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10 Machine Learning Projects in Retail You Must Practice

Read about the 10 projects in Machine Learning that Retail stores should develop today, for better outcomes in sales and customer retention.

Team Omind

Team Omind

March 21, 2024

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Machine learning, a subset of AI, has been a crucial factor in the retail experience exposure to customers. ML is transformative in the sense of deploying datasets to re-evaluate the entire retail process in reducing redundancies. Thus, with redundancies calculated and removed from the system, customers enjoy the benefit of a far-fetched customized experience. Retail managers all around the world have vehemently uptaken Machine Learning projects to remain competitive.  

As AI continues to evolve, so does the data-centric approach of ML. Centered around a human-centric delivery of the shopping experience, ML successfully raises willingness to accept the bargain. As customer behavior patterns are observed and internalized, organizations benefit from a distinct overview of market demands. Furthermore, retail managers also utilize ML to set the supply curve concurrent with the demand, effectively reducing redundancies in the process. 

Here is a brief overview of 10 Machine Learning projects crucial to retail. 

Customer Segmentation 

Customer segmentation is the process of segmenting customer datasets on different input classes, such as age, race, gender, and profession. The determined output is then used to classify customers based on input parameters so that companies can allocate their resources accordingly. Input parameters vary according to the requirements of the organization. Various customer segmentation aspects are in practice, including the K-means cluster algorithm and the Decision Tree.

K-Means Clustering Algorithm 

The k-means clustering algorithm is an effective machine learning tool to label a dataset into K number of clusters. By applying this unsupervised learning algorithm, you can set the number of cluster algorithms and obtain definitive datasets that can be used to further customer segmentation prototypes. 

The datasets are classified across centroids, leading to the formation of clusters with similar features. Individual clusters also retain their identity, thereby letting businesses profile customers according to their spending habits or other similar datasets. Applying the online retail datasets for business can prove to be a tangible application of the K-means clustering algorithm. The K-means clustering has a standalone value in customer segmentation. It can also be used in concurrence with other customer segmentation ML procedures such as Neural Networks, Association Rule Learning, and Decision Trees. The key steps of this ML project include exploratory data analysis, and visualization to find the optimum number of segments.    

Customer Churn Prediction

Customer churn, in retail, refers to the number of people unsubscribing to your services over a set period. The churn rate is an indicator of business growth as lower churn means better business. Several factors influence customer churn, including dissatisfaction, lack of appreciation, competitor preference, and finances. 

Churn rates need to be predicted at the early onset to prevent its prevalence. Ideally, a retailer must strive for a 0% churn rate, but it is practically impossible. The goal, therefore, is to use ML algorithms to predict customer behavior and take steps to maintain the churn rate as close to 0% as possible. Even a minimal percentage of churn can balloon over time, implying the need for timely preventive methods.  

Machine Learning can effectively correlate the current churn rate with various factors influencing it, thereby suggesting modes of improvement. The Telecom Churn Prediction dataset can be used as a suitable example of how ML can develop effective predictive models.

Steps for Building Churn Prediction Model Using the Telecom Dataset

Telcos report a significant customer churn problem, attributed to various factors such as demographics, usage patterns, and payment history. Application of ML project to create a predictive churn model involved the following steps.

Steps for Building Churn Prediction Mode
  • Data Cleaning: The data cleaning algorithm competently removes duplicates from the dataset, providing a fresh set for straightforward assessment. 
  • EDA: EDA or Exploratory Data Analysis helped to interpret the distribution of data and identify the patterns of churn in telco companies. The EDA can be performed across various variables such as gender and age. The final step of the EDA requires hypothesis testing. This implies the potential of churn under possible hypothetical situations. A code is thereby created based on the conditions of the hypothesis, to continue with the next stage of creating a predictive dataset.
  • Model Building: Finally, a Machine Learning model can be devised based on the EDA. Several algorithms can be utilized for model building, either as a standalone model or in concurrence with the others. Such algorithms include Gradient Boosting, Random Forest, Decision Tree, K-nearest neighbors, and Logical Regression.

Market Basket Analysis

Machine learning provides valuable insights for the retail industry. By analyzing previous purchasing patterns, retailers can gain key information to boost sales. For example, market basket analysis reveals which products customers purchase together. Retailers then use this data to optimize store layouts and web design to place complementary items side-by-side. This technique helps facilitate cross-selling opportunities. Do go through our other blog on customer analytics in E-commerce

Process using the Groceries Dataset and Apriori algorithm

The Apriori algorithm is frequently used in ML projects, such as the Market Basket Analysis of a grocery dataset. The Apriori algorithm asserts that a subset of a frequent set of grocery items must also be frequent. So, if bread, wine, and chips constitute a frequent subset, it is asserted that bread and chips would also be frequent. Likewise, for an infrequent item set, it is assumed that its subsets will also be infrequent.

