Artificial Intelligence (AI) is rapidly transforming almost every industry, including retail. Generative AI combines neural networks, Machine Learning, and statistical methods to produce original content such as text, images, videos, and music. Generative AI operates by learning from a dataset and then using this information to generate new data points that mimic the original input. Other than impressive images, it can also be employed to make predictions about any type of data, including sales and customer behavior data.
AI has profound implications for the retail sector, transforming operations, customer service, and business strategies. It enables retailers to anticipate customer needs, personalize marketing efforts, and enhance the shopping experience. Generative AI consulting helps implement cutting-edge solutions for businesses seeking advanced Artificial Intelligence capabilities: AI algorithms can predict buying patterns and customer behavior, offering invaluable insight. According to research from McKinsey, the implementation of generative AI could bring in an additional $2.6T to $4.4T to the global economy annually.
Let’s explore the applications of generative AI in retail and learn how using this technology can lead to increased profits and customer satisfaction.
How to leverage generative AI in retail
At its core, a generative AI is a pattern-seeking machine. Unlike humans who sometimes intuitively connect different concepts and ideas, such as linking the Christmas season to gingerbread, a generative AI is specifically designed to uncover these correlations. By analyzing a flow of data, a generative AI can identify connections that the human mind overlooks and generate new content based on those associations. Here are some examples of how generative AI can benefit retailers from product design to e-commerce assistance:
1. Speed up product design. Generative AI can speed up product innovation and facilitate the faster launch of new products onto the market. Designers can use it to explore new design concepts, generate innovative ideas, and streamline their workflow. While it can take days to produce a visualization of a concept, an AI tool can generate one from a description in minutes. This allows designers to concentrate more on the creative aspects of their work, while AI takes care of repetitive or time-consuming tasks. At the same time, it allows them to incorporate up-to-date analytics on trends, consumer preference by category, and other factors that translate into particular designs. Ultimately, this technology has the potential to greatly improve efficiency, productivity, and creativity in the fashion and retail industries.
2. Create optimal store layouts. AI systems can weigh customer traffic patterns, product placement, and purchasing behaviors to generate store plans that improve sales performance. Generative AI for retail can also optimize shelf space in stores using the data of sales, product turnover rates, and customer preferences to recommend the best allocation of products on the shelves. It can further help determine the specific needs of a particular store and choose an optimal layout.
3. Generate product descriptions. Automating the process of generating unique and engaging product descriptions is another task generative AI aces. It can create unique, compelling, and SEO-optimized descriptions in mere seconds. Human writers can lack context or omit the details that might seem inconsequential to most people but are the main point of interest to the customer. Accurate e-commerce descriptions are important as they set expectations, build trust, and ensure customer satisfaction. They also provide details about the product's features, specifications, and usage, helping customers find the exact product they are looking for faster and make informed decisions. Additionally, accurate descriptions reduce product returns and complaints by preventing disappointment or surprise.
4. Edit product visuals. Creating variations of product images becomes easier with the help of an AI assistant. It can automatically change the background, color, or angle without the need for manual editing. This technology can save time and resources for retailers, enabling them to quickly update and customize their product visuals to meet the needs and preferences of their customers at a reduced cost.
Generative AI can be used in the fashion industry to help customers visualize how a specific product will look on their body type. Human bodies are not easily categorized into 2D sizes, and online shopping remains a gamble. This is a significant barrier that currently prevents 43% of people from shopping online. Unlike augmented reality technologies that offer similar features, generative AI can accurately simulate how a specific clothing design will fit individual proportions with a simple picture.
5. Develop effective marketing strategies. Generative AI models can help create effective marketing strategies by analyzing consumer responses to past campaigns and generating insights for future campaigns. These models can also generate content for social media posts or advertisements to ensure messaging resonates with the target audience. AI algorithms also enable retailers to create personalized promotions targeting individual customer needs. AI can adapt these campaigns in real-time based on customer feedback and engagement. This level of personalization and adaptability can significantly increase campaign effectiveness and return on investment.
6. E-shopping assistance. After processing an individual customer’s past purchases and browsing history, an AI model can provide personalized recommendations for products. This personalized approach makes the customer feel valued and uniquely understood. Not only does it save the customer time on viewing the items that are not to their taste, but it also can help create identification with the brand. Less shopping fatigue and more attractive items on display can mean more sales and long-term customer relationships.
7. Provide customer support. AI-powered chatbots can handle many customer requests simultaneously and provide instant and accurate responses. They operate 24/7, ensuring customers are receiving support at all times. Chatbots are programmed to resolve common queries, guide shoppers through purchasing, and offer personalized product recommendations. This not only improves customer engagement but also reduces response time, enhancing customer satisfaction significantly.
Current use cases from the major retailers
Generative AI is continuously evolving, leading to the exploration and implementation of new and exciting use cases. Note that the projects mentioned here have all been very recently released. Generative AI in retail still holds immense potential and offers a world of exciting opportunities to be discovered.
eBay AI product description tool
eBay, an online marketplace, has released a plug-in that enables sellers to automatically generate text for their item descriptions based on product attributes. This technology aims to simplify the listing process and reduce the time and effort required for sellers. The AI can suggest descriptions for products, which is one of the most difficult aspects of selling on eBay. On the customers' end, a precise description of the product allows for accurate categorization and helps avoid misunderstandings.
