection and Risk Management solutions first-hand, as we develop custom systems implementing machine learning and integrate third-party software in our retail and eCommerce app development projects.  The major part of our experience as a fraud detection software development company is associated with an enterprise-scale omnicommerce payment processing platform that serves 30,000 merchants and processes $140 billion in transactions per month. As a part of this project, we have developed functionality for performing Know-Your-Customer (KYC) and Know-Your-Business (KYB) procedures, plus the system that allows for ongoing fraudulent transactions monitoring and detection.

7. Sentiment Analysis

Being aware of feedback is of utmost importance to get valuable insights on customer loyalty. Assigning human experts for this task can be a waste of valuable resources, while machine learning algorithms, on the other hand, can automate this process with minimal human intervention. 

In this case, machine learning and Artificial Intelligence transform customer service by collecting, processing, and analyzing customer feedback across the Internet and social media, helping companies to improve their products and services following real-world customer data. Medallia offers AI-powered text analytics for everyone, from individuals to enterprises. Among numerous use cases, they offer their platform for comprehensive customer analytics, providing machine learning tools to analyze the sentiment of customer feedback publicly available.

8. Inventory Management

Machine learning applications can help physical stores as well by ensuring optimal stock levels to cover current and future demand. Once again, with unprecedented capabilities to analyze and take action based on vast amounts of customer data, it is possible to forecast future demand, optimize supply chain operations, and significantly reduce costs on inventory management. 

RELEX Solutions has nearly two decades of experience in using machine learning in retail for inventory management, demand prediction, and supply chain optimizations. Their end-to-end inventory planning software automates, streamlines, and simplifies complex inventory optimization processes to drive 99+% availability with 30% less stock, according to their case studies. 

While the exact numbers may vary, retail companies should expect similarly impressive results with Inventory Management and Demand Forecasting with ML.

9. Visual Search

According to Verified Market Reports, the global Visual Search Technology market size was valued at USD 137.67 Billion in 2023 and is expected to reach USD 1,057.47 Billion by the end of 2030. Machine learning is a key technology in this market, improving customer experience by allowing users to find the desired products using images, rather than text. 

Syte is a platform that specializes in offering retail companies AI-powered Visual Search and Image recognition technology, maximizing the benefits of visual discovery and AI tags. 

In their projects they got impressive results, including:

  • 7.1x improvement in conversion rate for Coleman Furniture
  • 40% uplift in average order value for Decathlon
  • 829% increase in average revenue per user for Chow Sang Sang. 

When we build eCommerce websites, our experts also implement Visual Search of any complexity and ensure seamless integration with any top third-party solution, including Syte.

10. In-Store Analytics

In addition to inventory management, other processes in physical stores can be improved as well. Analyzing customer preferences and behavior in stores can help improve layout and product placement. 

RetailNext offers a complete suite of products for retail companies, that includes accurately measuring foot traffic to stores in real-time with Aurora, the most advanced traffic system ever built. It is highly secured and easy to manage and install, having an API to embed data into your applications and internal dashboards.

11. Predictive Maintenance

This application is a massive cost saver for different industries, and one of the top use cases of machine learning and artificial intelligence in retail as well. Data generated from IoT sensors can be analyzed by machine learning applications and predict failures of costly equipment or schedule maintenance to prevent any significant downtime.

Uptake is a leader in Predictive Analytics Software-as-a-Service (SaaS), working to translate data into smarter operations. They are focusing on fleet management, claiming to increase fleet uptime with 4x ROI, however, they also use predictive maintenance with Machine Learning to anticipate repair needs for retail equipment and prevent unexpected failures.

Serhii Leleko: ML & AI Engineer at SPD Technology

Serhii Leleko

ML & AI Engineer at SPD Technology

“We, at SPD Technology, have cross-industrial experience in building highly efficient Predictive Maintenance solutions. If you are considering leveraging machine learning for Retail, Manufacturing or any other industry to prevent the failure of expensive equipment in your organization, we are ready to schedule a consultation and work on an individual approach that will work for you!”