The Market Basket Analysis of a grocery subset will take into account the following metrics.

  • Support
  • Confidence
  • Lift 
  • Conviction 

Support (x) is defined as the ratio of the number of transactions of an item to the total number of transactions of the item x.

Confidence (x => y) measures the likelihood with which item y would be purchased when item x is also purchased. Confidence is measured as the ratio of support (x) to support (y). Confidence takes into account the popularity of item x first.  

Lift ( y => x) measures the likelihood with which item x would be purchased when item y is also sold. Here, the popularity of item y is taken as the predominant metric. 

Conviction, in simple terms, is a ratio of Support and Confidence/Lift to determine the possibility of the items frequently purchased together. Using the Support and Confidence metrics in ML, it is possible to suggest predictive trends in MBA, increasing sales figures. 

Fraud Detection in E-commerce Transactions

Fraud Detection in E-commerce Transactions

Payment fraud has become a top priority for businesses to combat due to the financial losses and reputational damage it causes. In addition to the immediate financial impact, payment fraud can also erode customer trust and loyalty while increasing scrutiny from regulators. To address this growing threat, organizations are adopting machine learning solutions.

Machine learning, a subfield of artificial intelligence, offers a powerful and adaptive solution for tackling payment fraud, which is complex and constantly evolving. By leveraging such ample datasets along with advanced algorithms, machine learning can effectively identify implied patterns and anomalies. This enables businesses to detect and prevent fraud in real-time, helping them maintain payment security to safeguard customers, revenue, and reputation.

Building a model using Synthetic Financial Dataset

Incidents of fraud, although less common, can still be mapped using a synthetic financial dataset. Synthetic data is defined as datasets that are created by computer technology as predictive models, without existing in the real world. In other words, it refers to data ‘generated’ as against data ‘collected.’ Synthetic data can be full, partial, or hybrid.

The key challenge of fraud modeling is that these cases rarely take place, leading to data imbalance. The insufficiency of data can be mitigated by using synthetic data, which is used to oversample the minority data, leading to the formation of a balanced dataset. 

Such an approach implies two key challenges. Firstly, it can lead to false predictions. Secondly, it can lead to wrong output if the original data is skewed. Such challenges can be overcome by applying ML principles such as data cleaning, decision tree, and ANN,  

Classification of Customer Reviews 

Analyzing customer reviews helps your company understand overall customer satisfaction, as they provide feedback on what customers really want. This insightful information enables you to improve customer service by quickly and efficiently resolving the issues consumers face, thereby creating a positive experience that focuses on their needs.

Also, reviews help create an instant bond with customers, acknowledging their voice as a part of the product experience. Such a heightened experience improves product credibility and increases customer loyalty. The methodology used includes the application of Natural Language ToolKits (NLTK) to parse customer reviews and develop an actionable data map on which customer experience can be reviewed. 

The processes utilized in NLTK analysis involve dataset preparation, text vectorization, and model evaluation. The critical user insights derived from this ML project can help organizations deliver an optimized customer experience, minimizing the chance of negative reviews by addressing the problems identified by the ML project.  

Product Matching 

Product identification presents a complex challenge for retailers. Though customers can easily locate competing items through quick online searches, retailers themselves struggle to achieve the same comprehensive view. Manual exploration of competitors' websites, while certainly diligent, often fall short of replicating the customer experience.  

To truly match products in today's crowded marketplace requires more than just hours of effort - it demands technology capable of aggregating data across the entire competitive landscape. With the right tools, retailers can mirror their customers' perspective, identifying comparable items rapidly and accurately. This enables confident value positioning and sharp competitive pricing. In a crowded marketplace, product matching is no longer a manual task - it is an automated imperative.

In this product-matching dataset on Kaggle, the following methods have been utilized. In the first method, the two datasets have been matched by Simple Matching since they are similar in their scopes. In Method 2, while matching products with a different order on two datasets, a Tag vector is formulated which is again converted into a vector using CountVectorizer and the TF-IDF approach. Similar methodologies include KNN and image matching.    

Cross-Sell Prediction 

Cross-selling is a powerful strategy for building customer loyalty and increasing revenue. By offering customers complementary products or services related to their original purchase, companies can provide greater value and deepen relationships. When done right, cross-selling demonstrates that you understand your customers' needs and are committed to meeting them. This thoughtful approach fosters trust and satisfaction. 

