The use of AI in customer service has gained popularity in the past few years but has grown even more prominent since the 2022 boom of AI tools. AI significantly helps in carrying out menial, repetitive tasks, and can even be proactive which personalizes the customer experience.
A study by Accenture showed that 73% of customers prefer businesses that use data to predict their needs and personalize their experience.
Your customers are individuals and expect to be treated as such. According to Qualitrics, businesses lose $4.7 trillion in revenue due to bad customer experiences. With this knowledge of the relevance of a positive customer experience, we can appreciate why many companies are seeing the value in AI.
Generally speaking, a model can be trained in two ways: statically or dynamically. With static AI systems, the algorithm is trained offline before deployment, and then “locked.” It can only continue to learn if someone updates it. Conversely, a dynamic model is trained online allowing data to constantly enter the system and be incorporated into the model.
As you have seen, there is tremendous value in customer satisfaction. Businesses normally interact with customers by grouping them into demographics. Doing this works reasonably well with static AI systems that simply respond to previous input data. The problem with this approach is that it is rigid and doesn’t account for individual customer preferences. What’s more, even businesses that try to account for each customer's uniqueness still face another concern – that each customer’s preference is subject to change. This is why you need a dynamic customer support system that can adapt to your customers’ needs as they change over time. Let’s look at some other challenges a human or static AI customer support system faces.
Data is a vital part of customer support for any business. Customer support needs data on the customer's needs to suggest the best solution. However, static customer support systems or human teams are very limited in data handling. This is not for lack of trying but because there is only so much information that can be processed and made sense of in time to develop customer-specific solutions.
This challenge is related to the limited data handling capacity of a human-run or static model customer support system. A dynamic AI system can sort all the data contained in it and predict trends in seconds. For static systems to achieve a similar result, a team must spend more resources retraining the system with every new piece of customer data that comes in and even this will still only partly solve the problem of predicting customer behaviors.
A static system is also less likely to have all the required product information to solve each customer’s needs. This will typically cause a decline in customer satisfaction as customers go through several agent transfers until their needs are met. This departmentalization of customer support can be easily fixed through using dynamic AI as all product data can be fed to the system.
AI has become even more valuable and helpful in customer support as we evolve from reactive and static to adaptive and dynamic AI solutions. The earlier versions of AI were limited in that they were fed data with predefined rules and responses based on specific inputs. This type of AI did not account for nuances in input; hence, its use in customer support was best limited to chatbots to help collect customer data as human support was awaited. These traditional, reactive AI systems had a two-channel approach to machine learning where data was collected offline, and the system was then trained to understand and react to the data.
However, an advanced form of AI, adaptive AI, has now been introduced to solve these limitations. This advanced, dynamic version utilizes adaptive machine learning to enable the system to learn as data is continuously fed into it in real time. This AI can modify its algorithms and make new decisions as it receives further information.
The ability of adaptive AI to mimic the human brain’s adaptability to changes as it gains new data is known as auto-adaptive learning. A machine learning model is trained either by supervised or unsupervised learning.
This is usually done when the expected output (response to the input data) is known. The AI develops its models by classifying data inputs or using regression techniques to predict continuous responses. This classification technique is used for inputs that can be grouped, while regression is largely used for ranged data or real number outputs.
Unsupervised AI learning is used to extract trends from input data with no labeled response. It utilizes clustering, association, or dimensionality reduction to find conspicuous patterns from a data set. Unsupervised machine learning in support is beneficial for AI to adapt easier to unpredicted input data from customers.
The personalisation of customer experiences is one of AI's major advantages in building dynamic customer support. It makes the customer feel seen, heard, and truly cared for. It also gives adaptive AI systems an edge over traditional ones.
Working with a Neople (AI-powered digital assistant) for customer support improves customer experience as it is trained on your specific business documents and data–including relevant conversations, product information, brand tone, and other relevant data. This gives your Neople a level ground to start interacting with your customers.
As an adaptive, dynamic AI, your Neople AI-powered digital assistant learns from each customer interaction, adjusting its approach and thus improving with each new interaction. For instance, meet Bob, Toppy’s customer support Neople. Bob has responded to many tickets since his induction into Toppy’s customer support team. Since being there, Bob has received tons of employee feedback. Using this feedback loop, Bob has become even better at proposing answers to questions directly on CM.com.
Looking to incorporate adaptive AI like Neople into your customer service? Here are some of the most significant advantages your team can gain by using a Neople.
Evolving AI can receive live data and provide real-time solutions to individual customer needs. This will provide a productivity boost for your customer service team as they will be able to focus on other aspects of the job while taking on a more passive role in customer interactions.
Both human customer support and reactive AI are prone to errors and misunderstanding while resolving customer conflict. However, with adaptive AI, these errors are limited as the AI quickly learns to understand the customers better.
The relationship between your business and its customers is mutually beneficial. They need your services and products and you need their business. When customers have a problem, you need to offer solutions quickly and efficiently. Customers want to feel like people and not disposable entities. An adaptive AI will quickly find trends in the data and ‘recall’ previous customer interactions, making interacting with it feel personalized.
Imagine a customer returning with a problem and, rather than simply being met by a monotonous response, they are recognized and greeted by name before being asked about their problem. This alone will improve the customer’s mood and increase the likelihood of a good customer experience by the end of the interaction.
In the past year, we have seen AI like Open AI's ChatGPT reach possibilities many never imagined. Well, guess what? There is still so much potential for learning AI, which we have yet to experience. Some of the potentials of adaptive AI include:
While AI can’t replace humans in customer service, its presence on the customer service team allows it to handle jobs best fulfilled by automation while freeing humans to focus on tasks that require our innate abilities.
As adaptive AI models are updated, their ability to utilize your customer and business data will enable them to recommend strategies for how to reach and retain customers for your business. The humans in your customer success team will then be able to refine these recommendations to create the best customer experience possible.
What businesses currently experience with adaptive AI systems is just a fraction of its potential in the industry. Dynamic AI’s ability to continuously improve as it interacts with customers and receives feedback makes it user-focused and even more likely to bring about excellent user experience for businesses. Over time, AI will create even more personalized customer messages and experiences.
The possibilities of adaptive AI in real-time customer support are limitless ranging from continuous AI learning from the extensive data available to trend prediction and personalized customer experiences. As prioritizing customers is one big way to increase your business revenue, investing in systems that significantly promote customer satisfaction is the way to go for your customer support team!
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