12. Product Categorization

Product categorization is another essential aspect of eCommerce and retail that we should talk about. A proper categorization enables the better organization and searchability of items in online stores, elevating the overall customer experience. In one of our recent projects, we leveraged AI/ML for an expanded product range and enhanced eCommerce product listings for our client, turning technology integration into additional revenue streams. We will discuss this project in more detail later in the article. 

As for off-the-shelf Product Categorization solutions, Clarifai, the AI Workflow Orchestration Platform, uses machine learning in the Retail industry for the automation of categorization and tagging of product images. They unlock the power of AI across many areas of client’s organizations, including:

  • Automated Data Labelling
  • Retrieval Augmented Generation
  • Content Moderation
  • Intelligence and Surveillance
  • Generative AI
  • Content Organization and Personalization
  • Visual Inspection. 

13. Churn Prediction

As your business grows, one of the main challenges is identifying which customers are at risk of leaving. Luckily, machine learning can help you improve your marketing efforts here as well, by coming up with effective retention strategies based on business data. AI can analyze consumer behavior data, purchase history, buying trends, market trends, and competitor pricing to find out the reasons why certain customers may leave. Having this information will help you come up with ways to improve your customer experience and work on solutions that will improve customer retention, for example, better pricing strategies.

BigML offers a versatile platform that elevates machine learning in the retail industry to the next level. Among many functionalities of this platform, the developers offer machine learning tools for effective Churn Prediction, helping clients to pinpoint at-risk customers and take immediate action.

14. Supply Chain Optimization

72% of organizations believe AI in supply chain management will be the leading competitive differentiator by 2025 if a study by Zipdo is to be believed. It is easy to see value here since automated machine learning algorithms can be far better in demand prediction and overall planning compared to traditional approaches and efforts of human experts, or at least become a highly effective assistant tool. 

Coupa, an all-in-one AI-driven platform, offers a variety of machine learning and Deep Learning in retail solutions covering the entire sales cycle.

As for supply chain optimization, they offer:

  • Building a digital and comprehensive replica of a physical supply chain using data from multiple sources.
  • Testing unlimited scenarios and reaching preferred outcomes with an industry-leading AI and optimization modeler 
  • Effortlessly sharing data, visuals, and decisions with stakeholders using one tool.

Retail machine learning can be far more effective when combined with other cutting-edge technologies. For example, companies can benefit from Augmented Reality and Computer Vision development services and introduce Virtual Try-On solutions to businesses, so the customers will be able to virtually try on clothes and accessories before making a purchase.

15. Virtual Try-On

If you are looking for an off-the-shelf solution, Zyler is a great example of this, turning virtual fitting experiences online into tangible results for business owners. With 11.7 million completed virtual try-ons to date, the company offers its clients the following advantages of leveraging technology:

  • Increased sales, as there is no cost or risk to the customer to try on hundreds of products virtually and discover new items.
  • Reduced returns, since customers get reassurance and validation that a product is right for them before they purchase.
  • Increased brand engagement, as customers try on an average of 60 products per visit, and they are 50 times more likely to share their personalized try-on images with others.

With our article about 13 groundbreaking applications of Generative AI in eCommerce, you will find out all the intricacies of this evolving technology and the ways to implement it in your organization in the most efficient manner.

Out-of-the-Box vs Custom AI/ML Solution for Retailers: Main Considerations

Each of the ML-driven use cases mentioned above can reinvent retail operations and spur innovation. However, regardless of which one is the top priority, businesses must decide whether to adopt an off-the-shelf solution or a custom-built one. While the former may be simpler to implement and the latter more precisely tailored to specific needs, there are many additional factors to consider before making the final decision.

Business Objectives and Use Case Complexity

The choice of a preferred ML-powered software often depends on the scope of business goals and the needs it must cover. For more standardized objectives like basic demand forecasting, fraud detection, or high-level customer segmentation, pre-built solutions can often deliver quick wins. Such ready-made platforms are typically designed to handle common retail challenges and can be easily installed across the company. 

