" The purpose of generative AI isn't intended to replace human talent but to augment and assist it. On average, 46% of working hours
in retail across frontline and corporate roles could be enabled by this technology. Generative AI has internal and external applications spanning Strategy, Data & Analytics, Merchandising, Product Development,
Supply Chain, Stores, Finance, and HR.” (Accenture, 2024)
Generative AI is creating new opportunities for enhancing performance and creativity for various roles in marketing, sales, IT, and services. The eCommerce industry is always adopting new technologies, and we see many leaders in this sector using machine learning systems.
Big companies such as Amazon and Walmart are already using generative AI solutions widely to provide highly customized online experiences and to optimize their order management and supply chain processes. For example, Walmart, now enables customers to reorder their favorite grocery baskets through Voice Order Services and Text to Shop which lets customers text Walmart’s AI agents directly to search for products and place orders.
Popular marketplaces such as eBay also started using Generative AI to produce better, more accurate product content from private sellers, improving the overall customer experience.
Amazon also just announced the opportunity for marketplace sellers to create custom product listings using generative AI, simply by using product URLs from their own branded websites.
The potential of Generative AI solutions for eCommerce businesses covers a range of important applications that are becoming common game changers, increasing workforce productivity, efficiency, and customer experience:
a) Online experience hyper-personalization
Generative AI solutions can support the human creative process and offer personalized and engaging experiences based on consumer data. It can enhance and optimize product recommendations and enable product search based on consumers’ preferences, behavior, and intent.
Generative AI tools like Salesforce Marketing Cloud or Mirakl’s suites of connected services enable every eCommerce player to create unique, tailored, and brand-aligned content for their customers.
I personally think the biggest potential of generative AI is in ‘personalization’. For the first time in history, online companies can truly serve the needs of their customers, based on their individuality and personal shopping goals. However, this potential also comes with ethical challenges.
Creating ‘virtual’ content amid the growing risks of deep fakes will require brands to be more transparent about the sources of any published content. In a world where one’s virtual identity and presence becomes inevitable, it is important to stress the difference between the real physical world and the creative virtual spaces that we, as consumers, are presented with. Also, in eCommerce, reliable and verified product search is essential for customer experience.
Generative AI can improve search, but also has the potential for misleading results if the data sources are not properly checked or if the search results are not accurate. This is especially important for sensitive consumer products or highly specialized, precise B2B businesses. The responsibilities of large online players will be in making the right choices when selecting data sources to train their machine learning models, while assigning pre-approved ratings and evaluation criteria. Business leaders will have responsibilities in making those choices, rather than machines.
b) Marketing automation
Generative AI is also transforming how marketers create and deliver content to their customers. According to a recent Salesforce report, half of the marketers surveyed are already using or testing generative AI, and another 22% plan to do so soon. Some of the common applications of generative AI for marketers are:
- Basic content creation (76%)
- Copywriting (76%)
- Creative thinking inspiration (71%)
- Data analysis (63%)
- Image generation (62%)
Marketers believe generative AI will positively change their role, allowing them to focus more on strategic work and less on operational tasks. On average, 70% of marketers expect generative AI to eliminate boring, repetitive work and free up time for strategic planning. They also estimate generative AI will save several hours of work per week, which amounts to over a month per year.
The enormous potential of AI for creativity will enable marketers to deliver highly relevant, personalized, and engaging content with virtually unlimited variations and testing. Delivering optimized content that matches the consumers’ interests and needs in real-time will have a significant impact on business performance and how businesses plan and invest in performance marketing.
Furthermore, predictive analytics and media mix modeling (MMM) are already using the power of AI systems. MMM is a data-driven approach that helps marketers optimize media spending and design more effective marketing campaigns.
Data science plays a key role in MMM by managing and analyzing large datasets from diverse media channels. Techniques like regression analysis, econometric modeling, and predictive analytics allow marketers to derive useful insights from complex data sets. These insights are essential for understanding consumer behavior and optimizing marketing strategies.
Thanks to machine learning and artificial intelligence, these analytical tools can be improved by processing substantial amounts of data to identify trends and predict the effectiveness of different media channels.
One of the most impactful improvements in MMM driven by AI is real-time analysis. Instead of relying on historical data, marketers can now make decisions and optimize campaigns based on current market conditions. AI algorithms can assess consumers’ sentiment in real-time and provide more personalized marketing suggestions.