Rather than a hard sell, effective cross-selling feels like a natural next step that benefits the consumer. A satisfied customer is more likely to purchase again and recommend you to others. In this way, cross-selling boosts customer lifetime value and retention. Though it requires insight into your offerings and customers, cross-selling offers an excellent opportunity for growth by enhancing the post-purchase experience. With a relevant recommendation at the right moment, you transform a single transaction into an ongoing, mutually beneficial relationship.

Health insurance is a common field where cross-selling is primarily applied. This dataset on health insurance cross-selling develops a predictive model using ML projects such as decision tree and random forest. The predictive output can be effectively used to promote health insurance across a diverse customer dataset. 

Predict Customer Satisfaction

Predict Customer Satisfaction

For e-commerce users, a satisfying shopping experience is the holy grail. Studies reveal numerous factors that shape this elusive consumer satisfaction, wielding the power to make or break repeat business. Customer and user experience reign supreme, with brand image, pricing, service quality, trustworthiness, and convenience following close behind. The ease of navigating the e-commerce platform and its aura of security also influence satisfaction. 

Additionally, customer value perceptions, promotional offers, and platform productivity impact the satisfaction meter. E-commerce players must keenly understand these drivers and continuously optimize them to delight customers. By soliciting candid consumer feedback and deploying evaluation models, e-commerce platforms can elevate service levels and satisfaction. When consumers feel satisfied, they reward platforms with repeat purchases and loyalty. By obsessively pursuing consumer satisfaction, e-commerce businesses create a virtuous cycle of value. 

Do go through our blog on the top 10 retention tactics for customers to find out more about how you can leave customers satisfied and also come back for more. 

Predictive Model Building with Women's e-Commerce Clothing Reviews Dataset

In this dataset on Kaggle, outlining the customer reviews on women’s e-commerce clothing reviews, a set of ML techniques is used to determine a predictive model. These include NLTK, model evaluation, and data pre-processing to achieve the desired output.  Following data engineering, data cleaning, and mapping of polarity, the ML project delivers a clear output on the predictive analysis of women’s wear reviews in the given dataset. 

Store Item Demand Forecasting

A critical component of retail success is anticipating what customers will buy in the future. Demand forecasting synthesizes insights from a multitude of sources to project upcoming needs. By considering external factors like market trends along with internal data on promotions and inventory, retailers can craft a detailed estimate of future demand. 

With greater forecasting accuracy, retailers can meet customer needs while optimizing inventory levels. Though forecasting relies on cold hard data, its ultimate purpose is deeply human: satisfying shoppers. By predicting what customers want before they walk in the door, retailers create a shopping experience that feels tailored, timely, and truly delightful.    

Implementation with Store Item Demand Forecasting Dataset

In this store item demand forecasting project, the following methodologies have been utilized to obtain the required results. 

  • Using a Rolling Mean for Demand Planning 
  • Using the XGboost model along with the Rolling Mean 
  • Utilizing inventory management rules 
  • Green inventory management
  • Optimized Procurement Management by using Python      

Other approaches include linear regression, ARIMA, and Light GBM. By developing a competent store item demand forecasting model, ML can effectively help in assigning the supply side of the demand-supply algorithm. 

Product Price Recommendation (Dynamic Pricing)

Dynamic pricing in e-commerce is a sophisticated practice that utilizes real-time data and algorithms to fluidly adjust prices based on market factors. Rather than static pricing, dynamic pricing takes into account variables like supply and demand, inventory levels, customer inclinations, seasonality, geography, time of day, and competitor pricing strategies. This flexible approach manifests in forms such as surge pricing, which increases prices when demand spikes, and personalized pricing, which customizes costs based on individual customers. 

Additional applications include segment-based pricing that targets specific demographics and time-based pricing with fluctuations based on hour or day. For instance, an online retailer may incentivize early sales with lower morning prices or capitalize on high demand for hot items by raising prices. When implemented strategically, dynamic pricing enables businesses to maximize revenue while providing customers with fair market-driven prices.

Mercari sought to provide pricing recommendations to vendors on its platform. This presented a considerable challenge, as sellers may list virtually any item or assortment of products imaginable. The methods utilized in the Mercari Price Suggestion Dataset include Feature Engineering, Ridge Regression, LightGBM, and XGBoost. Utilizing these ML algorithms, a substantial quantitative dataset was developed.    


Machine Learning has an indispensable role in e-commerce. Retail managers can benefit immensely by using these datasets as actionable templates to further the ML approach to solve unique problems. Overall, ML can and does have a transformative role in e-commerce which must be utilized fully to remain competitive. Revolutionize your own journey across eCommerce, retail & more. Schedule your free Omind demo & see the power of AI-driven experiences. Please visit omind.ai to learn more. 


Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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