Reevaluating eCommerce Capabilities with AI

During COVID-19, customers were forced to adjust their purchasing behaviors, and retailers needed a quick solution to reach shoppers without a physical storefront. The urgency of the situation, however, didn’t leave time for thorough planning and strategic decision making that would ordinarily govern such sweeping shifts. 

The pandemic ultimately served as the catalyst for accelerated eCommerce growth, shifting online sales from a complement to brick-and-mortar to a standalone channel. Customers today expect fully personalized, high-quality service both in-store and online, so retailers seeking a competitive advantage in this newfound digital age must be more intentional about their eCommerce capabilities and omnichannel strategies. 

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have been key tools in helping retailers reimagine their eCommerce strategies. Various AI systems can now be leveraged to enable data-driven decision-making, refine automated communication, and personalize individual user experiences. Today, we’ll explore some different ways business leaders can optimize their eCommerce capabilities with AI technology. 

Personalization  

Analyzing customer data to generate personalized content is a tried-and-true strategy for boosting retail eCommerce sales. However, personalization strategies rely on historical data and market segmentation, which traditionally takes hours of analysis and is often too generalized. Businesses with outdated customer relationship management (CRM) systems may struggle to find meaning in the data, hindering their ability to better tailor the customer’s experience. 

Using advancements in AI, businesses can implement effective, hyper-personalized marketing strategies powered by measurable, AI-generated insights. ML and Generative AI (GenAI) analyze datasets to identify patterns in consumer behavior, enabling companies to better segment their target market, personalize recommendations, and dynamically price products. Today, over 65% of retailers plan to implement GenAI , and almost 50% of retail CEOs reported using GenAI to enhance their personalization strategies. 

Amazon, for example, is one brand that has had success leveraging AI personalization strategies. Amazon’s AI-powered algorithm analyzes purchase history and market data to improve personalized recommendations and dynamically price featured products, so shoppers are shown the best deals for products they would be interested in. With AI, Amazon has increased conversion rates by 25%, and about 35% of their total sales came from an AI recommendation. 

Customer Service Experience 

Customers today expect personal, convenient, and instantaneous customer service. Customer service reps excel at personalized support but are expensive to maintain and cannot guarantee 24/7 availability, so classic chatbots have been seen as a scalable, inexpensive alternative. Their limited, pre-written responses, however, are often irrelevant or inaccurate to the query. Before, customers either had to queue on hold for a human representative, listen to an impersonal chatbot, or solve the problem on their own, which left them more frustrated than they were before they contacted customer support.  

To gain a competitive advantage, retailers must consider leveraging AI technology to maximize the effectiveness of customer service while minimizing support-related costs as part of their larger eCommerce strategy. Conversational chatbots powered by NLP and ML interpret customer interaction and purchase history to improve accuracy and converse fluidly. AI chatbots essentially operate like digital, personal assistants who cater to each user’s unique needs. Businesses who implement conversational AI can expect to reduce average response time to 5 minutes as well as customer service costs by almost 30%.  

One successful example is Domino’s AI chatbot, DOM, which is designed to handle online orders and queries across various channels. The advanced ML models, backed with NLP technology, guide the chatbot to respond accurately and in an appropriate and natural tone. Chatting with DOM offers users a quick and convenient online ordering experience and helps Dominos increase customer satisfaction by 25% while reducing costs by 20%. 

Demand and Inventory Planning 

For a digital retailer, aligning inventory with demand planning is vital for achieving customer satisfaction and supply chain agility. Traditional planning methods, however, depend on thorough data analysis to match production with real-time customer demand. Supply chain disruptions in recent years—such as supplier delays, transportation challenges, and unpredictable consumer behavior — have made supply and demand more volatile than ever, further highlighting this need for a comprehensive data management system to ensure greater resilience. Without advanced technology, demand forecasting and inventory allocation can be inaccurate, ineffective, and time-consuming, making the supply chain more susceptible to stockouts or low turnover rates.  

Supporting your supply chain with AI-powered technology will help you mitigate the challenges posed by demand volatility and optimize your inventory management, improving end-to-end adaptability and responsiveness. AI essentially translates data into actionable insights, helping you better predict future trends and adjust inventory as demand fluctuates. A data-driven approach can reduce excess inventory, lower associated carrying costs, and avoid lost sales from delivery delay — guaranteeing that products are available when and where customers shop, ultimately driving customer loyalty.  

PepsiCo, for example, turned to AI during the pandemic to help monitor demand and improve their supply chain efficiency and resilience. With intelligent AI technology, PepsiCo generated helpful insights that enabled them to optimize inventory levels, proactively adapt to demand, and reduce risk of stock-out during COVID. PepsiCo recognized the impact of AI for supply chain management, and this enabled them to exceed past their pre-pandemic performance levels. 

Considerations for AI in eCommerce 

As with any new technology, extra precautions must be taken to ensure AI will be helpful – not harmful – in the long run. Businesses planning to re-evaluate their eCommerce capabilities with AI need to have change management procedures ready to ensure universal adoption.  

In fact, almost 80% of employees in the US feel they would be more comfortable with AI if they were better trained in its capabilities. Providing this support will guide employees through the implementation and ensure they properly and effectively use the technology. Also, almost 90% of internet users have experienced an AI hallucination, or an AI-produced response that, while grammatically correct, is inaccurate or doesn’t make sense. This could negatively impact the customer experience, possibly leading to abandoned carts or a loss of brand loyalty. In customer-facing applications, it’s especially important to set guardrails and internal standards to monitor and improve AI tools, ensuring users receive accurate and relevant information. 

Final Thoughts 

During the period of rapid growth eCommerce experienced during and following COVID, retailers scaled their online capabilities quickly out of necessity. Now that the acute needs seen during the pandemic have stabilized, companies should consider revisiting and improving upon their previous strategies. Leveraging AI technologies provides an opportunity to innovate and optimize eCommerce strategies to deliver enhanced customer experiences and new efficiencies driven by data analytics. 

At Clarkston, our industry experts will help determine your best fit eCommerce strategy and seamlessly integrate the AI technology you need to achieve your long-term operational goals. You can trust our consultants to guide you to eCommerce success through this digital age.  

Contributions by Bella Gordon