The Next Frontier of Customer Engagement: AI-Enabled Customer Service

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Discover how AI-enabled customer service is revolutionizing customer engagement, offering personalized, proactive experiences, and driving value in the B2C landscape. (158 characters)

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AI in customer service, artificial intelligence, customer engagement, personalized service, proactive service, customer experience, digital transformation, chatbots, virtual assistants, machine learning, retail, financial services

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ai-customer-service, artificial-intelligence, customer-engagement, personalized-service, proactive-service, customer-experience, digital-transformation, chatbots, virtual-assistants, machine-learning, retail, financial-services

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The Next Frontier of Customer Engagement: AI-Enabled Customer Service

AI-enabled customer service is now the quickest and most effective route for institutions to deliver personalized, proactive experiences that drive customer engagement.

Engaging customers—and keeping them engaged—is a focal question for organizations across the business-to-consumer (B2C) landscape, where disintermediation by digital platforms continues to erode traditional business models. Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime.

Yet financial institutions have often struggled to secure the deep consumer engagement typical in other mobile app–intermediated services. The average visit to a bank app lasts only half as long as a visit to an online shopping app and only one-quarter as long as a visit to a gaming app. Hence, customer service offers one of the few opportunities available to transform financial-services interactions into memorable and long-lasting engagements.

Those customers are getting harder to please. Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect a consistent cross-channel service experience. And with cost pressures rising at least as quickly as service expectations, the obvious response—adding more well-trained employees to deliver great customer service—isn’t a viable option.

Companies are therefore turning to AI to deliver the proactive, personalized service customers want, when and how they want it—sometimes even before they know they want it. For transformed organizations, AI-enabled customer service can increase customer engagement, resulting in increased cross-sell and upsell opportunities while reducing cost-to-serve. In global banking alone, research from McKinsey conducted in 2020 estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year, of which revamped customer service accounts for a significant portion.

While a few leading institutions are now transforming their customer service through apps and new interfaces like social and easy payment systems, many across the industry are still playing catch-up. Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology. Customer service leaders face challenges ranging from selecting the most important use cases for AI to integrating technology with legacy systems and finding the right talent and organizational governance structures.

But done well, an AI-enabled customer service transformation can unlock significant value for the business—creating a virtuous circle of better service, higher satisfaction, and increasing customer engagement.

The Perils and Promise of AI Customer Engagement

Multiple converging factors have made the case for AI-based customer service transformation stronger than ever. Among the most important: increased customer acceptance of (and even preference for) machine-led conversational AI interactions. Meanwhile, related technologies such as messaging platforms are becoming more accessible, and customer behaviors are becoming more understandable with the relentless expansion of data pools institutions can collect and analyze.

Three Challenges

But challenges also loom. First, complexity. The COVID-19 pandemic acted as a major catalyst for migration to self-service digital channels, and customers continue to show a preference for digital servicing channels as the “first point of contact.” As a result, customers increasingly turn to contact centers and assisted-chat functions for more complicated needs. That raises the second issue: higher expectations. Customer confidence in self-service channels for transactional activities is leading them to expect similar outcomes for more involved requests. Businesses are therefore rapidly adopting conversational AI, proactive nudges, and predictive engines to transform every point of the customer service experience. Yet these moves raise demand for highly sought-after skills, generating the third challenge: squeezed labor markets that leave customer service leaders struggling to fill crucial roles.

How Leaders Fulfill AI’s Customer Engagement Promise

Leaders in AI-enabled customer engagement have committed to an ongoing journey of investment, learning, and improvement, through five levels of maturity. At level one, servicing is predominantly manual, paper-based, and high-touch. At level five—the most advanced end of the maturity scale—companies are delivering proactive, service-led engagement, which lets them handle more than 95 percent of their service interactions via AI and digital channels.

