How AI Can Improve Customer Experience (CX)

With more and more businesses trying to scale, sales processes are getting crowded. Sales reps are struggling to navigate overwhelming demands with fewer resources. 

So, how to keep the process going while ensuring customer satisfaction? The answer is artificial intelligence. Here is all about using AI in customer experience to deliver a fast, satisfactory, and personalized journey. 

Table of Contents

How AI Can Improve Customer Experience (CX)

What Is AI in Customer Experience?

Why It Matters Now?

Benefits of AI in CX

Challenges & How to Avoid Them - Keep It Human

High-Impact Use Cases (With Practical Tools)

Implementation Playbook: How to Use AI in CS?

Real-World Examples of AI in CX

Ethics, Trust & Transparency

Ready to take the next step with your customer experience?

FAQs

Quick Answer

AI improves customer experience by launching automated email outreach campaigns and forecasting sales trends. Businesses employ digital automation agents to handle complex inquiries and analyze customer data. This way, sales teams can make informed decisions.

What Is AI in Customer Experience?

AI in customer experience (CX) is employing advanced algorithms in the customer’s journey, before, during, and after they make a purchase. The intention is to deliver a fast, productive, and personalized experience. 

The AI customer experience industry is shifting to intelligent solutions, as customers expect more from businesses. Today, AI sales agents are trained on natural language processing and predictive analytics that help them understand customers’ sentiments, behavior, and needs, ensuring accuracy and efficiency in decision-making.  

Why It Matters Now?

Customers today think differently. They expect businesses to offer around-the-clock support, no waiting time, personalized recommendations, and more. Simply put, customer experience directly impacts how a person feels about a brand.  

A positive customer experience builds loyalty, reputation, and profits. So, it's only natural for companies to invest in this relationship. Businesses today invest significant money in keeping ahead of the curve to make the customers happy. And, AI tools help them do this. 

Intelligent sales platforms gather business data from various channels, condense it, and analyze it for actionable insights. These tools also possess generative and conversational capabilities that help them produce content for brands that directly resonates with the customers. 

However, one must strike a balance between using AI and human touch in customer experience. For this, you can employ testing and monitoring, and then proceed to capitalize. 

Benefits of AI in CX

Using AI to improve customer experience comes with numerous benefits. 

• Fast Response: AI sales agents can resolve queries almost immediately, reducing wait times and improving overall CSAT scores. 
• Around-the-Clock Support: Unlike a human workforce, digital sales reps work 24/7. This means your customers can ask you questions anywhere, anytime. 
• Cost-Effective: AI-driven customer experience means you no longer have to support big support teams. A few agents, and you can streamline almost all processes in the sales cycle. 
• Boost Productivity: AI is capable of automating repetitive tasks with better accuracy. Be it resolving inquiries, forecasting revenue, and nurturing, it can assist sales reps with all in less time. 
• Personalized Engagement: Sales platforms like Sintra.ai have dedicated business memory that stores all the company’s data and goals to provide tailored responses. This not only improves customer satisfaction but also contributes to the brand’s awareness. 
• Scalability: CX AI sales platforms are capable of managing a high volume of customer requests. 
• Sentiment Analysis: Advanced AI algorithms can now understand audience emotions from their tone, voice, and behavior. So, it's easier to adjust responses and improve interactions and FCR score, reducing errors.     

Challenges & How to Avoid Them - Keep It Human

While a significant success, improving customer experience with AI can be risky at times. Let’s see how to avoid this. 

Personalization: Whatever you say, AI lacks the personal touch that customers need. Hence, human interference is essential for building a connection and engagement. 

Complex Integration Network: AI integrations are complex than traditional ones. Hence, you must start gradually with limited use cases. Consult an expert to ensure that the AI is perfectly aligned with the system. This will remove scope for errors and hallucinations. 

