These machine-learning use cases in retail are sure to grab your attention!
Nowadays, data proves to be a powerful pushing force in the industry. Big companies representing diverse trade spheres seek to use the practical value of the data. Thus, data has become of great importance for those willing to take profitable decisions concerning the business. Moreover, a thorough analysis of a vast amount of data allows for influencing or manipulating the customer's decisions. Multiple information flows, along with communication channels, are used for this purpose.
The sphere of retail is developing rapidly. Retailers manage to analyze data and create a peculiar psychological portrait of a customer to learn their sore points. But unfortunately, a customer tends to be easily influenced by the tricks developed by retailers.
Here are some machine learning use cases in retail, created for you to be aware of current trends and tendencies.
1. Recommendation engines:-
Recommendation engines proved to be of great use to retailers for customer behavior prediction. Retailers use recommendation engines as one of the main leverages of the customers' opinions. Providing recommendations enables retailers to increase sales and dictate trends.
Recommendation engines do a great deal of data filtering to get insights. Usually, recommendation engines use either collaborative or content-based filtering. The customer's past behavior or product characteristics will be considered.
Besides, various data types, such as demographic data, usefulness, preferences, needs, previous shopping experience, etc., go via the past data learning algorithm. Then the collaborative and content-filtering association links are built.
The recommendation engines compute a similarity index in the customers' preferences and offer the goods or services accordingly. The up-sell and cross-sell recommendations depend on the detailed analysis of an online customer's profile.
Example:
Netflix's recommender system uses ML algorithms to suggest movies or TV shows according to a user's watch or search history.
2. Market basket analysis:-
Market basket analysis is a traditional tool of data analysis in retail. Retailers have been making a profit out of it for years. This process mainly depends on the organization of a considerable amount of data collected via customers' transactions. This tool may predict future decisions and choices on a large scale.
Knowledge of the present items in the basket, along with all likes, dislikes, and previews, is beneficial for a retailer in layout organization, price making, and content placement. The analysis is usually conducted via a rule-mining algorithm.
Beforehand the data undertakes transformation from data frame format to simple transactions. A specially tailored function accepts the data, splits it according to some differentiating factors, and deletes useless. This data is input. On its basis, the association links between the products are built.
It becomes possible due to the association rule application. The insight information largely contributes to the improvement of the retailers' development strategies and marketing techniques. Also, the efficiency of the selling efforts reaches its peak.
3. Price optimization:-
Having the right price both for the customer and the retailer is a significant advantage brought by optimization mechanisms. The price formation process depends not only on the costs to produce an item but also on a typical customer's wallet and the competitors' offers. The tools for data analysis bring this issue to a new level of approach.
Price optimization tools include numerous online tricks as well as secret customer approaches. The data gained from the multichannel sources define the flexibility of prices, taking into consideration the location, an individual buying attitude of a customer, seasoning, and the competitors' pricing.
The computation of the extremes in values and frequency tables are the appropriate instruments to make the variable evaluation and perfect distributions for the predictors and the profit response.
The algorithm presupposes customer segmentation to define the response to changes in prices. Thus, the costs that meet corporate goals may be determined. Retailers can use the real-time optimization model to attract customers, retain attention and realize personal pricing schemes.
Example:
Apotek Hjärtat is the most significant private medical supply chain in Sweden, which employs ML and AI technologies to offer competitive prices compared to their competitors, both offline and online.
4. Inventory management:-
Inventory, as it is, concerns stocking goods for their future use. Inventory management, in turn, refers to storing goods to use them in times of crisis. The retailers aim to provide a suitable product at the right time, in a proper condition, and at an appropriate place. In this regard, the stock and the supply chains are deeply analyzed.
Powerful Machine Learning Algorithms and data analysis platforms detect patterns and correlations among the elements and supply chains; the algorithm constantly adjusts and develops parameters and values and defines the optimal stock and inventory strategies.
The analysts spot the patterns of high demand and develop strategies for emerging sales trends, optimize delivery and manage the stock by implementing the data received.
Example:
Costco is an American multinational corporation that uses machine learning to maintain sustainable and viable fresh foods and products. As a result, they have increased their productivity by producing more fresh food than was required.
5. Merchandising:-
Merchandising has become an essential part of the retail business. This notion covers a vast majority of activities and strategies that can be aimed at increase of sales and promoting the product.
The implementation of the merchandising tricks helps to influence the customer's decision-making process via visual channels.
Rotating merchandise helps to keep the assortment always fresh and renewed. Attractive packaging and branding retain customers' attention and enhance visual appeal. In this case, a great deal of data science analysis remains behind the scenes.
The merchandising mechanisms go through the data picking up the insights and forming the priority sets for the customers, taking into account seasonality, relevancy and trends.
6. Lifetime value prediction:-
In retail, customer lifetime value (CLV) is the total value of the customer's profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as they are less predictable than costs. Two significant customer methodologies of lifetime predictions are made by direct purchases: historical and predictive.
All the forecasts are made on the past data leading up to the most recent transactions. Thus the algorithms of a customer's lifespan within one brand are defined and analyzed. Usually, the CLV models collect, classify and clean the data concerning customers' preferences, expenses, recent purchases, and behavior to structure them into the input.
After processing this data, we receive a linear presentation of the possible value of the existing and potential customers. The algorithm also spots the interdependencies between the customer's characteristics and their choices. The statistical methodology helps identify the customer's buying pattern until they stop making purchases.
Data science and Machine Learning ensure the retailer's understanding of his customer, the improvement in services, and the definition of priorities.
7. Fraud detection:-
Detecting fraud and fraud rings is a challenging activity for a reliable retailer. The main reason for fraud detection is the tremendous financial loss caused. And this is only the tip of the iceberg. The conducted profound National Retail Security Survey goes deeply into detail.
The customer might suffer from fraud in returns and delivery, abuse of rights, credit risk, and many other fraud cases that do nothing but ruin the retailer's reputation. Once being a victim of such a situation may destroy the customer's precious trust forever. Therefore, the only efficient way to protect your company's reputation is to be one step ahead of the fraudsters.
Big data platforms continuously monitor the activity and ensure the detection of fraudulent activity. Therefore, the algorithm developed for fraud detection should not only recognize fraud and flag it to be banned but to predict future fraudulent activities. That is why deep neural networks prove to be so efficient.
The platforms apply standard dimensionality reduction techniques to identify hidden patterns, label activities, and fraudulent cluster transactions. Using the data analysis mechanisms within fraud detection schemes brings benefits and somewhat improves the retailer's ability to protect the customer and the company as it is.
Final Thought:-
Machine learning can be applied to various uses in retail. By becoming an ML engineer, you too can use machine learning in the industry you want to profit. The importance of such technology in modern sectors is vast. You should consider participating in a study that enhances your knowledge of ML, AI, and Data Science as a subject. Succeeding in developing ML projects for various uses will improve your understanding. You may join Advance AI and ML Program to get an overview of the essential and highly recommended topics of Machine Learning.