The most mature companies tend to operate in digital-native sectors like e-commerce, taxi aggregation, and over-the-top (OTT) media services. In more traditional B2C sectors, such as banking, telecommunications, and insurance, some organizations have reached levels three and four of the maturity scale, with the most advanced players beginning to push towards level five. These businesses are using AI and technology to support proactive and personalized customer engagement through self-serve tools, revamped apps, new interfaces, dynamic interactive voice response (IVR), and chat.

A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service.

What AI-driven customer service maturity looks like:

• Level 1: Manual and high-touch, based on paper forms and offered largely via assisted channels.
  • Reactive service, with the majority of interactions on human-assisted channels.
  • Paper use is still prevalent.
• Level 2: Partly automated and basic digital channels, with digitization and automation of servicing in assisted channels.
  • Reactive service, with limited self-servicing opportunities
  • Lower adoption of available self-service channels.
  • Lower availability of digital or straight-through-processing (STP).
• Level 3: Accessible and speedy service via digital channels, with self-servicing on select channels and a focus on enabling end-to-end resolution.
  • Somewhat proactive, but limited engagement.
  • Self-service channels such as mobile apps, interactive voice response (IVR) systems, and internet sites handle half of all interactions, and can support STP.
• Level 4: Proactive and efficient engagement deploying AI-enabled tech, with self-servicing enabled by proactive customer interactions and conversational user experience (UX).
  • Proactive, with high customer engagement on digital channels.
  • Self-service channels such as mobile apps, IVR systems, and internet sites handle 70-80 percent of interactions and can support most requests and transactions.
• Level 5: Personalized, digitally enabled engagement, bringing back the human touch via predictive intent recognition.
  • Engagement via service interactions that are personalized and proactive at the individual customer level.
  • Digital touchpoints drive service-based engagement, for example, via enhanced cross-selling and upselling.
  • More than 95 percent of service interactions and requests can be solved via digital and STP channels.

Toward Engaging, AI-Powered Customer Service

To achieve the promise of AI-enabled customer service, companies can match the reimagined vision for engagement across all customer touchpoints to the appropriate AI-powered tools, core technology, and data. The new model for customer service includes communicating with customers before they even reach out with a specific need, providing AI-supported solutions, and evaluating performance after the fact.

The Human Factor in AI-Supported Service

AI-powered does not mean automation-only. It’s true that chatbots and similar technology can deliver proactive customer outreach, reducing human-assisted volumes and costs while simplifying the client experience. Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience. A reimagined AI-supported customer service model, therefore, encompasses all touchpoints—not only digital self-service channels but also agent-supported options in branches or on social-media platforms, where AI can assist employees in real time to deliver high-quality outcomes.

Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent. For example, the system might flag that the customer’s credit-card bill is higher than usual while also highlighting minimum-balance requirements and suggesting payment-plan options to offer. If the customer calls, the agent can not only address an immediate question, but also offer support that deepens the relationship and potentially avoids an additional call from the customer later on.

AI Service in the Field: An Asian Bank’s Experience

Next-generation customer service aligns AI, technology, and data to reimagine customer service. That was the approach a fast-growing bank in Asia took when it found itself facing increasing complaints, slow resolution times, rising cost-to-serve, and low uptake of self-service channels.

Over a 12-month period, the bank reimagined engagement. It revamped existing channels, improving straight-through processing in self-service options while launching new, dedicated video and social-media channels. To drive a personalized experience, servicing channels are supported by AI-powered decision making, including speech and sentiment analytics to enable automated intent recognition and resolution. Enhanced measurement practices provide real-time tracking of performance against customer engagement aspirations, targets, and service level agreements, while new governance models and processes deal with issues such as service request backlogs.

Underpinning the vision is an API-driven tech stack, which in the future may also include edge technologies like next-best-action solutions and behavioral analytics. And finally, the entire transformation is implemented and sustained via an integrated operating model, bringing together service, business, and product leaders, together with a capability-building academy.