On the other hand, if retailers need to deal with intricate pricing algorithms, hyper-personalized recommendation engines, multi-level inventory management, or other highly specialized scenarios, then a custom-built system with tailored algorithms can cover the depth and flexibility of those unique requirements. 

Level of Customization Required

Customizations and the level of their complexity are often the crucial factors that determine whether to go for a ready solution or create one from scratch. This is because if a business requires standard functionality, off-the-shelf solutions can be sufficient. These systems can handle a few modifications, mostly typical ones like chatbots or customer segmentation, without sacrificing performance.

In case retailers need full control over every aspect of operations and, therefore, require detailed customizations, it would be better to develop custom solutions. Thus, it becomes possible to set up the types of data analyzed or the specific algorithms powering predictions as well as create unique data pipelines or analytical approaches.

Integration with Existing Systems

Once a business opts for off-the-shelf solutions, they might stumble upon the lack of native compatibility with existing tools. This can potentially require additional middleware or manual data transformations to fill in the gaps. In this way, businesses may find themselves involved in complex development work or heavy re-engineering. This is why it is important to understand that ready-made solutions can be painlessly integrated only with standard features or functionalities. 

Custom platforms, on the other hand, can be specifically designed to grant control over almost every element, from data ingestion to model deployment. In this manner, businesses can connect ML-powered platforms with your ERP, CRM, POS, eCommerce, and other internal systems, allowing the organization to benefit from real-time data sharing and process automation. 

Cost and ROI Considerations

When it comes to tight budgets, it is always reasonable to choose pre-built AI-powered solutions. They come with lower upfront costs and quicker piloting of AI capabilities. This is an ideal go-to option for niche retail businesses that focus on a small customer base and do not require highly specialized features that will require scaling over time.

Conversely, if the business is ready to invest in its growth, it is also reasonable to finance the development of a custom AI/ML system from the start. Custom platforms usually demand a higher initial outlay, as they involve specialized development, testing, and deployment processes. However, once the system is up and running, companies save on licensing fees, which can lead to favorable ROI over the long term.

Data Ownership and Privacy

When integrating off-the-shelf ML-based systems with third-party or cloud-based platforms,   businesses will deal with external data hosting. This will raise concerns about privacy, data sovereignty, and shared liability in the event of a breach. This is why retailers should carefully examine the vendor’s security measures and data governance policies to ensure they align with internal standards and legal obligations.

In contrast, a custom solution allows maintaining full control over the company’s data and how it’s processed, stored, and secured. This can be especially important in regulated environments or when dealing with sensitive customer information, as proprietary solutions can be tailored to meet strict compliance requirements.

Competitive Differentiation

Pre-built systems are available to any company willing to enhance their retail processes with ML. This means that many businesses may use the same platform, offering similar functionalities to their customers. 

However, if businesses are looking to differentiate themselves from their rivals by, for example, hyper-personalizing shopping experiences or creating cutting-edge marketing campaigns, a custom AI/ML platform can become a cornerstone of such initiatives. Proprietary algorithms and custom analytics can produce insights that competitors cannot easily replicate. In such a way, businesses can gain a distinct advantage. 

AI/ML Expertise Available In-House

The whole point of ready-made ML-powered solutions is that they allow for easier implementation and user-friendly setup. They typically contain pretrained models and vendor support that reduce the need for extensive internal expertise. This enables retailers to deploy AI/ML tools quickly and gain immediate insights without building a sizable data science department.

On the other hand, custom platforms often require a dedicated data science or engineering team to design, train, and maintain models. If the company’s in-house AI/ML development skills are strong or the company can invest in partnering with a specialized vendor, a custom platform can be developed with tailored algorithms to precisely meet the unique requirements of the business.

