Impact of Generative AI on Customer Experience

Do you remember typing a question so nuanced on Google, you had no hope of getting an answer?

But then, you’d find an ancient Reddit thread, a Quora post — and for the Internet veterans — someone on Yahoo! Answers asking the exact question?

Oh, the relief on our faces!

That’s exactly what generative AI is solving for today. You can experience that moment of serendipity, but now, it’s not just luck — it’s by design.

Now, take that eureka moment and amplify it across every interaction your customers have with your business. Generative AI in customer experience (CX) enables you to build meaningful, human-like dialogs with every interaction — tailored to each customer's context. Let’s understand how you can adopt this tech today and re-imagine your customer experiences.

What is generative AI?

Generative AI is a subset of artificial intelligence that specializes in creating unique content by analyzing and learning from extensive data sets. It identifies and replicates complex patterns, styles, and structures from its training data, which allows it to generate new outputs, such as text, images, codes, product designs or audio clips that closely resemble those produced by humans.

These training data sets are built from the ocean of information available online to ensure an iterative, creative content production.

Generative AI often utilizes advanced neural networks like Generative Adversarial Networks (GAN), and Natural Language Processing (NLP) to render natural, highly contextual responses each time you feed it a well-engineered prompt.

Large Language Model or LLM – The Building Block of Gen AI

Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand, generate, and manipulate human language. They are foundational in generative AI, trained on extensive text data, and excel in tasks like translation, summarization, and answering questions.

Tools like Bard, ChatGPT, Jasper, and X’s Grok are prime examples of how LLMs enable sophisticated, human-like interactions with AI. Despite their impressive abilities, LLMs are not infallible. Their reliance on training data can sometimes yield outdated or factually inaccurate output.

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In customer experience, generative AI shapes interactions that hit the mark every time, turning routine exchanges into moments of accurate, personal connection. Turns out, the majority of decision-makers also want to focus on generative AI to improve their CX.

Generative AI vs. Conversational AI

Both Generative AI and conversational AI aim to humanize conversations between man and machine; however, they differ in many aspects, as described below:

Types of Gen AI in customer experience 

The Gen AI wave has changed in 2023. In fact, you could potentially derive 75% of the value for your use cases in customer experiences from Generative AI. Here are the types of generative AI in customer experience you can use to level up your business. 

1. Chatbots and voice bots
   Conversational bots that are powered by generative AI can power customer self-service, reduce resolution times, and improve customer satisfaction — by ensuring case-specific tonality and context in real time. It can help you narrow down the reasons your customers contact you and identify relevant intents to deploy bots in a much shorter window.
   
   Take a quick glance at 7 Steps to Implement Generative AI in Customer Service.
   
   

2. AI-backed personalization
   Generative AI refines customer profiles using data from past interactions, purchases, and preferences, sharpening the accuracy of product and content suggestions. It crunches data on what your customers view, click, or buy to deliver recommendations and a bespoke shopping aisle.
   
   

3. Channel-agnostic action
   Besides powering bots, Generative AI equips agents with the ability to respond aptly across platforms — crafting detailed HTML emails or responding on social media — ensuring every interaction is contextually on point and visually coherent.
   
   

4. Synthetic voice production
   Generative AI's voice generation transforms IVR systems with speech that sounds convincingly human. These AI-crafted voice messages provide a consistent, brand-aligned auditory experience across customer touchpoints.
   
   

5. Visual customization
   Generative AI in eCommerce streamlines the creation of images and 3D models built to user preferences. This tool actively adapts product visuals to match customer interests, enhancing their experience by providing a clearer, more personalized view of items, from virtual product displays to interactive home design simulations.
   
   

6. Augmented virtual trials
   In fashion and home decor, generative AI offers virtual try-on capabilities, enabling customers to see products on themselves or in their living spaces. These AR customer experiences not only add a layer of interactivity but also help in making more confident purchase decisions.
   
   

7. Automated content drafting
   For marketing, generative AI is a powerful tool for creating compelling ad copy, social posts, and product descriptions. It pivots content to resonate with the target audience, ensuring that marketing efforts are relevant and engaging.
   
   

8. Data enrichment
   Behind the scenes, generative AI enhances customer data sets, enriching the information that trains machine learning models. In fact, Sprinklr AI+ uses generative AI to use these data sets in multiple languages to drive strong strategic decisions — and simply the best CX you can render. Here’s how.

How to improve customer experience with generative AI: Challenges & solutions 

There are many surefire use cases of Generative AI in CX with palpable challenges and solutions. Let’s understand them in detail.

Challenge 1: Incomplete customer insights

Businesses were limited by static data collection methods, missing the deeper, evolving narratives of customer behavior.

Solution: Continuous learning systems

Generative AI continuously evolves to refine customer understanding, deriving real-time insight from live data streams to render delightful experiences.

