AI for Customer Support: Why You Need It

In today's digitally-driven world, customer support has never been more important – though it can be hard to meet rising demands as your business and customer base grow. You may face difficulties gaining insights into your customers' demographics, requirements, and concerns, or you may not have the necessary resources to address their needs promptly.

Poor customer service costs businesses billions of dollars annually. In a 2020 survey, 40% of US customers claimed they stopped doing business with a company due to poor customer service.

As a result, companies are now turning to artificial intelligence (AI) for assistance. AI solutions streamline customer service and help businesses identify potential areas for growth by analyzing the factors that lead to customer churn. There are two primary approaches to utilizing AI technology in customer service: implementing AI-powered chatbots to handle a larger volume of inquiries more efficiently and employing Machine Learning (ML) platforms to analyze customer data for valuable insights.

In this article, we'll introduce you to Akkio, a predictive AI platform that can help your business improve customer support. With Akkio's AutoML, you can automatically route customer service inquiries to the appropriate team while leveraging Chat Explore to analyze customer data.

Replying to Customer Queries Using AI-Powered Chatbots

AI-powered chatbots are a game changer in the realm of customer support. These sophisticated software applications leverage AI and Natural Language Processing (NLP) to simulate human-like user conversations. By integrating chatbots into messaging platforms, websites, and mobile apps, businesses can provide 24/7 customer support.

Although chatbots have been around for some time, they haven't always been as helpful as they are today. Most chatbots are trained to translate user queries into a limited number of questions, provide straightforward answers, or route the user through to customer service.

However, recent advancements in NLP technologies, such as GPT-3, have transformed chatbots into efficient tools. By answering a broader range of customer questions and freeing up capacity for human agents, these cutting-edge chatbots drastically reduce waiting times and improve customer satisfaction.

A survey of 500 business leaders by Intercom found that chatbots speed up response times by an average of 3x and increase customer support satisfaction scores by 24%.

Reply to Customers Directly

As of 2023, AI chatbots are experiencing a renaissance. The most advanced chatbots harness ChatGPT alongside a "source of truth" derived from a company's documentation –  including FAQs, user guides, product specifications, or a marketing knowledge base.

This combination enables chatbots to deliver accurate and relevant answers to customer queries. Moreover, AI chatbots can assist users in taking actions, such as placing orders or scheduling appointments, further streamlining the support process.

Built on ML – a subset of AI where a model is trained on existing datasets about your customers – these chatbots can learn from previous interactions and continually refine their responses. Implementing this type of chatbot offers numerous benefits to businesses, such as servicing more customers simultaneously, streamlining customer support, and handling technical queries.

This, in turn, allows in-house teams to focus on more technical issues, and data from chatbot interactions can be analyzed to extract valuable business insights.

Scale Alongside Your Business

An AI chatbot can keep up with your business as it grows, scaling its capacity to take on a larger volume of customer queries without additional resources. This keeps your customer support experience consistent and quick.

What's more, AI chatbots can be integrated with other systems – like customer relationship management (CRM) systems, marketing platforms, and other business tools you might invest in to streamline your processes. Integration keeps communication clear across different departments, allows for data sharing, and can benefit the overall efficiency of collaboration within the company.

Route Customer Queries to Human Support

Despite their efficiency, customer service chatbots do have their limitations. Chatbots sometimes struggle to comprehend complex or nuanced questions and lack the personal touch and empathy you'd receive from a human customer service representative. Fortunately, these challenges can be addressed with AutoML (Automated Machine Learning).

AutoML can automatically read and route customer inquiries to the most suitable team – a process known as escalation. This could involve directing technical questions to a higher-level support agent or billing inquiries to a retention department. By using AI chatbots to identify when escalation is needed, businesses can ensure that customers receive the appropriate support at the right time, reducing frustration and improving customer satisfaction.

Integrating this functionality within a chatbot enables it to determine whether it is appropriate to hand over the conversation to customer service agents. AI chatbots use various techniques, such as NLP, ML, and conversational AI, to understand customer queries and respond with accurate and relevant information.