The Challenges of Applying Machine Learning in Retail

While ML is one of the most desirable innovations the retail industry thrives for, it is also one of the most challenging functionalities to implement. Below we list the major complexities our clients face during ML implementation for eCommerce and retail and ways to solve them. 

Managing Sparse and Incomplete Customer Data

Retail businesses often struggle with fragmented data as customers move across several channels. Some of the data is stored on devices in physical stores, some comes from eCommerce platforms and social media, some is saved in POS terminals. This lack of a single source of truth results in incomplete or inaccurate customer profiles, and our clients often note how hard it is to personalize marketing campaigns and improve engagement in such a scattered environment.

To solve this problem, we usually work on unifying data with:

  • Robust data governance policies and frameworks that allow maintaining data integrity, security, and compliance. 
  • Processes to clean, validate, and enrich data that fill in gaps, correct inaccuracies, and standardize information.
  • Enterprise data warehouses that combine data from POS, eCommerce, CRM, and social media into one centralized system.

Balancing Personalization with Customer Privacy

eCommerce businesses must comply with regulations like GDPR, FTC, and CCPA. While being essential safeguarding sensitive information, these regulations also limit how our clients can collect, store, and utilize customer data. 

We allow retailers to collect data and still strike a balance between offering personalized experiences by:

  • Utilizing federated learning and differential privacy techniques to train AI models without exposing individual data points.
  • Employing tokenization and encryption to ensure compliance while still allowing for detailed analytics and personalized service.

Difficulty in Scaling AI Across Omnichannel Retail

As our clients expand to multiple channels like online marketplaces, mobile apps, and social media, they notice that ensuring consistent AI-driven outcomes (e.g., pricing and promotions) becomes quite challenging. This happens because each channel operates with its own unique set of data, customer behaviors, and market dynamics. As a result, disconnected systems with heterogeneous data lead to inconsistent customer experiences and operational inefficiencies.

To address the complexities of scaling AI across multiple retail channels, we:

  • Develop modular AI models that can be easily customized for each channel. 
  • Integrate data from all retail channels into a unified system comprising a data lake vs data warehouse. 
  • Implement systems that update data about pricing, promotions, and inventory levels in real time across all channels.

ML Model Bias and Data Quality Issues

When datasets lack information or contain inherent biases, ML models can generate skewed recommendations, establish unfair pricing structures, and produce inaccuracies. For instance, in one of our projects, our client had the issue with biased data and its overrepresentation of certain demographics while excluding others. This prevented accurate customer segmentation and targeting. 

To avoid model biases, we focus our efforts on:

  • AI fairness auditing to detect bias in datasets and model outputs.
  • Data cleaning and enrichment by developing data pipelines that remove outliers, fill in gaps, and normalize information.
  • Inclusive model training that minimizes bias during the training phase.

Handling Real-Time Pricing and Promotions Without Errors

While dynamic pricing algorithms adjust prices in real time based on demand, inventory, or market trends, they can also inadequately trigger extreme discounts or surge prices. This means that a slight error in data input or model calibration can cause disproportionate price changes that alienate customers.

We help our clients to avoid this risk of hurting long-term brand loyalty with unreliable pricing by:

  • Implementing real-time pricing systems with built-in checks to prevent excessive discounts or markups. 
  • Monitoring pricing thresholds and profit margins, halting irregular price updates before they go live.

Fraud Detection Complexity in Retail Transactions

In some of our projects, clients’ ML-powered security platforms generated a high volume of false positives in detecting fraudulent retail transactions. This means that these platforms blocked legitimate users. Other clients turned to our services to fine-tune their ML-based fraud detection algorithms to make them better detect fraud. 

Our team helps our clients to mitigate these issues by:

  • Incorporating advanced techniques to monitor user behavior.
  • Making AI models process transactions in real time and flag suspicious behavior.
  • Continuously updating fraud detection algorithms based on historical patterns and newly emerging threats.