Lack of Trust: Despite the growing popularity, customers do not trust AI. Some of them are overly skeptical of the accuracy of the solutions. To overcome this, you can employ agents for certain types of tasks that do not directly impact your customer relationship. 

High-Impact Use Cases (With Practical Tools)

Let’s see how AI-powered CX is helping business transform their customer experience across industries. 

24/7 Self-Service & Smart Triage

AI has truly transformed self-service rates. Today, AI chatbots handle everything from FAQs to product recommendations and simple queries. With such facilities, customers can immediately find the support they need, without navigating the complex agents’ setup. 

The 24/7 and context-driven support are especially crucial in high-demand situations. By 2029, the global chatbot market will grow to $46.64 billion, which is a true reflection of their popularity in sales processes.

Tools like Cassie are a perfect example of 24/7 self-service. This sales agent can follow up on email threads, respond to customers’ chats, manage support tickets, build product manuals, and more.   

Personalized Outreach & Email at Scale

Conversational and generative AI help businesses craft personalized campaigns to identify leads, engage them, and re-engage the lost customers. 

AI systems are trained to interpret data like a customer’s buying history, their online behavior, and profile nuances (demographics, job status, etc). Accompanied by generational capabilities, these tools add a personalized touch to the responses, improving CSAT significantly.  

Meet Emmie, your AI email assistant. Trained on hundreds of thousands of data points, Emmie follows up on email sequences, be it cart reminders, post-purchase messages, or promotional content. It also reaches out to potential leads to maximize revenue avenues.    

Agent Assist & Virtual Support

Digital agents in the sales cycles are your go-to companions for customer service. These automated reps resolve your customers’ problems using real-time data updates and smart information retrieval.

Whether on a phone call or email, these agents can transcribe calls, extract key points, and recommend products. Not only this, but they also save the interactions in the system for future reference and action plans.  

An example would be Vizzy, an AI-powered virtual assistant that automates repetitive, time-consuming sales tasks. It can compile your lead lists, produce financial reports, and generate marketable content, all with a few prompts.    

Sales Enablement & Smart Handoffs

AI strives to improve customer experience, and for this, it employs advanced algorithms during interactions. With a clear idea about what your potential leads and existing customers want, help them close deals faster. 

Sales enablement in AI-powered CX refers to empowering your sales team with enriched data. Valuable sales insights, predictive analytics, and consumer behavior - all help your company’s sales teams to craft a tailored experience for every person visiting your web.

Comes Milli, your intelligent sales assistant that can craft and launch cold outreach campaigns. From building proposals to advising sales reps on negotiations and writing cold scripts, Milli can do it all. Plus, it’s fully aware of your business context, so engaging prospects is no issue.  

Analytics & Voice of Customer Insights

Digital agents can now collect, analyze, and interpret feedback from across channels to constantly improve. This gives sales reps avenues to identify marketing gaps and make decisions accordingly. 

Data analytics integrated into your work system gather customer information, structure it, and then employ sentiment analysis to extract pain points and trends. Depending on these insights, sales agents develop thorough marketing strategies to engage leads and reach targets. 

Dexter, a data analyst, is yet another example of AI in customer experience management. It uses advanced algorithms to provide sales forecasts, financial insights, and ROI calculations in minutes. It can also interpret complex data to help businesses make smart choices.  

Implementation Playbook: How to Use AI Customer Experience?

Now that we know how integrating automated reps into your sales cycle can maximize revenue and CSAT score, it’s time to learn how to use them. Here is a step-by-step roadmap on how to use AI to improve the customer experience. So, dive right in.

Step 01: Start Small 

Begin small and always with clear objectives. It’s better to limit yourself to one or two use cases to reduce risk. For instance, employ Emmie in your workflow for following up on customers’ queries. 

Step 02: Set KPIs

Be clear about how you will measure success. Set key performance indicators for your AI-launched campaign. Here are some essential KPIs you might consider. 