The company has received very positive feedback on the metrics it has already collected. eBay expects improvements in metrics such as acceptance rates, editing rates, and conversion rates to assess the effectiveness of the AI-generated descriptions.
Leading e-commerce giants Amazon, Alibaba, and Aliexpress are actively involved in developing and testing their AI-powered recommendation systems. These cutting-edge systems aim to enhance the accuracy of the recommended goods section beyond all previous standards. Additionally, they can be combined with a conversational bot that understands customer preferences. Increased accuracy can be achieved through greater flexibility compared to traditional search engines and the ability to extract information from images. By utilizing AI algorithms, these platforms strive to provide highly personalized and targeted recommendations, ultimately leading to greater customer satisfaction and increased sales.
Read more: Generative AI use cases and applications
Downsides and challenges of implementing AI tools
While the potential benefits of Generative AI in retail are significant, retailers must consider several challenges when implementing this technology.
Data quality and tracking systems
AI models need large amounts of well-structured and properly stored data to function effectively and generate meaningful results. Implementing an AI system can be a significant challenge for a business's IT systems. When implementing generative AI tools in a retail business, the process can reveal underlying issues and inefficiencies in various areas. For example, by analyzing customer and sales data, the AI tool may uncover inconsistencies in the way data is stored or flaws in the integration of different systems. Often, the history of a business's growth leaves behind imperfections that never become significant enough to be a priority.
This could lead to problems such as inaccurate inventory management, inefficient supply chain processes, or ineffective customer targeting. However, by working with a technology consultant during the implementation, businesses have the opportunity to identify and address these issues, ultimately improving and streamlining their overall business processes.
While most retailers already have data on sales and item tracking readily accessible, implementing technologies like optimizing store layouts will require the development of a new system to track foot traffic. Systems like these are rarer and will require additional time to be installed and gather enough data.
Data governance and security
Data governance refers to the overall management of data within an organization. It involves establishing policies, procedures, and controls to ensure the accuracy, integrity, and security of data. In the context of Generative AI in retail, data governance becomes essential since the integration of AI tools only adds pressure to the systems.
By implementing data governance practices such as data cleansing, standardization, and validation, retailers can ensure that the data used by AI systems is reliable and trustworthy. This, in turn, improves the accuracy and effectiveness of AI-driven insights and recommendations.
Another reason why data governance is critical in AI-powered retail is to address privacy and security concerns. With the large amount of customer data being collected and analyzed by AI systems and robust security measures are needed to protect this sensitive information. Data governance helps establish policies and controls for data access, storage, and usage, ensuring that customer data is protected from unauthorized access or breaches. Personal data leakage can be extremely damaging to a business's reputation. By implementing strong data governance practices, retailers can build trust with their customers and maintain secure operations.
In conclusion, although there are hurdles to implementing Generative AI in retail, they can be turned into opportunities to improve essential business processes.
Featured case study: data analytics solution for a subscription management provider
Let's delve into how N-iX assisted Cleverbridge in adopting AI tools for their email subscriptions and the resulting benefits to the company.
Cleverbridge, a company headquartered in Germany, provides complete e-commerce and subscription management solutions for generating revenue from digital products, online services, and SaaS in various industries. The challenges include reducing the churn rate and maximizing the Customer Lifetime Value for end clients. To address these challenges, the company was looking to adopt machine learning techniques to predict subscription churn and suggest the most effective communication strategy. Additionally, they aimed to introduce Generative AI to improve the process of marketing communication content creation.
N-iX took on an approach to guide the company's AI adoption by designing and implementing ML systems to facilitate the implementation of ML models. We leveraged MLOps to enable 24/7 operations and also built LLM-powered applications. N-iX engineers implemented a multi-tenant Machine Learning solution for predicting subscription churn for end clients. The solution includes algorithms for suggesting and implementing the communication strategy. It also leverages MLOps best practices to create a Machine Learning system. Previously, N-iX has also cooperated with Cleverbridge to create an efficient data strategy and successfully transferred the client's data to a modern custom data platform for the data reporting solution.
The generative AI solution for content creation increased the speed at which the company could produce new marketing materials. The speed and simplicity are also combined with additional analytics and extended tool selection for managing campaigns. The algorithms divide the email campaigns into segments. For each segment, the algorithm will adjust the intensiveness of the campaign. If users are canceling their subscriptions more frequently than desired, the algorithm will send special offers, personalized content, and re-engagement emails to prevent unsubscribing.
The machine learning solution incorporates new data to update the churn prediction model for improved accuracy over time.
The value N-iX brought by developing a generative AI solution
- Automated marketing interventions for segments produced based on subscription churn propensity.
- Data-driven way to improve customer retention.
- Accelerated content creation and reduced manual labor involvement content writing process.
- Content tailored to specific clients, ensuring a personalized experience.
- 24/7 operations and self-tuning of the ML system.
As the retail industry continues to evolve, it is clear that Generative AI will play a crucial role in shaping its future. By embracing this transformative technology, retailers can unlock new insights, deliver personalized experiences, and forge stronger connections with their customers. The possibilities are endless, and with each new use case and implementation, the retail landscape is set to be redefined by the power of generative AI.
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