The transformation resulted in a doubling to tripling of self-service channel use, a 40 to 50 percent reduction in service interactions, and a more than 20 percent reduction in cost-to-serve. Incidence ratios on assisted channels fell by 20-30 percent, improving both the customer and employee experience.

Seizing the Opportunity

To leapfrog competitors in using customer service to foster engagement, financial institutions can start by focusing on a few imperatives.

• Envision the future of service, keeping customers and their engagement at the core while also defining the strategic value to be attained—for example, a larger share of wallet with existing customers? Expansion of particular services, lines of business, or demographics?
• Rethink every customer touchpoint, whether digital or assisted, together with opportunities to enhance the experience while also increasing efficiencies.
• Maximize every customer service interaction, to deepen customer relationships, build loyalty, and drive greater value over the customer’s lifetime.
• Leverage AI and an end-to-end technology stack, to provide a more proactive and personalized customer service experience that supports self-service and decision-making for customers as well as employees.
• Adapt agile and collaborative approaches to drive transformation, comprised of SMEs from different business and support functions of the organization.

Holistically transforming customer service into engagement through re-imagined, AI-led capabilities can improve customer experience, reduce costs, and increase sales, helping businesses maximize value over the customer lifetime. For institutions, the time to act is now.

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AI in Retail and Improving Efficiency

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Explore how artificial intelligence is transforming the retail industry, from personalized shopping experiences to supply chain optimization. (140 characters)

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artificial intelligence, AI, retail, customer experience, personalized shopping, predictive analytics, demand forecasting, supply chain optimization, automated customer service, fraud detection, AI analytics, data-driven decision making, dynamic pricing, ethical considerations, retail management

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artificial-intelligence, ai, retail, customer-experience, personalized-shopping, predictive-analytics, demand-forecasting, supply-chain-optimization, automated-customer-service, fraud-detection, ai-analytics, data-driven-decision-making, dynamic-pricing, ethical-considerations, retail-management

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Many consumers are aware of the profound transformation the retail industry is undergoing. By simply walking through your favorite stores, you can see that artificial intelligence (AI) technology in almost every area of retail. From enhancing the customer experience to optimizing operational processes and inventory management, AI has become a powerful tool for retailers looking to stay competitive in the dynamic retail landscape.

Better Customer Insights by Using AI in Retail

Companies are putting a heavy focus on using AI to help with the customer experience. Tractor Supply® CEO Hal Lawton stated that his company has “leveraged AI within its supply chain, human resources, and sales and marketing activities.” This company has a primary focus on customer service, so AI has aided Tractor Supply in improving their customer service.

Tractor Supply uses an AI-powered tech assistant known as “Gura,” which stands for great, uncover, recommend, and ask. Any store associate can use this tool to bring high-quality service to customers.

For example, imagine that a customer wants to find a type of dog food that helps with sensitive skin. A store associate can then use Gura to determine which foods would be the best fit for the customer, as well as find the inventory levels and price for a type of food in real time.

Personalized Shopping Experiences

One of the key advantages of integrating AI in retail management is its ability to offer personalized shopping experiences. AI algorithms analyze vast amounts of valuable customer data – including purchase history, browsing behavior, and preferences – to create individualized recommendations.

Amazon® excels in the use of AI technologies to create personalized customer recommendations. Its AI software uses machine learning to suggest products based on a customer's past purchases and the behavior of similar users. This software not only enhances customer satisfaction, but it also drives sales by presenting relevant items to shoppers based on business intelligence information.

Amazon’s new notice of “high return rate” also helps customers redirect their search to the customer reviews section of a product page to help customers determine if an item is worth purchasing or if they might end up returning it. This use of AI also helps to control Amazon’s reverse logistics and inventory systems.

Predictive Analytics for Demand Forecasting

Businesses can optimize their inventory management systems by using artificial intelligence for demand forecasting. AI analyzes historical sales data, market trends, and external factors. Then, it predicts future customer demand for products with a higher accuracy rate than traditional methods of forecasting.