The other important improvement is the ease of compliance with new GDPR and privacy regulations. Traditional marketing attribution is no longer relevant. In a world where GDPR and privacy regulations have limited the traditional cookie-based attribution, it becomes important for online brands to find ways to optimize their investments and increase return on ad spend (ROAS). With the integration of AI and ML in MMM, there is an increased attention to data privacy and ethical use of consumer information. Advanced data encryption, aggregated data, and anonymization techniques help ensure compliance with data protection laws like GDPR and balance effective marketing with consumer privacy.
c) Data democratization & performance analysis
One of the key advantages that online players have is data, but not just data as a resource by itself, but data used in a productive way to analyse historical performance and predict future performance. According to the latest Google Cloud 2024 Data and AI Trends Report, almost 75% of data decision makers anticipate that insights will be more widely accessible in 2024.
Google, 2024
84% of them think that generative AI will help their organizations get insights faster. This is why it is essential for companies that want to maximize the potential of data availability across their entire organizations to introduce the right data storage and data monitoring systems.
How to connect the performance of sales departments to the analysis of marketing departments, to the optimization of supply chain programmes. Using data to tell a story is what will make the difference in businesses that can stay ahead of the curve by meeting consumers’ needs through better customer experience, better products, better fulfillment solutions and better customer service.
Creating or outsourcing machine learning systems, feeding them with accurate, controlled and verified data (structured and unstructured) is one of the bases to drive actionable data analysis and to link performance to meaningful actions. Creating refined customers’ segments, understanding where the biggest value comes from and where the gaps are, is what creates competitive advantage for companies in an online world that becomes increasingly crowded with new players globally.
Creating the required flexibility to develop new online experiences, new loyalty programmes, new products based on insights is what keeps companies like Amazon and Alibaba at the forefront of online retail. In fact, Amazon uses AI technology to analyse all their collected data. This technology enables personalized recommendations, fraud detection, inventory management, supply chain optimization, and customer service improvements to create added value along the whole value chain of the company.
d) Customer service personalization and automation (CRM)
Providing quality customer service is more challenging in the post-pandemic world. Customers demand faster service and loyalty rewards from companies.
In fact, 72% of consumers say they will remain loyal to companies that provide quicker service. However, 78% of service agents say they struggle to balance speed and quality, up from 63% since 2020.
Generative AI is a game-changer for customer service and CRM. It can create relevant and personalized responses to customer queries, improve productivity and efficiency, and reduce costs. Customers and service professionals are keen to see how AI-powered customer services will shape their future experience.
Here are some of the ways AI can enhance customer service:
- AI-powered chatbots: Chatbots can handle simple customer inquiries, provide instant responses, and assist with tasks such as order tracking, product recommendations, and troubleshooting. They can also direct complex questions to the most skilled human agent. Chatbots can work across different channels and are available 24/7, reducing response times and increasing customer satisfaction.
- Natural Language Processing (NLP): NLP enables AI systems to understand and interpret human language. It has powerful applications in analyzing customer sentiment, satisfaction, and needs. NLP is the foundation for building and improving chatbots, personal assistants, and sentiment analysis tools to deliver personalized and relevant customer service experiences.
- Sentiment Analysis: AI-powered sentiment analysis tools monitor and analyze customer feedback, reviews, and social media interactions to gauge customer sentiment. This helps companies identify areas of improvement and respond to customer concerns.
- Personalized Recommendation Systems: AI-driven recommendation systems analyze customer purchase history and preferences to provide personalized product recommendations. These systems have important applications in merchandising strategies with essential cross-selling and upselling opportunities.
- Predictive Analytics: AI-based predictive analytics uses customer data to predict consumer needs, behavior patterns, and potential problems. This helps businesses proactively address customer concerns, optimize resource allocation to maximize revenue, and personalize customer interactions to drive expected actions.
- Self-Service Solutions & FAQs: AI-powered self-service content solutions, such as customer service knowledge bases or FAQs, leverage natural language processing to detect, understand customer queries and provide relevant information or troubleshooting steps.
- Intelligent Re-routing: AI-based intelligent re-routing systems can quickly analyze incoming customer queries and route them to the best service department with the relevant knowledge to address them. Customers can quickly be connected with the best service representatives who can address their needs in a targeted way.
According to Salesforce, the percentage of service decision makers who report using AI has increased by 88% since 2020. Some of the benefits that AI systems can bring to customer service agents across online organizations are:
- Higher productivity: AI can act as an assistant integrated in an agent’s workflow, increasing productivity by 14% for support agents.
- Service efficiency: AI in customer service can give customer service workers smart recommendations across knowledge bases, conversational insights, and customer data. In fact, 63% of service professionals say AI helps them serve their customers faster.
- Personalized interactions: AI systems can quickly collect and populate customer-specific information in real-time, making it easier for human agents to deliver personalized service experiences if needed.
- Operational efficiency: AI can analyze customer calls, emails, and chatbot conversations to determine the signs that a customer is likely to escalate an issue, the time it will take to resolve an issue, and more. These insights help find new ways to improve the customer experience and to better prepare for answering customer needs.