NLP is a subset of AI that enables computers to understand human language (including idioms, slang, and regional variations), while conversational AI systems engage in natural language conversations. Conversational AI uses NLP as a foundation to interpret human language but also involves other technologies, such as machine learning and speech recognition, to enable human-like interactions. As a result, chatbots and virtual assistants often use conversational AI to provide customer service.

By analyzing the customer's responses and queries, AI chatbots can identify when the customer is frustrated, confused, or requires a more personalized response, and transfer the conversation to a human support team if necessary.

Another significant advantage of using AutoML tools is their ability to prioritize support tickets automatically. Typically, these tickets are attended to in chronological order or after human screening.

How to Automatically Route Customer Queries With Akkio

If you're looking for an easy-to-use and cost-effect solution to automatically route support tickets to the appropriate team, look no further than Akkio! With Akkio, you can build custom models designed to predict which team incoming inquiries should be directed to, streamlining your customer support workflows and ensuring a faster customer response time.

You'll need to employ a supervised learning approach to create these custom models. This involves feeding the model a sample of labeled data, such as previous examples of support inquiries, along with the corresponding team they were assigned to. Training the model with this data allows it to accurately predict the most suitable team for new customer queries.

Akkio's AI capabilities encompass sentiment analysis and text analysis, both components of NLP. This means that your custom model will not only be able to determine the general topic or category a customer query falls under based on the language used but also gauge the emotions or sentiment behind the message. Understanding whether your customers are happy, sad, or frustrated allows you to provide tailored and empathetic responses that are more reminiscent of human interactions.

It's important to note that Akkio does not offer pre-trained models – you'll need to build custom models and train them using your data. However, if you want to speed up the training process, you can leverage external datasets from platforms like Kaggle. These platforms offer a wide range of customer support datasets that can supplement your in-house data and help train your model more effectively. Of course, you may prefer to rely solely on your own data to ensure the model is fine-tuned to your specific customer support needs.

Gaining Key Insights About Customers’ Data Analysis Using AI

Businesses can leverage AI and machine learning techniques like clustering, decision trees, and regression analysis to analyze customer data and ultimately improve the overall customer experience. By gaining insights into customer needs and preferences, identifying trends and patterns, and predicting customer behavior, businesses can tailor their offerings and communications more effectively.

This process often involves supervised Machine Learning, where an ML model is trained on existing datasets about your customers. However, unsupervised learning techniques, such as clustering, can also be applied to user queries to discover patterns.

Utilizing ML for customer data analysis offers numerous benefits, including:

• Efficiency: ML automates the analysis of a large amount of data, saving time and resources for businesses and allowing them to focus on more strategic activities.
• Personalization: Insights gained through ML enable businesses to tailor their products, services, advertising, and communication to meet customer needs and wants better. For example, an e-commerce website can use ML to analyze a customer's browsing history and display products that are likely to interest them.
• Churn prevention: ML can help businesses predict and prevent customer churn by analyzing data to identify patterns of behavior that indicate a customer is at risk of leaving.

Some common types of customer data analysis conducted with ML include:

Text Analysis

We've already seen how ML can route customer support tickets and emails, and it can also be employed to classify text. This technique involves categorizing text into predefined categories, such as product quality, customer service, or shipping speed.

By classifying customer feedback into these categories, businesses can quickly pinpoint areas of importance to their customers and prioritize their efforts accordingly.

Sentiment analysis is another useful technique that can analyze text data and determine the emotional tone of the language. Sentiment analysis algorithms typically use NLP methods to analyze text data and determine whether the language used is positive, negative, or neutral.

So, suppose you're analyzing customer feedback, like product reviews, social media posts, or customer support interactions. In that case, you can use sentiment analysis to gain insights into your customers' emotions and opinions about products or services. This information can then be used to improve offerings, customer service, and marketing strategies.