A great example of this data analysis is the use of AI by Walmart® to predict which products will be in demand during specific seasons. This type of high-quality analysis allows for better stock planning and reducing instances of overstock or stockouts.

AI enhances Walmart’s daily supply chain workflows and anticipates cycles in demand, especially during peak events or unexpected events in customer traffic.

However, it’s important to note that Walmart needed a multiyear approach to collect enough data and develop the software to create flexible algorithms for its AI tool. Using AI was a large investment of time, money, and resources, but its use has paid off for Walmart by creating a more seamless shopping experience for customers.

AI-Driven Operational Efficiency

Artificial intelligence technologies can streamline various operational processes within an organization, which is particularly useful for leading retailers. The goal is to achieve better resource utilization, reduce costs, minimize errors, and increase productivity.

Supply Chain Optimization

Using AI for supply chain management provides real-time visibility and predictive capabilities. Retail businesses can use AI in their automated inventory management to track shipments, monitor inventory levels, and identify potential bottlenecks in the supply chain. As a result, operational efficiency improves and costs are reduced.

Swedish fashion retailer H&M® leverages AI to optimize its supply chains, analyze trends, and forecast demand, enabling the company to quickly respond to changing market demands and minimize lead times. The company’s AI tool is perfect for fast fashion; its software captures data from search engines and blogs so that AI algorithms can predict and analyze trends.

The data aids H&M’s leaders in making retail operations decisions on how much to buy, when to buy it, and where to place it in the stores. The AI data also helps to indicate when H&M should restock a popular item and determine the number of customers that would likely suit that item. By using AI for supply chain optimization, H&M can reduce its waste and make more sustainable decisions.

Automated Customer Service to Improve the Customer Experience

Automating customer service through AI and machine learning technology in the retail sector helps to increase customer satisfaction and improve customer relations. Chatbots or virtual assistants can handle routine customer queries, provide product information, and even assist in the purchase process.

While not all retailers have perfected the art of the chatbot – as many customers will attest – the use of artificial intelligence still helps to streamline customer service interactions. Chatbots can interact with customers using natural language processing to determine the problem with an item and initiate an exchange or return. Afterward, artificial intelligence and machine learning can analyze any customer feedback to eventually collect more data to better assist shoppers in the future.

AI tools can also store the browsing and purchase history for customers. This information is useful for both the retailer and the consumer.

Another way artificial intelligence can help consumers is by interacting with them directly while they shop. For instance, some stores use smart shelves, where customers simply scan barcodes to get suggested products or to learn more about an item.

Virtual Artist is an excellent example of how Sephora® uses an artificial intelligence tool to improve customer satisfaction. Customers use this tool to try different makeup products virtually. This tool enhances the online shopping experience, reduces the need for in-person trials, and cuts down on product returns, satisfying both Sephora and the customer.

AI-Enhanced Fraud Detection and Security

The retail industry also uses artificial intelligence to enhance the capabilities of systems designed to detect and prevent fraud. AI can be especially useful in cybersecurity and financial transactions.

Fraud Prevention through AI-Powered Solutions

Online and in-store fraudulent transactions are on the rise. Retailers, both big and small, must do what they can to ensure that all purchases are authentic. Artificial intelligence algorithms can analyze transaction patterns and identify potentially fraudulent transactions in real time, which helps to protect both the retailer and the customer.

Fraudulent transactions cost the retail industry over $100 billion in 2023, according to FOX Business. Many customers do not realize that the rise in the cost of items is a direct reflection of retailers losing money due to fraud.

Alibaba®, the Chinese e-commerce giant, uses AI to analyze user behavior and identify potential fraudulent transactions. Its AI tool safeguards the interests of both the company and its customers.

In-Store Surveillance

Security for brick-and-mortar stories has received a major boost from the use of AI-powered surveillance systems. These systems analyze video feeds in real time, flagging suspicious activities or behaviors.