- Improved job satisfaction: AI allows agents to eliminate repetitive, time-consuming work and focus on situations that require creative problem solving, social intelligence, and complex critical thinking. Not surprisingly, 79% of IT leaders already report generative AI helps reduce team workload and burnout.
- A proactive, post-interaction service experience: AI can draw information from customers’ purchase history and marketing data to surface proactive actions for agents to take. For instance, AI bots can let customers know that it’s time to renew a subscription, remind them about booking a new appointment, or that a product upgrade or discount is available.
AI solutions available on the market now offer conversational, predictive, and generative capabilities to provide appropriate answers and create smooth customer-agent interactions. Service agents can be empowered to deliver personalized service and reach resolutions faster than ever. It is therefore no surprise that AI technologies can have a significant impact on how companies manage their CRM (customer relationship management) efforts.
e) Supply chain management and inventory optimization
The Covid crisis has increased the costs and challenges of fulfilling and delivering orders globally, affecting ecommerce performance. To reduce costs and increase efficiency, ecommerce companies need data and insights to improve their supply chain programmes, optimize their delivery routes, and reduce touch points.
Free shipping and returns are convenient for customers, but costly for the industry: over $200B in the US and 7B GBP in the UK Fashion Industry. Ecommerce businesses need to find more efficient and sustainable suppliers, optimize delivery routes, and reduce inventory waste.
AI systems can help by managing inventory across locations, planning for full loads’ deliveries, and adjusting inventory stock levels. Robotics process automation (RPA) is already widely used to help in this area and companies like Amazon use Collaborative robots to manage inventory storage. AI systems can also help ecommerce businesses to respond to new and stronger regulations on sustainability and carbon footprint, find early solutions to promote sustainability, reduce waste and cut emissions.
This is a great opportunity for businesses that invest in this area. AI systems can also help reduce purchase returns by providing better product ‘fit predictors’, more accurate product descriptions, and better AI customer service assistants.
f) New product development
Data and AI are transforming product development in the fashion industry, enabling companies to deliver products that meet consumer demands in real-time. Companies like Inditex and Yoox are leading the way, using AI to create targeted, creative, and limited inventory collections that respond to consumers' needs, eliminating the risk of overstocking.
How do they do it? It all starts with a well-established production capacity, quick turnarounds, and efficient routes to market. But the real magic happens when AI is used to leverage data for an efficient product 'feedback loop.' AI systems can analyze everything from packaging sizes to color palettes, providing insights into consumer trends and predicting future needs. This information is then used to inform new product development.
Some of the exciting applications of AI in product development are:
- Trend Analysis: AI systems can gather and analyze vast amounts of data, providing insights into consumer preferences, cultural choices, and emerging behaviors. They can also monitor social media platforms and industry-specific news websites in real-time, tracking hashtags, mentions, and engagement metrics to provide insights into which trends are gaining traction. And with predictive analytics, AI algorithms can use historical data to predict future trends, helping designers and retailers make proactive decisions.
- Generative Design: Generative AI is a branch of AI that focuses on teaching robots to create original and innovative material. Unlike traditional AI, which functions according to preset rules, generative AI can learn from data and develop content on its own. This technology uses complex algorithms and neural networks to identify patterns and generate outputs that are similar to human creativity. Generative design is a powerful approach to product development that leverages algorithms and computational power to explore numerous design possibilities and optimize outcomes. It enhances product development by providing designers with a powerful tool to explore, optimize, and innovate in the early stages of the design process. And with the ability to rapidly explore a vast design space, considering a wide range of parameters and constraints, designers can consider innovative solutions and uncover design possibilities that may not be immediately apparent through traditional methods.
- Demand Forecasting: AI tools can identify complex patterns and relationships within historical sales data, enabling more accurate predictions of future demand. They can also perform predictive analytics, analyzing various factors that influence demand, such as seasonality, market trends, and economic indicators. And with the ability to process and analyze real-time data streams, AI tools can provide up-to-the-minute insights into changing market conditions and consumer behavior.
- Sustainability: AI algorithms can analyze and recommend sustainable materials for product development, considering factors such as renewable resources, recyclability, and environmental impact. AI can also optimize energy consumption in manufacturing processes and facilities, reducing energy waste and lowering greenhouse gas emissions. And with the ability to minimize waste in production processes, AI can help reduce the environmental impact and lower costs associated with waste disposal.
AI is revolutionizing product development, providing powerful tools for trend analysis, generative design, demand forecasting, and sustainability. By harnessing the power of data and predictive analytics, fashion companies can stay ahead of the game, delivering products that meet consumer demands in real-time.