In fact, a US-based personal styling service called Stitch-Fix used generative AI to super personalize their shopping experiences. They equipped their stylists to use OpenAI’s text-to-image model DALL·E to visualize clothing unique to a customer’s color, fabric, and style preference. This visual guide then lets them find and select outfits from their own inventory that align closely with the customer's envisioned look.

They even used ChatGPT 4 to sift through thousands of customer notes, including requests and feedback, allowing them to grasp each customer's unique style. This analysis enabled them to create more tailored and accurate styling options for their clients.

Talk about curation!

Challenge 2: Outdated marketing approaches

Conventional marketing methods lacked the capability to adapt to the fluid patterns of customer engagement swiftly.

Solution: Predictive behavioral modeling

Generative AI models predict future behaviors by analyzing current trends, enabling businesses to craft anticipatory marketing strategies.

For example, Sprinklr AI+ can help you tap into unstructured conversations to map out emerging trends in your market. It helps you filter out positive, negative, and neutral activity around your business and your industry to surface invaluable insights that can be used to build striking marketing campaigns.

Challenge 3: Generic product development

Product innovation was slowed by a lack of customer-specific insight, resulting in generic, less impactful offerings.

Solution: Customized product insights

Generative AI informs product design with deep consumer insights, driving more personalized and in-demand product developments.

Samsung’s home appliances about to level up

Recently, at the IFA tech trade show in Berlin, Samsung’s Head of Software Development, Yoo Mi-young announced the company’s plans to integrate generative AI in their home appliances by 2024. "Generative AI technologies will be applied to voice, vision, and display," she reported. Samsung is building its home gadgets to communicate with users conversationally and respond better to questions based on past exchanges and context. This would mean that the appliances will have higher operational awareness — such as identifying foods being prepared in the oven or items stocked in the fridge, enabling them to offer customized recipe ideas and nutritional advice.

Challenge 4: Imprecise customer segmentation

Traditional segmentation often missed the nuances of customer clusters, leading to broad and ineffective outreach. 

Solution: Deep pattern recognition

Generative AI delves into data with pattern recognition capabilities, detecting subtle customer segment behaviors for hyper-accurate audience targeting.

For instance, digital-first fintech companies can use it for customer segmentation. Generative AI can help them identify micro-segments of users with similar spending habits and socio-economics to introduce features catering to each group.

Challenge 5: Non-adaptive marketing content

Marketing content often fails to resonate due to its static, one-size-fits-all nature.

Solution: Dynamic content generation

Generative AI creates and adapts marketing content in real time, ensuring relevance and resonance with changing customer interests. Here’s what it looks like to create highly targeted, relevant content using the generative model on Sprinklr AI+.

Challenge 6: Customer retention guesswork

Businesses struggled to predict customer churn, often reacting too late with retention efforts.

Solution: Trend anticipation algorithms

Generative AI identifies at-risk customers by learning from churn patterns, allowing pre-emptive action to boost customer retention.

5 Tips to implement generative AI in CX for businesses

As the hype around Gen AI simmers down, it’s vital for businesses to evaluate the real value Gen AI brings to them. Either connect use cases to measurable KPIs or recognize net new revenue created by GenAI in CX. Additionally, leverage these five tips to risk-proof your AI investment and make Generative AI work for you.

Tip #1: Assess data infrastructure 

Ensure your data architecture can support generative AI by being robust and flexible. A solid foundation is critical for AI to analyze and generate reliable outputs.

Tip #2: Integrate with existing systems

Seamlessly introduce generative AI into your current tech stack like CRMs, communication channels, analytics tools, etc. It should enhance, not disrupt, your ongoing operations.

Tip #3: Focus on user training

Invest in training your team to work alongside AI. Understanding how to interpret AI-generated data and results is key to finding its full potential.

Tip #4: Iterate and optimize

Start with a small-scale implementation to test and learn. Use the findings to optimize the AI's performance for larger-scale rollouts.

Tip #5: Stay informed on AI ethics 

As you implement generative AI, stay updated on the evolving standards and regulations related to AI ethics and data privacy to ensure compliance. Understand that “Responsible AI” is the intersection of trust, partnership, and integrity between brands, vendors, and consumers.

Take your CX on a fresh spin with Sprinklr AI+

Isn’t it exciting to see what generative AI can really be? That was an unintentional rhyme scheme, but doesn’t it make you feel limitless? Your customers deserve the exceptional experiences generative AI is capable of. That's where Sprinklr AI+ comes in.

A decade in the making, it distills unstructured CX data into clarity, with over 1,250 AI models as diverse as your customer base, cutting across 100+ languages and 150 countries. Take a free demo and we will show you how you can curate bespoke journeys for everyone.