In fact, facial recognition technology can identify known shoplifters, alert security teams, and enhance overall store security. The National Retail Federation (NRF) recently reported that internal theft costs retailers $110 billion a year, so this technology can reduce expenses for retailers.

Retailers like Walmart and Target® have invested in AI-based surveillance to minimize losses due to theft and to enhance the safety of their stores. AI-powered cameras at self-checkouts can monitor human behavior; these systems alert nearby employees of accidentally (or intentionally skipped) merchandise scans.

There’s a balance to be struck when retailers use AI.

The Rise of AI-Powered Retail Analytics

Modern-day retail is all about analytics. Data analysis can deliver valuable insights to enable business executives to make data-driven decisions, optimize retail operations, and improve customer retention.

Data-Driven Decision Making Through Harvesting Valuable Customer Data

AI-driven analytics provide retailers with valuable insights into customer behavior by allowing for data-driven decision-making and the analysis of customer interactions. Retailers can then optimize pricing strategies, marketing campaigns, and product placements for their customers, based on this data.

British retailer Tesco® uses AI to analyze customer data and personalize promotions, resulting in increased customer engagement and loyalty. The company argues that AI is key to maintaining their growth.

Dynamic Pricing

The ability to adjust prices in real time – based on consumer demand, competitor pricing, or even inventory levels – is a competitive advantage for many retailers. Applying AI allows retailers to use dynamic pricing to remain competitive and maximize their profits.

Delta Air Lines® and Amazon are great examples of the use of AI for dynamic pricing. Both use AI algorithms to adjust their prices, ensuring they capture the maximum value from transactions and produce the desired outcomes for the business.

Ethical Considerations and How the Retail Industry Should Use AI

Artificial intelligence in retail is flashy and fun. It is all over the news and crawling across every LinkedIn article page. But many companies need to understand that AI, while revolutionary and mimicking human intelligence, comes with its own challenges and ethical considerations.

Maximizing profits is the ultimate goal for retailers and their brands, but human concerns need to be a top priority as well. Privacy concerns, algorithmic biases, and the potential for job displacement are issues that need careful consideration. Striking the right balance between leveraging AI for efficiency and ensuring its ethical use is crucial for the sustainable growth of AI in the retail sector.

Online retail businesses often collect vast amounts of customer data for personalization, targeted advertising, and business insights. The ethical issue at stake here is how that data is collected, how it is used, and if customers have given consent.

Consumers need to be aware of what data retailers collect. Many people believe that they should be allowed to give or deny consent to the collection of their personal data by businesses.

Also, the use of AI automation for repetitive tasks and smart systems could lead to job displacement for certain employee roles. The ethical challenge is to ensure that the implementation of AI technologies is accompanied by efforts to reskill and upskill affected workers, creating a transition to the future of work.

However, there would need to be a decision on who is responsible for retraining employees. Should that responsibility fall on the retailer or should employees seek knowledge from other sources?

A Strategic Use of AI Is Needed

As technology continues to evolve, retailers should embrace AI strategically and address any ethical considerations along the way. The future belongs to those retailers who can harness the power of AI to meet customer expectations and drive operational excellence.

The Retail Management Program at American Public University

For students interested in retail management, American Public University offers an online bachelor of arts in retail management. The courses for this program are taught by experienced faculty members with a deep knowledge of the ever-evolving business world. There are also several courses that address the link between AI and retail management, including:

• RTMG301: Retail Innovations
• RTMG304: Digital Retail Inventory Management
• RTMG310: Digital Retail
• RTMG311: Digital Retail Technologies
• RTMG313: Digital Retail Strategies

In addition, many of our business programs, including the B.A. in retail management, are accredited by the Accreditation Council for Business Schools and Programs (ACBSP®). This specialty accreditation ensures that programs have been carefully evaluated by industry experts to ensure academic rigor and professional standards.