• How much has the waiting time reduced per ticket?
• Was the AI able to resolve issues in the first interaction? 
• How do users feel about the changes in the sales service?

Step 03: Organize Data

Gather data from your customer inventory. Go through chats, support tickets, buyers’ history, etc. Before you feed it to the system, sort the data into proper structures. 

Step 04: Execute under Controlled Environment

You must not go all out with the execution. Here are some best practices for implementation. 

• Start with a small user group. 
• Assign a human to monitor it every now and then. 
• Keep checking the responses for errors or hallucinations. 

Step 05: Measure the ROI

It’s time to evaluate the results. See if your KPIs meet the expectations. Look for broader achievements like faster resolution, happy customers, etc. 

Step 06: Scale Gradually

Once a success, you can scale with time. Expand it to one or two channels initially and see how the system works for you. 

Real-World Examples of AI in CX

Leading businesses are employing advanced sales agents to deliver value in sales processes. Let’s have a look at some of them.

Starbucks 

Starbucks was struggling to balance the scale of its business and personalize for customers. Going beyond traditional marketing, the company developed Deep Brew AI. This app would analyze the customer’s purchase history, location, and local events. 

Depending on these insights, it would personalize offers, give promotions, and predict the demand for staff. This automation helped them in, 

• Increasing customer satisfaction and loyalty
• Boosting sales through tailored offers
• Streamlining operations and reducing waste.  

Pro Tip: Struggling with the same and don’t know where to start? Try Milli and Emmie to streamline your marketing strategy while tailoring your strategies for individual leads. 

Amazon

The sales team at Amazon, focused on user-based collaborative filtering, noticed that the strategy was not translating well into purchases. It then substituted the strategy with item-to-item filtering that would locate items that are most likely to be co-purchased and recommended them to the customers. This approach helped the company with, 

• Staying relevant while scaling the product recommendations. 
• Understanding buyers’ behaviors and how they translate into sales. 
• Making data-driven decisions. 

Pro Tip: The mentioned recommendation mechanism is already sophisticated, but employing Sintra’s Brain AI can help enhance its context and strategic vision. It’s the memory for your business that stores all the significant data (customers, goals, objectives). With this, you can task AI agents to perform different tasks while maximizing profit avenues.   

Ethics, Trust & Transparency

With constant advancements in AI-powered customer experience also comes the risk of AI ethics and transparency. Customers want to know how businesses are using their data. Here are some best practices you can adopt to gain customers' trust while using AI. 

• Disclose what customer information you are using and how. 
• Do bias checks to make sure AI does not differentiate between customers. 
• Allocate a human rep to oversee the system for errors and hallucinations. 
• Use tools with advanced data encryption for a safe browsing experience. 

Ready to take the next step with your customer experience?

Here is everything you need to know about the role of AI in customer experience as a business aiming to scale. Unlike traditional methods, it is inexpensive, efficient, and faster.

Want to go slow? Try Sintra.ai. It gives you the option only to purchase individual agents, so you can test them and evaluate their performance without going out of your pocket. 

FAQs

Is generative AI safe to use in customer interactions?

While quite popular, AI chatbots use customer information from different sources, which raises privacy concerns. Hence, as a business, you must be transparent about data usage. 

What is an example of AI in customer service?

Chatbots are the most common example of AI in customer service. Businesses employ smart bots to automate customer interactions, be it for resolving queries, answering FAQs, and recommending products. 

How can businesses balance AI automation with human connection?

Businesses can use a hybrid model to balance AI automation with human connection. They can task smart agents to automate repetitive tasks, so they have ample time to focus on high-priority tasks like strategy and planning. 

How does AI improve customer experience?

AI sales agents improve user experience by scaling business operations while being personal to the customer. Digital agents can study customers’ behaviors and preferences to facilitate the journey to individual needs. 

What metrics should companies track when using AI in CX?

A few key metrics companies should track when using AI in CX include CSAT score, churn rate, first response time, and retention rate.

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