Artificial intelligence is the new pillar of innovation that is improving customer relationship management and e-commerce. Once a science fiction concept, AI is now a reality that is transforming sales, marketing, and many other business functions.
In customer relationship management (CRM), AI is revolutionizing how businesses store and analyze contact information, track customers throughout the sales process, and automate critical sales and marketing functions, streamlining workflows.
Generative AI, which leverages natural language processing (NLP) and large language models (LLM), can understand and generate human-language text, making it a significant force in technology and business this year. Insights generated by generative AI optimize business processes and are accessible to office workers of different roles and experience levels, for both professional and personal use.
Marketers, advertisers, and customer experience (CX) specialists are using generative AI to improve campaign effectiveness, enhance hyper-personalization, and build customer trust and loyalty. It allows them to process and understand large volumes of text and apply sentiment analysis to gain insights that improve customer success and boost CRM effectiveness.
AI also helps marketers, advertisers, and call center agents better understand consumer sentiment and perception toward the brand. It determines their likes and dislikes, according to SAS Martech Solutions Marketing Manager Jonathan Moran.
“This information helps them adjust their strategies and improve communications, service and support with customers,” he told CRM Buyer.
Advantages and Disadvantages of Conversational AI
One of the biggest marketing trends is AI-powered conversational marketing. Brands and businesses are using this strategy to communicate with their customers through personalized, real-time interactions. These interactions often take place through a combination of web, email, chat, messaging, and social media channels.
“Conversational AI offers cost, efficiency and productivity benefits to marketers,” Moran noted.
Automating customer responses across digital channels reduces costs and saves money in terms of resources and personnel. Combining conversational AI with other process automation technologies, such as robotic process automation (RPA), can increase efficiency and productivity by scaling simultaneous customer service and support interactions, he continued.
Moran acknowledged that the main drawbacks of conversational AI are customer misunderstandings and the resulting frustration. As automated technologies are used to converse and interact with customers, certain dialects, syntaxes, sentiments, and emotions cannot always be understood by these technologies.
“This can result in a poor customer experience, but can also lead to customer frustration, attrition and churn,” he admitted.
Better understanding and accepting AI-assisted agents
Misinformation and misconceptions about AI are common among workers and consumers. We asked Jonathan Moran to share his expertise on the benefits of this new technology and what steps can be taken to control its use.
CRM Buyer: How can conversational marketing be useful with a solid objective?
Jonathan Moran: Conversational marketing is more effective if an organization or brand has strong data management and a scalable and flexible analytics platform. The former ensures the availability of relevant and quality data, while the latter helps transform that data into better decisions.
How is AI marketing different from digital marketing?
Moran: I believe that conversational marketing, machine learning marketing, or algorithmic marketing (all based on AI) are just components of broader digital marketing strategies. As digital marketers evolve their practices and processes, integrating AI technologies and capabilities into those processes becomes a natural step in their digital marketing maturity.
Can AI be integrated into existing digital marketing without eventually eliminating human marketers?
Moran: My colleagues and I recommend following the concept of human intervention leading to leapfrogging (HITL). While people can use technologies like generative AI to get a head start, human supervision is still necessary.
AI helps marketers be more effective and efficient. With it, organizations can optimize their processes and improve their competitiveness.
So you don’t see these AI-powered advances as a threat to human workers’ jobs?
Moran: Just as automation in manufacturing has given rise to new occupations, AI will give rise to new jobs and careers. Of course, as AI evolves, so will marketing and advertising professionals. Adding AI skills to your portfolio will become increasingly important. Those who integrate AI into their roles will have an advantage over those who don’t.
Ultimately, AI will not replace humans. On the contrary, it will help them accomplish more.
Do you think AI training will ever surpass human capacity for creativity?
Moran: Despite all its promise, AI has its limits. It cannot feel or express emotions like a human would. It cannot innovate, invent, or add new knowledge. ChatGPT, for example, builds on existing information or summarizes it.
I believe that AI will never surpass human creativity. But it will be a powerful and important tool to augment and support human creativity.
What ethical issues are inherent in the use of AI in marketing?
Moran: The main ethical issues in marketing concern the biases that AI machines can impose. AI makes decisions based on data and algorithms, not human intuition. There are situations where a decision is made in an automated way that can be offensive because of age, gender, race, cognitive ability, or any other determining factor.
A few years ago, marketers and, to some extent, content providers used subliminal advertising techniques. Is the use of AI for predictive analytics a similar subterfuge?
Moran: I don’t think AI is intended to deceive customers in any way. The majority of consumers are aware that AI technologies are being integrated into digital marketing practices.
AI should be – and is – primarily used by brands to automate processes, both for efficiency and effectiveness. The result is typically a happier consumer, as their service and support issues are addressed quickly.
AI-related technologies, such as natural language processing (a technique based on sentiment and text analysis), help marketers and advertisers better understand consumers’ sentiment and perception of the brand, as well as their likes and dislikes. This information helps them adjust their strategies and improve customer communications, service and support.
What are the challenges of integrating AI into the creative aspects of content production?
Moran: The main challenge is authenticity. While there is no doubt that AI-powered content creation increases the efficiency and productivity of marketing and advertising departments, AI techniques such as generative AI can create images and content that include plagiarism, unintentional bias, and content that is not intended to be reused.
Often lacking the authentic emotion and unique perspectives of human-created content, AI-created content can feel inauthentic to both the brand and the consumer.
One way to avoid this lack of authenticity is to keep humans in the loop, especially creative professionals. With human oversight, errors can be minimized and AI can deliver on its great promise of helping marketers and advertisers hyper-personalize messages and offers, build brand loyalty, and improve campaign effectiveness.
What is the biggest threat AI poses to e-commerce?
Moran: The biggest threat today is fraud powered by AI technologies. Brands need to ensure that cybercriminals are not using AI to create fraudulent customer profiles that steal from brands through e-commerce platforms.
Additionally, if brands are using generative AI to create content for e-commerce sites, they need to ensure that approvals and safeguards are in place before that content (images and descriptions) goes live.
How can AI developers ensure ethical AI outcomes?
Moran: Three key considerations can help control AI behavior: data quality, oversight, and flexibility.
AI models are trained using data. Insufficient data or lack of proper reinforcement learning can lead to inaccurate or unethical decisions.
Organizations need a governance system with clearly defined owners and stakeholders for all AI projects. Then define the decisions they will automate without human intervention. AI models should be monitored and audited regularly to identify drift and unexpected operations.
To enforce policies, users must be able to select and adjust training data, control data sources, and choose how data is transformed. They must have the ability to modify AI models when they are incorrect or operate outside ethical boundaries.
