WhatsApp, Facebook Messenger, and other social chat applications have opened a brand-new avenue to get in touch with customers. And customers love to use them because they don’t have to change apps; they get customer service right in the apps they use the most.
If given a choice between searching for answers and talking to an agent to get answers, customers will always choose the least-effort path. This is because these platforms provide instant gratification which doesn’t take long for users to get addicted to.
Given the number of users using these platforms, they cover markets for almost every industry. It is clear that the stakes are high. However, it is also clear that companies are not doing enough to handle the massive influx of customer feedback.
According to a report, American companies lost about $1.6 Trillion USD to customers switching due to poor customer service.
So, how do companies handle the volume of complaints across multiple channels without wasting resources or losing efficiency?
The solution involves automation that takes advantage of the existing customer service framework and delivers intelligence to agents allowing them to improve their service levels at scale.
How Artificial Intelligence is contributing to Customer Service
When thinking of AI, chatbots are the most natural application that everyone can recall rather fondly. But modern AI has gone far beyond plain rule-based chatbots.
Leveraging AI in marketing helps the team understand customer feedback better and gather crucial insights to enhance the quality of their marketing efforts. Several AI applications in customer service help reduce the time to respond to each query and also improve the overall customer experience through the process of resolution.
In other words, there is a lot more to AI in customer service than just chatbots.
1. AI-based Shopping Systems
AI has overhauled the way companies sell and customers shop. Artificial intelligence in eCommerce has opened up endless avenues of possibilities for engaging customers.
Product recommendations in e-commerce were once based on categories similar to what the user bought. Now, an AI-based system can recommend products based on the customer’s order history, similar products, and the buying habits of other customers of the product.
Amazon, Netflix, and Spotify are already recommending your favorites based on your own preferences as well as preferences of those who have similar viewing habits as you.
A tool called Shelf AI combines voice search bots like Amazon Alexa and Google Home with AI and Machine Learning (ML) to deliver superior shopping experiences. It learns each individual shopper’s behavior through order data and uses contextual knowledge and semantic precision to deliver better suggestions.
Companies like Botgento and Octane AI build interactive Facebook bots to provide a more organized shopping process for sellers such as Shopify store owners. These bots allow customers direct access to the online store from within the Facebook messenger. They can navigate their order list, explore favorites and recommendations, and even manage their wishlists.
2. Automated pre-processing of customer queries
How do you improve the productivity of customer service agents without costly approaches such as increasing their hours or hiring more agents?
The answer is to pre-process the queries across all channels and attach additional contextual information to customer tickets.
A basic ticketing system allows centralized, intuitive access to tickets, reduces the turnaround time and improves the efficiency of the agent workflows.
Modern ticketing tools also add analytics and reporting capability which gives a big picture of the types of tickets, nature of tickets, sources of tickets, and so on. It gives the customer service team a high-level overview of problems faced by customers. They can then work on creating training content for teams to solve such repetitive queries quickly and decisively.
With AI in the mix, the system can automatically identify the low-priority and low-effort tickets. They can send automated content or knowledge-base articles as part of their content marketing strategy to the customer adding a self-service capability. Tickets that cannot be solved through self-service can be passed on to agents with additional contextual information for a quick resolution.
Tools like Fusion CX can also enable bot-based resolution for low-effort tasks such as password change or plan upgrade/downgrade.
Modern applications of Machine Learning in customer service
Machine Learning is a subset of AI which takes the automation in customer service tools beyond simplistic rule-based associations. ML capabilities allow the tool to analyze and learn from existing data and identify patterns to draw accurate insights and provide suggestions.
ML services work behind the scenes and empower agents to provide better customer services. Their effect is not as explicitly apparent as AI-based applications mostly because these ML services are subsets of the AI functionality.
Organizations of scale can provide personalized services to users by learning their behavioral patterns. By unlocking the wealth of insights hidden in customer data, ML services allow AI applications to provide more relevant suggestions thus enhancing customer’s delight and improving chances at retention.
3. Fraud detection services for users of financial services
American Express (AmEx) uses ML to analyze millions of transactions to find fraudulent transactions. Their core service compares native user patterns with known patterns of fraudulent transactions and derives insights about the specifics of the fraud. This fraud data is then fed to an AI application as rules which powers fraud detection.
Customers are relieved almost instantly because they have an automated system that can detect fraud on their cards. This scores massive customer loyalty points for AmEx and sets them up as a modern banking services provider that truly cares about its customers.
What’s great about these services is that they are not one-time, data-dependent applications. They can learn from ongoing patterns and adapt their insights as threats evolve.
4. Customized insurance discounts for users
Progressive, a group of insurance companies, runs a program called Snapshot for its auto insurance customers. The program takes input from a mobile app or a plug-in device to monitor the user’s monitoring habits.
It analyzes the user’s driving habits over a period of 6 months and compares these habits to safe driving protocols. It gives users a driving which it uses to provide discounts based on the user’s safe driving habits. The safer they drive, the higher discounts they get.
5. Navigation suggestions inside a large campus
Disney’s theme parks provide rich, entertaining experiences for the entire family. To help enhance this experience, guests are given wristbands called MagicBand. These bands hold information about payments, tickets, and even act as room keys.
As users navigate through the park premises, the band provides intelligent suggestions on the better route through the park. During summer and on business holidays, the crowd patterns are picked up via various touch points throughout the park. The ML system analyzes the crowd movement patterns and provides insights for crowded rides and routes.
This allows users to save time and avoid inconvenience in heavily crowded areas.
Modern customers are proactive, smart shoppers. They don’t want to waste their hard-earned money on brands that don’t provide exceptional services. They read online reviews before buying from a brand for the first time.
Customer experience is quickly becoming a competitive differentiator for all contemporary business. Most of the customer’s purchase journey happens on the brand’s online assets, which are opportunities for the brand to curate these experiences.
AI and ML provide innovative solutions in customer service. AI and ML applications are helping companies compete better in a hypercompetitive market.