Customer Segmentation

Machine learning can help businesses segment customer interactions based on urgency. For instance, ML can identify whether an inquiry is a feature request that can wait for the engineering team or a critical downtime report that requires immediate attention.

Additionally, ML can be used to distinguish between different types of customers requesting support, such as a sporadic user who can wait a few days or a high-priority customer who needs an immediate response.

Segmentation isn't solely based on customer interactions but also considers customer profiles and behavior patterns to create more targeted marketing campaigns or product offerings.

Predictive Analysis

ML can be utilized to predict customer behavior and preferences based on historical data – and this technique helps businesses anticipate customer needs and provide proactive support. Examples of predictive analysis include Customer Churn Prediction, which uses ML algorithms to analyze data and predict which customers are at risk of leaving, and Customer Lifetime Value (LTV) Prediction, where ML algorithms analyze customer data and predict the lifetime value of each customer.

Identifying high-value customers allows businesses to tailor their marketing campaigns to these individuals, offering personalized promotions and recommendations that are more likely to drive conversions. Similarly, identifying customers at risk of leaving lets businesses take steps to improve their experience and retain their loyalty.

By harnessing the power of AI and ML in customer data analysis, businesses can enhance their customer support strategies and deliver a more personalized user experience.

Analyzing Customer Data With Akkio

Akkio's machine learning platform enables businesses to conduct various customer data analysis tasks, such as text analysis, customer segmentation, and predictive analysis. In this section, we'll walk you through the process of setting up and training a new ML model using Akkio, step-by-step.

Setting Up and Training a New ML Model With Akkio

1. First, you'll need to sign up for an Akkio account. Input your details when prompted, verify your account, and choose a free or paid account depending on your needs.

2. Then, connect your training data to create a new flow from the Akkio homepage. You can upload your datasets directly or integrate Akkio with your business's data collection tools, such as Salesforce, Hubspot, Google Sheets, or Zapier. For a complete list of integrations, visit the Akkio integrations page.

3. Now it's time to clean, prepare, and transform your data – and Akkio automates the process for you! Click the 'Data Cleaning' button, configure your instructions, and hit 'Preview' > 'Apply Transform' when you're happy. Chat Data Prep provides additional assistance, too, and allows you to transform your tables with plain English.

4. Next, choose whether you want to build a model for prediction, forecasting, or anomaly detection. Then, click the plus button on the left-hand of the screen, click your preferred model, and proceed with the training by clicking 'Train Model'.

5. The last thing to do is deploy the flow. Click the plus button again, select 'Deploy Flow', select your deployment method, and then hit the 'Deploy' button on the following screen. Then, you can analyze your customer data and start generating insights.

Akkio's Chat Explore feature, powered by GPT-4, provides instant insights into your data, allowing you to make informed decisions quickly. Additionally, Akkio's live reporting functionality makes it easy for businesses to take action based on data reports.

Try Akkio in Real Time

Want to see Akkio in action and see how AI tools can enhance your customer support? Explore the solution's sentiment analysis below, which can help businesses gauge customer emotions and tailor their responses accordingly.

Improve Your Customer Support With Akkio

The world of customer care evolves quickly, and harnessing the power of AI to improve your customer support is no longer a luxury, but a necessity. AI-powered chatbots and data analytics offer a multitude of benefits, including increased efficiency, enhanced personalization, and the ability to predict and prevent customer churn.

However, while AI chatbots can effectively handle customer issues, data analytics offer a more comprehensive approach to understanding customers' needs and preferences. Investing in data analytics is a no-brainer – you'll gain access to a wealth of real-time insights that'd otherwise be time-consuming and challenging to obtain.

Akkio stands out as a powerful and cost-effective solution for businesses seeking to optimize customer support through advanced analytics. With Akkio's user-friendly interface and versatile features, you can quickly analyze customer data, segment your audience, and make data-driven decisions to elevate your customer experience. Sign up for Akkio today and take the first step towards enhancing your customer support.