How AI with Big Data Can Improve the Customer Experience

Efficiency. It’s one of the most lucrative goals for today’s businesses. Whether you want to free up your employees to complete creative tasks or reduce wait time for customers, you can’t do without modern technologies. That’s where artificial intelligence and big data analytics come into play.

These solutions not only streamline operations but also significantly augment the customer experience. How? AI, especially the recent introduction of generative AI tools like ChatGPT, has proven that robots can be highly realistic. They can speak like humans, and it’s hard to tell whether you talk with a person or not. It may seem scary. On the bright side, this presents numerous possibilities for elevating the customer experience (CX).

It’s not only about chatbots, although they’re among the most common use cases of AI. From personalization to sentiment analysis, you may harness the potential of both technologies to support agents. In this article, we’ll describe AI and big data, explain how they can enhance operational excellence, and clarify how to introduce them into your business. Stay tuned!

Understanding big data and AI in customer experience

First, let's define AI and big data in customer experience. AI is the technology that focuses on creating robots that behave, talk, and write like people. It combines terms like machine learning, natural language processing (NLP), and other solutions to enhance customer interaction with support agents.

Take AI-powered chatbots as an example. They can provide immediate responses to routine customer inquiries. Conversational AI platforms, on the other hand, offer more nuanced, personalized customer service. The goal is to make the work of customer service agents more manageable, efficient, and rewarding.

That's why more companies are integrating AI. Gartner predicts that by 2025, generative AI will power 80% of customer service and support organizations, leading to better customer service, productivity, and satisfaction.

Now, on to big data. It's a term used to describe enormous data sets with many variables that call for advanced and state-of-the-art approaches to sift through and arrange. Data can be called big if it meets the following criteria (often referred to as the 5Vs):

• Volume: The scale of data generated should be immense, like 241 million emails sent per minute.

• Velocity: How quickly data is generated and whether it's enough (or too much) for what you need to do with it.

• Variety: Data comes in various forms, like music, video, emails, and images, ranging from structured numerical data to unstructured assets.

• Veracity: Data should be reliable and high-quality to make informed decisions.

• Value: Good big data should apply to real-life situations, from analyzing customer interactions to generating valuable insights.

Note: Big data analysis and analytics are two completely different words. The former denotes examining datasets to identify patterns or trends, primarily looking at historical data to understand what happened and why (the spotlight is on the past). Big data analytics focuses on processing and performing statistical analysis on existing datasets to predict future trends and behaviors (the attention is shifted to what's going to happen).

The mechanics of AI and big data in enhancing CX

Let's take a look at both technologies and what they bring to the table. As you'll notice, integrating them goes beyond just being trendy. With the help of these advancements, you can set yourself apart from the competition, increase customer satisfaction, and strengthen customer retention strategies.

The value of AI for customer experience

Round-the-clock customer support

AI-powered customer experience enables clients to solve problems independently when needed, not when it’s convenient for support teams. This reduces a ticket queue that doesn’t spiral out of control after the weekend.

Chatbots can answer FAQs, provide customers with tailored resources, and deal with routine queries. This allows human agents to resolve only complex issues the robot is unable to tackle, improving customer service.

Quicker resolutions

Suppose a customer reaches out to you after having a poor experience with your product. In short, it broke. The customer feels disappointed and irritated and expects a support agent to do something about it.

What happens when they have to wait? You'll most likely lose this client forever, and they may leave negative customer feedback on your website or other public platforms. AI prevents such scenarios by handling various challenges or routing unknown problems to the most suitable customer service agent.

Cutting down errors and bias

Humans frequently make mistakes. But what if they didn’t have to? AI minimizes those facepalm moments by automating information capture. This means no manual data entry errors. Plus, with AI's help in analyzing customer interactions, you can make decisions based on hard data, not assumptions and guesses.

Smart routing

There can be cases when one specialist is more suitable for a particular task than the other. AI can analyze customer questions to identify intent and match them with the needed person. It runs through keywords, allowing customers to get expert advice faster and agents to play to their strengths.

Personalized recommendations

Netflix is among the first companies you think of when you bring up personalized interactions. After you create a Netflix account, the platform begins collecting information about your watching habits, favorite genres, and even the individual films or television shows that you have rated.

Based on this, it suggests content you may like. Your habits inform its unique recommendation algorithm tailored to your preferences. Similarly, AI can deliver personalized content in other spheres, be it eCommerce or SaaS.

Predictive analytics on customer behavior

Past customer data, such as conversation history, customer feedback, and existing support tickets, is useful for predicting customer behavior. It's another example of how AI can improve customer experience. It can provide insights into consumers' pain points, allowing you to proactively address problems before they escalate.

Suppose you utilize Salesforce CRM. This tool is already equipped with an AI solution called Einstein Prediction Builder that can prognosticate on future sales, customers who are more likely to churn, the likelihood of no-shows, and so on.

How can big data improve customer experience?

• Hyper-customization: Big data gives you a better understanding of your target audience (their needs, product usage, website visits, and so on). Armed with these actionable insights, you can ensure more personalized interactions than with regular data.

• Anticipating needs: Similar to AI predictive analytics, big data is essential for planning your next actions. Let's assume a customer buys a winter coat. Maybe they can benefit from a glove suggestion.

