Artificial Intelligence in Retail

Retail is one of those traditional industries that appeared ages ago. But now, in the era of technology, it is developing like never before. More and more retailers choose to sell over the Internet. Especially during pandemics, people turn to online shopping, as it is safer, quicker, and more convenient.

As the competition grows, customer expectations rise, and the technology moves forward, the retail business is undergoing a profound transformation. To sustain performance and remain relevant, enterprises should take advantage of these changes. That’s why the importance of AI for retail can hardly be overestimated.
Retailers are hard-pressed with the demand for novelty. The need to differentiate themselves from the competition has never been greater. Consumers are looking for hyper-personalization while remaining vigilant about their privacy.
Some companies are already reaping the benefits of AI for retail by making each customer journey unique. As a result, expectations for the entire industry soar. Using AI in retail is going to become a must in the foreseeable future.
These pressures make the industry grow and explore new markets. However, retailers need to be flexible, swiftly responding to changes in market demands.


How AI is used in retail – potential use cases

In-Store Assistance and Automation

Description: Most  retailers work as they used to for decades. They still have in-store employees, paper tags, cashiers, etc. Although a store cannot operate without humans entirely, most of these roles can be handed over to artificial intelligence.  

Influence on business: AI opens the door to endless automation possibilities. Most of the manual and paper-based work can be eliminated and substituted by robots or intelligent machines. Smart shelf tags can replace paper ones. Even searching for products in the store can become easier with the help of computer vision and speech recognition technologies.

Benefits: Reduced time and costs, increased efficiency and customer satisfaction, increased security.

Complexity: 2 (below average)

Data needed: Visual and textual descriptions of products, a history of clients’ purchases, previous conversations with clients, graphic depiction of suspicious behavior and theft. 

Examples: cashier-free stores, smart in-store assistance, visual and voice search, in-store theft prevention.

Predictive Analytics

Description: Running a business is not an easy task, as it is full of many unknowns. A little bit more information about your customers, their tastes, and preferences can give you a significant advantage over your competitors. Luckily, there is artificial intelligence that can shed light on many things. 

Influence on business: Predictive analysis is a powerful machine learning tool, and it is used widely in ecommerce. One example could be the prediction of customer lifetime value. It is an indicator that shows how important and valued each customer is. With this knowledge, you can adjust the spendings on advertising and customer support accordingly. Another example would be customer churn prediction so that you will be able to act accordingly: make a discount or offer another product.

Benefits: Reduced costs, increased profits, optimized strategy, better advertising.

Complexity: 2 (below average)

Data needed: Financial data, clients’ personal information, a history of clients’ purchases.

Examples: Prediction of customer lifetime value, customer churn, future sales. 

Personalized Experience

Description: personalization is the key to success in many industries, including retail. More than 90% of shoppers would prefer brands that recognize, remember, and provide relevant offers and recommendations. Offering the same products and services to every customer would not work well  in acquiring both money and loyalty. 

Influence on business: Machine learning allows companies to segment customers into different groups and open the door for targeted campaigning. For example, offering a skateboard to a teenager is better than offering it to an older woman. Machine learning models can also analyze each customer individually and offer the most relevant products based on their previous purchases or wish lists.

Benefits: Increased sales, optimized advertising, increased clients’ satisfaction.

Complexity: 3 (average)

Data needed: Customers’ personal information, a history of purchases.

Examples: customer clustering, target campaigning, recommendation engine.

Customer Support

Description: A good shop does not only sell the right products, but it must also provide excellent support to help their clients. The speed and quality of customer service show clients how much they are important to a company and companies with loyal customers are more likely to succeed.

Influence on business: Machine learning can help with this task. Chatbots are a popular and convenient way to communicate with clients. They can provide helpful information, answer basic questions, provide assistance with a purchasing decision, and so on. Support ticket classification allows the company to address the appropriate department in case of problems and reduces the time needed to solve them.

Benefits: Reduced time, increased customer satisfaction.

Complexity: 3 (average)

Data needed: Previous conversations with clients, customers’ feedback, classified support tickets, FAQ, wiki, relevant articles.

Examples: chatbots, support tickets classification, feedback analysis, sentiment analysis.