Balancing Automation with Human Decision-Making

It is not uncommon for AI-driven product recommendations to misfire and offer customers irrelevant product suggestions. It is also typical for over-automation to cause pricing or inventory errors and negatively influence profit margins. This means that relying too heavily on automated systems can have unintended consequences.

We help our clients to overcome such issues by: 

  • Building solutions with built-in checkpoints that allow for manual review and approval in critical scenarios.
  • Integrating AI outputs into existing processes to ensure team members stay informed and can override decisions when needed.

Lack of Retail-Specific AI Talent

AI/ML development in retail often requires specialized data science, engineering, and analytics expertise. However, most retailers simply don’t have the in-house talent needed to keep pace with rapidly evolving AI technologies. At the same time, onboarding skilled professionals can be expensive and resource-intensive. 

Many retail businesses rely on our AI expertise instead of hiring engineers in-house. For them, we: 

  • Train in-house teams to manage AI solutions effectively.
  • Provide a full suite of AI services, from data strategy consulting and model development to deployment and ongoing support.
  • Offer ongoing maintenance, updates, and optimization.

What Makes Professional ML Development Services for Retail a Wise Choice

Machine learning integration offers businesses groundbreaking opportunities, presenting a wide range of benefits from effective supply chain optimization to highly personalized customer experiences. However, to fully capitalize on this, it is vital to approach ML strategically and a professional development company can provide the edge required for success. 

The first factor that makes professional services a winning choice is customization. Every organization is unique with specific goals, customer profiles, and daily operational challenges, so ML solutions should be closely aligned with particular workflows to get the most out of. Along with that, professional developers have up-to-date knowledge of all technological advancements, which can be hard to maintain with an in-house team. 

There is also an aspect of efficient data collection and integration, as retail organizations always generate enormous amounts of data, including sales, customer behavior, and inventory systems. A vetted ML service provider has the necessary experience, tools, and approaches to handle complex data ecosystems, resulting in seamless data flows and lightning-fast performance. 

Finally, your business will grow, and the complexity of data and demands on your ML solutions will increase accordingly. With a pro, as your development partner, you can ensure that your custom ML solutions will expand along with your business, handling bigger data volumes and more sophisticated data analytics in the retail industry processes.

Why Partnering with SPD Technology for Retail ML Solutions Development

We, at SPD Technology, have built a strong reputation for delivering AI and machine learning development services to innovate companies of different sizes in a variety of industries. Our experts always come up with groundbreaking products that deliver tangible results for our clients. As for the retail industry, we have a deep understanding of it and a proven track record, since we have been operating in this business niche for over 18+ years. During this time, we fruitfully collaborated with both small retailers and global eCommerce leaders like the BlackHawk Network. The latter collaboration still goes on. 

Our team is constantly adapting the latest advancements in data processing, algorithms, predictive analytics, risk management, and fraud detection, providing the necessary expertise to help our clients stay competitive. We take a holistic approach to data integration while developing our modern, scalable ML systems that derive actionable insights for smarter decision-making on all levels.

SPD Technology will be your partner from initial consultation to deployment and post-release support, as we offer comprehensive, end-to-end development services. Our team will work alongside your business, feeling like a natural extension of your organization, providing optimization, improvements, and updates as long as needed.

SPD Technology’s Experience in Applying Machine Learning in Retail

Here, at SPD Technology, we are delivering eCommerce and Fintech solutions for our clients across the globe, including respected market leaders like PitchBook and BlackHawk Network. In some of the projects, we leverage our machine learning expertise to add to our holistic eCommerce development services.

Developing a High-Load AI Chatbot for an Online Fashion Store

Business Challenge

Our client, a French B2C eCommerce company operating in the fashion industry, needed an AI-powered chatbot for resolving customer queries in the online store. Based on the proven track record of completed ML projects, SPD Technology was chosen to develop this solution from scratch and help the client automate customer support with a seamlessly integrated, powerful chatbot. 