• Cost reduction: Identify where you waste money and use the technology to craft more effective and efficient strategies.

• Unveiling breakthrough offerings: Find the gaps in your product line and develop new products and services to grow the business.

• Boosting loyalty: Purchase history, customer calls, and other sources can provide details essential for refining your rewards and increasing the number of loyal customers.

Practical applications and examples

It may not always be easy to figure out how to use AI to improve the customer experience. Yet, it can, and we'll share some inspiring examples of companies to help you visualize its advantages.

1. Krafton

The PUBG: Battleground game developer from South Korea turned to AI to serve a diverse, global player base. It sought one of the most crucial AI capabilities: multilanguage support. Zendesk, an integrated customer service platform, has provided different tools to satisfy these needs. Among them were prepared responses for effective ticket management and Dynamic Content for automated translations based on user location.

2. American Eagle

The retail giant employs AI to improve customer experience in its brick-and-mortar stores. By leveraging AI, computer vision, and machine learning, it makes personalized recommendations based on the following:

• customers' body measurements;

• skin tone;

• clothing preferences, etc.

At the same time, it preserves the human touch, creating an impeccable fitting experience.

3. Decathlon

The sporting goods retailer opted for Heyday's conversational AI platform to deal with a 4.5X surge in customer inquiries during the spring of 2020. The digital assistant has lowered support costs and addressed a wider range of customers' needs, exceeding 1,000 distinct intentions (65% of all inquiries).

4. Myntra

The following case in point of AI in customer service is Myntra, a Walmart-owned eCommerce website. It introduced MyFashionGPT, a ChatGPT-powered feature that streamlines the customer journey in the following ways:

• It relies on natural language processing to understand users;

• It offers them various products from relevant categories;

• It facilitates the completion of desired looks hassle-free.

Whether it's wedding attire or vacation outfits, MyFashionGPT is a powerful tool for crafting the ideal look.

5. HomeServe

The final item on our list of AI customer experience examples is HomeServe USA's Charlie. It's an AI-powered virtual assistant that handles over 11,000 customer support calls daily. Charlie promptly responds to consumer inquiries, triggers the claims procedure, and arranges repair appointments. This AI tool also provides human agents with real-time guidance based on customer sentiment.

AI-driven customer experience: strategies and tools for implementation

Let's provide tips for marrying AI, big data, and customer experience. Remember that integrating these technologies is a continuous process as you collect more information over time and open up new business opportunities.

Gathering big data

You can accumulate data using various sources and methods, such as:

• helpdesk plugins that can capture data from chat, email, or calls;

• customer relationship management systems;

• Google Analytics data to study customer behavior on your website;

• surveys shared via platforms like SurveySparrow;

• dark data (what's generated during regular business activities but is not used for other purposes).

Diving deep into analysis

The next step is to dig into the collected data and what it can offer, namely:

• Clean the data—remove duplicates, fix incomplete entries, and discard irrelevant details.

• Decide what information you want to find out from this big data.

• Search for patterns in the data and visualize them with dedicated tools using tables and graphs.

Selecting the right analytical tools

Utilize specialized data analytics software. The available solutions vary depending on their advantages, disadvantages, and possible use cases:

• Microsoft Excel: A famous beginner-friendly product from Microsoft that is suitable for simple to mid-level data analysis.

• Python: A programming language that finds its application in software development and data analytics, too. It's flexible and provides extensive library support for data manipulation and visualization.

• R: Another fast and highly flexible programming language, but specifically designed for statistical analysis and data visualization.

• Jupyter Notebook: a web-based computing platform that calculates larger data sets using Python, R, and other languages.

• Tableau: A more sophisticated Excel version known for its ability to produce dynamic, multi-layered visualizations.

Expanding AI capabilities

As mentioned above, AI is an umbrella term for various technologies and approaches, including:

• robotic process automation (RPA);

• machine learning;

• NLP, among others.

Use RPA and machine learning algorithms to automate repetitive tasks, predict customer needs, and conduct sentiment analysis. On the other hand, NLP can analyze customer feedback across various channels and allows machines to understand and generate human language.

Extra tips for implementing AI in CX

1. Start small: You don't need to overhaul customer experiences right from the beginning. Break down your work into specific, manageable projects to see immediate impacts and learn from practical experience.

2. Invest in team education: AI, big data, and data analytics have steep learning curves, so plan additional training resources, create a knowledge base, or purchase mentorship programs.

3. Iterate, improve, and measure: Establish KPIs to evaluate whether your team and customers feel the benefits of AI in customer service. These can be customer satisfaction scores, response times, and resolution rates. Use the insights to enhance strategies for AI, improving customer experience.

4. Explore open-source data resources: Open-source data can provide information on the current market status. Some resources are Google Public Data, the US Government’s Open Data, Yelp Dataset, and Open Data Monitor, among others.

Conclusion

AI and big data are potent tools for understanding customers better, establishing quality interactions, and boosting efficiency. If you, like many other companies, want to use AI, consider the following tips mentioned in the article:

• Strategize: Identify areas within customer service that can improve thanks to AI and big data integration.

• Select tools wisely: Opt for solutions that align with your specific business requirements.

• Train your team: Make sure they understand the purpose of matching AI and customer experience and how to harness these new technologies effectively.

Curious to learn how a data catalog can help you add AI to your CX equation? Book a demo with us today.