SUMMARY
A study by Accenture found that nearly one-third (33%) of customers leave a brand after just one bad experience
Customer service executives can use LLMs as assistants to improve customer communication, personalise responses, and improve the speed of their response
AI can also help in building powerful search and recommendation systems that can enable customers to exactly find what they are looking for
It is a well-known fact that a happy and engaged customer is the most effective ambassador a brand can have, and their impact can well surpass marketing campaigns. Study after study has proven this. For instance, a study by Cognizant found that engaged customers in the consumer electronics industry bring in 44% more revenue, while in the hospitality industry, they bring in 46% more revenue.
In a PWC report, customer experience was cited as the most important brand differentiator by 86% of business executives. Essentially, customer engagement directly translates into revenue and is the foundation of a strong business.
The reverse is equally true. A study by Accenture found that nearly one-third (33%) of customers leave a brand after just one bad experience. And many don’t just leave, they ensure they speak about it on social media. One in three customers (34%) will share a bad customer service experience on social media, as a study by Sprout Social found.
Most organisations know this. However, keeping customer engagement isn’t the easiest thing to scale. Customers now expect instant responses, personalised interactions, and proactive service. This is where Artificial Intelligence (AI) has emerged as a powerful new approach, and has the potential to transform customer experience (CX) in businesses of all sizes.
In this article, we will explore the cutting-edge AI technologies that enterprises can use to scale their customer engagement. Let’s dive in.
Conversational AI
LLMs, or Large Language Models, are a form of Generative AI technology that can process and generate text in human-like language. They are trained on massive amounts of data and can perform many kinds of tasks, like translation and writing different kinds of creative content. LLMs are of two kinds – proprietary closed source models (Closed LLMs), or open source models (Open source LLMs).
Open source LLMs like Mistral-7B, Llama2 and Falcon are far more powerful than closed ones, as businesses can choose the one that fits their use-case, deploy them on the cloud on their infrastructure, train them on their data, and build AI pipelines that integrate with their internal workflow.
One major advantage of this method is that businesses can then retain full control over sensitive customer data, and therefore, do not have to worry about the cross-border compliance challenges that come with sharing customer data with a closed platform.
Since LLMs are extremely good at generating language, customer service executives can use them as assistants to improve customer communication, personalise responses, and improve the speed of their response. They can also be used to build advanced chatbots, which can act as a first point of contact for the customer, answering simple questions, and then directing the more complicated ones to human agents. A lot of enterprises are already exploring this and we expect to see CX drastically improve over the next year due to LLMs.
Generating Personalised Images
Another powerful strategy that has emerged is AI’s capability to generate images. Stable Diffusion is a cutting-edge AI model that creates realistic images from text descriptions. It’s particularly adept at photorealism. This makes it perfect for personalised marketing.
Imagine showing a customer a picture of themselves using your product, created just from a text description. Stable Diffusion can combine a customer’s photo with a product image, letting them see how they might look with it. This is the kind of personalisation that AI can drive, and it helps customers experience a product even before they buy it.
As more advanced image synthesis models emerge, we expect newer and richer customer experiences, transforming our approach to shopping online.
Voice Chatbots
Along with AI that can generate language and visuals, Generative AI models for audio transcription and audio generation have also emerged. These models allow real-time transcription from audio and easy generation of human-like voice.
In geographies where customers prefer to communicate using their voice instead of text, such as in tier 3 or tier 4 of India, businesses can leverage audio AI models to understand customers’ queries and build chatbots that respond in their language. One example of such a model is WhisperSpeech, which can clone a voice and generate full audio from text. In the future, we expect powerful voice chatbots that can instantly respond to customers, in a language they understand, and help businesses improve customer service.
Reverse Image Search And Recommendation Systems
Along with helping customer service become more responsive, AI can also help us build powerful search and recommendation systems that enable customers to exactly find what they are looking for.
Reverse image search AI, for example, can allow customers to upload an image, and find products that exactly match that image or are similar to it. This technology has already been in place in the top marketplaces, but with an emerging AI technology known as Vector Search, this has become even more powerful.
Vector search engines have a range of capabilities. They can help businesses build reverse image search or advanced recommendation systems or can be combined with LLMs to build search experiences where customers can search using natural language (such as, ‘find a white shirt with pink and blue stripes’).
Approach To Leveraging Generative AI For Customer Engagement
AI is one of those technologies that has no ‘one-size-fits-all’. The way leaders should think about AI when planning to deploy it within their organisation is to first exactly outline the use case, find the right set of AI models, build an AI solution, A/B test it with a small set of customers, and then scale it up.
Also, it is key to test and deploy enough guardrails to ensure that the AI model doesn’t go off-brand or generate inaccurate responses. Finally, since AI solutions built for customer engagement would need to harness customer data, businesses should choose open-source AI models that they can deploy on their cloud infrastructure, ensuring that they retain control of their sensitive data.
In the coming future, most customer engagement will be AI-powered and far more interactive than we are used to today. This is the future that AI-powered customer engagement is promising to bring.
