5 Ways How AI Improves Customer Service
Brands and organizations have evolved through the years, with internal processes shifting to an automated model to speed up processes and maximize internal efficiency. More money is being spent on advertising and customer research to create high-demand products and carve a niche for brands within a competitive market space. But within this world of change, a few things remain constant –, and customer expectations are one of them. Customers expect speedily, hassle-free service delivered efficiently and with added value in the modern world.
Even the best products stand to fail if customers feel that the after-sales service of a company is not up to par, turning instead to competitors who care for them and make them feel heard and taken care of. Customer service agents often find themselves on the frontline of this battle for retention, facing a barrage of questions, complaints, and feedback related to products, services, and more.
AI for customer service has revolutionized this field, adding value to a system that initially placed great strain on service agents and was vulnerable to human error, time constraints, etc. Today, more companies have turned to AI in different forms, from IVR systems to advanced chatbots, leading to the growth and prominence of AI in various industries.
Features like Natural Language Processing (NLP) have given companies the ability to sift through textual conversations with customers, understanding the context and tone of communications to fine-tune and personalize communications with individual customers.
At the same time, databases like Frequently Asked Questions (FAQs) allow them to service masses of customers without a single call being placed to a customer service helpline. Below, we’re going to examine a few points in detail to see why AI for customer service is a viable choice to scale customer support and why it’s being widely adopted across companies, no matter the industry they belong to:
1. Data capturing and delivering insights
A massive amount of data is generated every day regarding customer interactions and behavior, as well as the latest trends, rise in demand, etc. Data analytics tools already exist to collate this data and present observers with a summation of all the information regarding a particular customer or groups of customers. AI for customer service possesses the ability to take this a step further, understanding and decoding data and results and ‘learning’ as a result of it – allowing handlers to modify behaviors as required.
AI can help deliver insights on data sets, allowing brands to provide relevant content, boost sales and ultimately achieve high levels of customer satisfaction and experience through a highly personalized journey. Earlier, human interaction was a must, if for no other reason than to simply decode the data at hand and suggest optimized solutions – but automation has proved itself amply capable of doing the same by providing an accurate understanding of customers.
2. Automating customer service
Think back to our earliest depictions of a service center on television or in training videos and magazines. The scene displayed was almost always a room filled with service agents, voices raised over the din of ringing phones, and an atmosphere that was rushed and tense. Each agent pushes themselves to the limit to answer as many calls as possible, thereby reducing customer churn. While it wouldn’t be unfair to assume this was what an efficient service center looked like, the truth is far from it.
Every one of those agents is likely overworked, and ringing phones aren’t a sign of successful customer service – every one of those phones represents a customer frustratedly awaiting his turn to be serviced. A large percentage of these customers will hang up before an agent is available, and most of these customers are further likely not to call back again – moving on from the brand as they feel like the service was inadequate.
AI for customer service has changed this scene entirely, giving customers the ability to self-serve and shifting this burden away from service agents unless required. The most outstanding example of this is the Interactive Voice Response (IVR) system – an automated AI response to customers calling service lines that helps filter through queries and direct customers to the appropriate department without requiring agent intervention.
Chatbots on websites are another example of automated customer service, offering customers assistance from the get-go on various topics ranging from product and feature assistance to answering FAQs. These features help speed up the process of customer service and ultimately lead to boosted customer loyalty and retention.
3. Guiding agents with the next best action steps
AI for customer service doesn’t just benefit the customer through instantaneous interactions and responses – it also aids agents, providing a detailed breakdown of the best practices and responses to customer queries and complaints. Newer agents might require guidance or training modules to get up to speed with the proper process to follow when it comes to different consumer questions.
AI is often set up to supplement and help guide them through the process, instantly guiding them to the next best action step via interactive decision trees software as and when specific keywords are entered into a database. This helps them resolve queries faster instead of putting customers on hold as they conduct spot research to try and find the required answers. For example, consider a customer talking to an agent on a food delivery app if something has gone wrong with the order.
The customer might demand a refund – but agents often don’t have the authority to process these requests themselves. Ordinarily, the agent might have to consult a superior or a guidebook to see what is recommended in these situations. AI will help them resolve this query faster, guiding them through the steps and brand policy for these situations, such as suggesting a replacement meal, offering a discount coupon, and authorizing a full refund if the customer still requires it. This situation allows the agent to serve customers more efficiently – while keeping consumers happy as they receive quick solutions to their problems.
4. Employing natural language understanding
One of the more recent developments in the field of AI for customer service is the advent of NLP. Earlier, AI was coded to pick up on specific keywords to understand and process requests and instructions, but not all consumers are guaranteed to use these keywords. The AI would be rendered helpless in these situations as a service agent took over to process the customer’s request.
Advancements in NLP have changed the game, allowing AI to decode conversations with customers better, giving companies better insights into patterns of interaction, and arming them with the data required to boost customer experience (CX).
AI can now go beyond simply understanding the keywords of a conversation, picking up on tones and conversation context to understand when a customer requires human intervention, escalation, and more. These advancements also allow AI bots to hold more natural conversations with customers – which is a high priority. Most people will refuse to take advice from a simple robot but will take the same advice if they feel that the bot is truly intelligent and has fully developed and understood their requirements.
5. Predicting behavioral patterns for better experiences
As mentioned above, AI for customer service allows companies to gather and interpret vast quantities of data relating to customers, queries, products, complaints, and more. NLP has further boosted its ability to understand these conversations and predict behavioral trends and patterns for similar discussions in the future – leading to better experiences for customers and reduced churn. This doesn’t end with improving the quality of customer interactions, however.
Predictive analytics will also allow companies to foresee global trends and talking points, identify sales opportunities, and intercept as yet unseen threats to their business, using real-time data to generate personalized responses and options for different situations.
An example of how AI utilizes behavioral patterns for better output is the query bar on top of most websites. While this bar leaves consumers free to search for their question of choice, a recommended list is often visible before a word is typed, with the top searched questions ready to go. Here, the company has noticed a customer trend relating to some questions being asked more often than others and has thus made this question highly visible – making their service feel more intuitive to a customer with the same question.
Conclusion
This article briefly showcases some of the top reasons for introducing AI to your customer service processes. From predicting behavioral patterns to anticipating and answering questions, AI is geared to optimize customer experience – giving you a boost in a competitive market and building brand loyalty through exceptional service while reducing the cost of human labor and training. The list of advantages also goes well beyond the ones listed above – check out a host of providers for more details on how AI can help revolutionize customer service.