Enhance customer experiences with Generative AI

2 weeks ago 21

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The advent of Generative AI and its application in real-life use cases has been on the cards for a few years now. With advancements in chip manufacturing, neural networks and other deep learning technology, Machine Learning has slowly made way for Generative AI applications, powered by large foundation models that learn from a vast pool of data. However, this is not the impetus for demand.

As a lifelong student of the markets, I observed that use cases required to enhance customer experience with products, services and brands, became challenging to serve with traditional Machine Learning. This in turn caused innovators in academia and industry to develop Large Language Models and Foundation Models, to support and exceed customer expectations.

Customers today expect quick and insightful responses, highly personalized recommendations, expert advice, and the ability to generate media, all at their fingertips. A Google Cloud research found that 95% of retail decision-makers believe that generative AI will have an impact on customer experience. With the proliferation of services and products that are comparable in terms of technology and the solutions offered, customer experience has become a crucial differentiator and business driver.

Statista found that customer experience is considered a primary competitive differentiator by 44.5% of organizations globally. Use cases such as personalization, summarization, Q&A, and intelligent chat-bots, to improve customer interactions, increase employee productivity and ensure a positive brand recall.

In this article, I explore how generative AI can be leveraged to improve customer experiences, and the best practices across 3 use cases, that can help organizations of all sizes create a differentiated value proposition for themselves.

Let’s start with a ubiquitous use case, personalization. Whether it be content recommendations on your favourite streaming platform, or product recommendations on your preferred online stores, personalization, when done right, can help boost customer engagement.

For organizations, understanding individual preferences can help design targeted customer journeys. These targeted customer journeys can be designed to contain tailored marketing campaigns, product placement and support responses. According to Adobe, 72% of consumers worldwide express confidence in generative AI’s ability to enhance their customer experience.

Foundation models like BLOOM (BigScience Large Open-science Open-access Multilingual Language Model) and the Llama family of models from Meta are popular open-source choices for building personalization engines. By leveraging multiple foundations and smaller machine learning models in conjunction, organizations can create hyper-personalized applications, where responses are tailored to every unique individual.

The possibilities here are unbound, with smart marketers able to create actionable customer cohorts from personalized campaigns. Generative AI enhances customer data sets, providing actionable insights that inform business decisions. By identifying patterns and correlations in customer behaviour, organizations can make informed choices about product development, marketing strategies, and customer engagement efforts.

This data-driven approach leads to improved performance and competitiveness. Where generic marketing messages often fail to resonate with customers, we notice a large group of market-savvy organizations adopt personalized advertisement placements across relevant social media.

This practice helps organizations of all sizes reach their target customers more effectively, and allows smaller organizations to compete for the same markets, increasing customer choice and creating a fair market.

Victoria’s Secret is a good example of an organization that has implemented an AI-powered search feature that leverages Google Cloud’s Vision API. Customers can upload images to find specific products, creating a highly personalized shopping experience.

The second use case that has benefited greatly from imbibing generative AI is Customer Support. This enhanced customer support helps organizations retain customers, derive insights into products and customer behaviour, and increase employee productivity.

Studies across the National Bureau of Economics, Shakked Noy and Whitney Zhang, Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer display how organizations, including a Fortune 500 enterprise software firm, increase productivity by 66% on average!

The benefits don’t stop there either, as implementing generative AI tools can help cover knowledge gaps among employees and upskill them in additional areas of expertise. Another benefit is the undifferentiated heavy lift of document generation that is routine and process-led.

Leveraging generative AI tools for such tasks frees up an organization’s workforce to focus on value creation and innovation, in turn fulfilling employees’ career and personal goals. Organizations can observe productivity gains, improved employee retention and greater customer satisfaction as a result of creating impactful employee assistant tools.

One approach to enhanced customer support is automation, where organizations can design AI-powered chatbots and virtual assistants to handle routine inquiries, allowing human agents to focus on more complex issues. This not only improves efficiency but also enhances customer satisfaction by providing quicker responses.

A second approach is agent assistants, which help employees get resolutions to customer issues in real-time, by relying on vast troves of proprietary data. Organizations are building tools to conduct Retrieval Augmented Generation (RAG) on this proprietary data, ensuring that responses are relevant and accurate to the customer.

The best examples of this are from the financial services, Banking and Healthcare industries, where generic responses to customer queries can be confusing and misleading, necessitating responses that are use case, issue and organization relevant.

A third approach rising in prevalence is code assistance, with tools that can help code developers reduce the lead times in project development, version migrations, debugging and updates. The advancements in this field will be interesting to follow, as organizations and society grapple with questions about right-sizing workforces based on the tools available.

Regardless, I believe that these tools further democratize the market for organizations of all sizes and help people focus on pursuing value-added activities. This in turn spurs innovation and improves customer experience.

The third and last use case I explore here is dynamic content creation. Generative AI has empowered organizations to create impactful content leveraging their creative skills, by outsourcing the heavy burden of background tasks to powerful tools. Large models such as Stable Diffusion and Dall-E allow organizations to build innovative applications where their users can generate images based on text inputs, bringing a creative flair and unmatched user engagement to these organizations.

As large, open-source models become more powerful, expect capabilities such as creating rich presentations and powerful story-telling with word prompts to become prevalent in business.

To summarize, the evolution of generative AI promises to be the biggest game-changer to customer experiences since the internet, with rich experiences now within the grasp of organizations large and small.

As more enterprises develop powerful, purpose-built tools and capabilities, forward-looking players in the space will move fast and develop enhanced customer experiences that aid in customer retention, create positive top and bottom-line growth, and improve user experience for all consumers.