Real-Time Optimization

Description: Retail is a dynamic industry with new products and services appearing all the time, new companies entering the market and ever changing customer preferences.. All of these affect the market demand and supply and it is crucial to detect any such changes to act accordingly. 

Influence on business: Artificial intelligence offers flexibility and adaptability. It allows the companies to control the situation on the market and adjust the strategy or prices accordingly. 

Benefits: Higher flexibility, quicker reaction to market changes.

Complexity: 4 (above average)

Data needed: Basic market properties (products’ elasticities and prices, demand, supply), information about competitors, consumers’ tastes and preferences, demographic and transportation information, a history of purchases.

Examples: dynamic pricing, competitors’ analysis, logistics, warehouses, and supply-chain optimization, identification of the market demand changes.

Fraud Detection

Description: As the popularity of online shopping grows, so does the opportunity for cybercriminals to scam online businesses. The number of ecommerce fraud attempts has been rising steadily at an alarming rate of 25% in 2020, which results in huge monetary and client losses. It is, therefore, vital to prevent any illegal activities and protect your company from bad actors. 

Influence on business: Artificial intelligence can effectively detect suspicious activity and notify security. Machine learning models can spot abnormal operations and block them. It is also often possible to identify the crime source i.e. the criminal,  location, and device.

Benefits: Reduced losses, increased security.

Complexity: 5 (high)

Data needed: A history of clients’ transactions, clients’ personal and demographic information, legal documents (laws, rules, regulations), a history with descriptions of detected fraudulent activities.

Examples: anomaly detection, prevention of suspicious transactions, risk of payment rectification calculation.

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How to start?

The impact of Artificial Intelligence in retail is becoming more and more pronounced. Nonetheless, the potential of this new technology is yet to be realized.  AI for retail can fundamentally redefine the industry. With its help, traditional cost structures can be reviewed and novel relationships with customers to be formed. If your company is going to use AI in retail, now is the right time to start your journey. 

Executives, who want to arm their organization with the new AI tools, should pay attention to the following three principles: 

  • Data is king. Before embarking on your data science for retail journey, determining the quality, quantity, and type of data is crucial. This way you will get an idea of what can be achieved. However, the absence of access to the required data doesn’t mean the end of your aspirations. You can still follow through with the AI use cases in retail you envisioned. Try finding public data sources or a business partner with the necessary data. Think of a strategy to start mining data valuable for AI in retail industry. This may include ‘digitizing’ some analog processes or creating a consumer-facing digital product to act as a data-harvester.
  • Set realistic goals. There is no need to choose the most complex retail AI use cases. Start small and release an application faster. You can build on it over time. The AI applications can be extended to scale step by step. Therefore, with time, your company will explore new capabilities and capture increasing value. 
  • Don’t let failures stop you. Implementing Artificial Intelligence in retail industry is a tricky business. You need to invest a lot of time and resources, tackle social stigmatization of the technology, employ interdisciplinary collaboration, etc. Failure should be embraced as an integral part of innovation, contributing to organizational learning. The “fail fast, fail early” mindset is the best strategy to keep time and cost investments in check.

Using AI in Retail: Summary

Machine learning algorithms grow in complexity, reliability, and performance. In addition to this,  fundamental tools become readily available. As a result, the Artificial Intelligence retail industry is getting more and more real.

Modern e-commerce, planning, and customer-relationship management platforms allow companies to accumulate huge structured datasets. Retailers have a lot of information about customer behavior and their operations. 

Moreover, businesses can rely upon external data sources: government censuses, social media platforms, financial institutions. Anything that lays a solid foundation for the successful development and deployment of Artificial Intelligence in retail business. 

Hardware advancements also contribute to the increased adoption of the new technology. Distributed storage and processing platforms, dedicated cloud storage facilities, processors optimized for machine learning reduce the cost of creating and deploying statistical algorithms. Therefore, you don’t need much capital to build a mature data and analytics system.


What does AI mean in retail?

AI allows businesses to be more efficient and meet higher expectations of consumers, who look for novel experiences.

How is Artificial Intelligence used in retail?

AI in retail examples includes automating manual and paper-based work, predicting customer lifetime value, using chatbots to communicate with customers, etc.

How is AI changing the retail industry?

Armed with the new technology,  companies can perfect their operations and offer customers highly personalized consumer journeys.

Become an AI-driven retail company.
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