SPD Technology Approach 

After the initial research, we came up with an architecture, which was designed with GDPR-compliant data anonymization and guardrails, as LangChain agent powered by Mistral LLM served as the foundation of the solution’s design. We build this system to efficiently handle customer queries by retrieving responses from a vector database hosted on AWS, with content pre-stored in S3 buckets. AWS Bedrock and embedding models helped us to enhance pre-trained AI models through prompt engineering, eliminating the need for full retraining. 

We refined the chatbot’s responses through rigorous testing, including the highly effective RAG Triad method. Once ready, the solution was packaged as an API, seamlessly integrated into the client’s infrastructure, and deployed with autoscaling via EC2 instances and Kubernetes to manage high-traffic loads effectively.

Value Delivered
  • Complete Transformation of Customer Service: We developed a powerful solution that automated customer interactions for our client, significantly reducing the number of requests handled by human agents.
  • Outstanding Performance: Our chatbot delivers responses within less than 10 seconds for 99% of user queries and has 100% uptime during peak hours of 30 requests per second. 

Overall, we managed to deliver a cutting-edge AI-powered chatbot and help our client’s web development team to easily integrate, tune, and configure our custom solution, resulting in a significant process optimization.

Leveraging AI/ML for Expanded Product Range and Enhanced eCommerce Product Listings

Business Challenge

Our client, an online retail business, delivers clothing, electronics, beauty items, home supplies, and others to customers at competitive prices via a dropshipping model across the USA. We were chosen to ensure stabilization and enhance customer experience for the client’s B2C web solution.

SPD Technology Approach

Our team successfully tackled multiple challenges in boosting performance, improving SEO, and delivering peak scalability for the client’s web solution. We successfully optimized SQL queries, reducing the overall execution time through index creation and materialized views. Our SEO efforts combined several important activities, including improving visibility by cleaning up 404 errors, refining navigation, conducting keyword optimization, and enhancing product pages. 

There was also an interesting custom solution for parsing and categorizing the products that involved extracting data using ChatGPT and implementing Tesseract OCR and HuggingFace for image cleanup. Additionally, we included autosuggestions functionality that was generated using Algolia and ChatGPT. 

Finally, we ensured peak scalability for the web solution through map-reduce algorithms, AWS Batch, and parallel processing techniques.

Value Delivered
  • Significant Product Expansion: AI/ML capabilities allowed us to incorporate over 1,000,000 products from the major global retailer’s website into the client’s inventory, effectively scraping 300,000 products in a mere 3 hours.
  • Massive Performance Boost: We achieved impressive results in website loading speed, with a Google PageSpeed score improvement from 29 to 97 (web) and from 12 to 90 (mobile).
  • Effective SEO: Our involvement resulted in a 56% traffic increase for the client’s website within six months.

Ultimately, our engineers leveraged cutting-edge Artificial Intelligence/machine learning in retail technology, including expertise in Generative AI development services and image recognition models, to accurately categorize products, clean up irrelevant data, and improve search functionality to streamline operations and increase user engagement.

Conclusion

As we discussed the most valuable applications of machine learning in retail, it is safe to say that businesses that want to have a competitive edge should heavily consider implementing machine learning in Retail solutions into their core operations and think about introducing new products with this technology. 

The range of existing applications is already impressive, covering a wide range of areas including demand forecasting, customer segmentation, automated inventory management, intricate sentiment analysis, and supply chain management optimization. Take the benefits of using chatbots and virtual assistants, for instance, and with modern machine learning technology, the business value of these quite common solutions can be elevated to never-before-seen heights.

We, at SPD Technology, are here to help you embrace machine learning retail solutions and secure strong market positions by implementing cutting-edge innovations without any significant disruptions to critical business processes.

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FAQ

  • What is One Application of Machine Learning in Retail Banking and Finance?

    If we pick one application common in those industries, it will be a good idea to mention Fraud Detection. Powered by ML algorithms, financial institutions can monitor customer behavior and identify suspicious patterns in vast amounts of data. This real-time detection is possible since ML algorithms can learn from historical sales data and other inputs and distinguish fraudulent activity.