Ways in which customer support processes can lower churn

Why churn is such an important metric

Customer support plays a crucial role in reducing customer churn because only 1 out of 26 unhappy customers really complain and the rest simply churn out, this is why it’s important to pre-emptively identify signs of possible churn and satisfy the customers.

It can be extremely expensive, and not to mention difficult to acquire new customers instead of retaining them. Not to mention the advantages of longer customer relationships on the brand itself. Most companies looking for a new product will find reviews to be a dealbreaker and reviews are almost directly a result of great customer experiences.

From a churn perspective, some would argue that the customer experience is just as important as the product because, without it, customers tend to slowly drift apart in search of a product that values them. A product can be extremely intuitive and user-friendly, but usage concerns are not always linear, causing a need for concerns to rise more often than not, which is where how that query is resolved will be the difference between a good or bad customer experience.

Identifying churn patterns

Every customer interaction, transaction, and feedback point is a piece of the puzzle. AI excels in piecing together these fragments to form a coherent picture of customer behavior and satisfaction levels. By analyzing patterns in usage, purchase history, and support interactions, AI can forecast potential churn risks with remarkable accuracy.

AI can help point out abnormalities - these could range from changes in buying patterns to decreased engagement or negative feedback. AI tools can alert businesses about these red flags in real time, allowing for immediate action. For instance, a sudden drop in usage might prompt a personalized check-in from the support team, addressing any underlying issues the customer might be facing.

By grouping customers based on behavior, preferences, and risk levels, AI enables businesses to segment customers into groups and craft targeted strategies. High-risk customers can receive special attention or offers, while satisfied customers might be engaged differently to foster loyalty. This segmentation ensures that interventions are not just timely but also highly relevant and personalized.

Streamlining onboarding with AI

The customer journey begins with onboarding, a critical phase for retention. AI-driven onboarding processes can personalize the experience, guiding new users through product features and functionalities tailored to their specific needs. This not only fosters product understanding but also builds a foundation of customer satisfaction and loyalty from the outset.

Empowering support agents with AI assistance

AI systems can quickly sift through extensive databases to find relevant information, from customer histories to product details. This means agents spend less time searching for information and more time-solving customer issues. When a customer contacts support, the AI can immediately present the agent with the customer's previous interactions, purchase history, and even preferences, allowing for a more personalized and efficient service.

AI doesn't just offer information; it also suggests solutions. By analyzing similar past queries, AI can recommend effective resolutions to agents. This guidance is particularly valuable for complex or uncommon issues, ensuring that even less experienced agents can provide expert-level support. Furthermore, AI can identify patterns in customer issues, leading to insights that might help preemptively resolve widespread problems.

AI can offer real-time support to agents during customer interactions. For example, as the conversation progresses, the AI can analyze the customer's tone and sentiment, providing the agent with insights and suggestions on how to steer the conversation effectively. This real-time assistance ensures that agents can adapt their approach on the fly, improving the chances of a positive outcome.

Threado AI for agent assistance

Threado AI is a customer support agent co-pilot that helps companies reduce support handle time, improves response quality, and makes it easy for agents to find the right resolution instantly thus enhancing the customer experience. For customers, the Threado AI bot makes self-service easily deliverable to customers because it can be trained on multiple data sources including multiple integrations, making the bot smart enough to extensively answer customer concerns without having to speak to an agent.

You can train Threado AI on past customer tickets, product documentation, SOPs, or any structured or unstructured data and assist agents where they need it in real time to help them respond to customer queries.

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What does the product do to assist customer support agents?

• Threado agent assist: A Chrome extension that sits on top of any helpdesk or ticketing platform uses AI to summarize customer tickets or conversations. The bot is also capable of auto-suggesting responses based on customer questions and the agent can ask the bot to auto-improve responses to fit the tone, sentiment, and intent of the customer.
• Omnichannel support: Apart from providing agent assistance for ticketing platforms, the bot can be installed as a chat widget within the product, embedded on the website, and installed on Slack or Discord to auto-respond to agent queries. Threado is the only support tool that offers support on Slack.
• Improve knowledge base: Agents can leverage the bot to get well-formulated answers to customer queries and fill in the gaps of the unanswered queries by improving the knowledge base aka data on which the bot has been trained.

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AI-powered self-service: The future of support

AI-powered self-service are intelligent portals that act as the first point of contact for customers seeking support. These portals are equipped with advanced search functionalities, intuitive navigation, and personalized content, making it easier for customers to find the answers they need. By understanding user queries in natural language, these systems can deliver precise, context-specific information, enhancing the customer’s ability to resolve issues on their own.

AI transforms traditional FAQs and knowledge bases into dynamic, interactive resources. These tools can learn from customer interactions, continuously updating and expanding their content to reflect common queries and emerging issues. This ensures that the self-service resources remain relevant and effective over time, adapting to the evolving needs and behaviors of customers.

There are also virtual assistants and chatbots, offering a more conversational approach to issue resolution. These AI-driven chatbots can understand and process user queries in natural language, providing responses that are both accurate and contextually relevant. They can handle a wide range of tasks, from answering common questions to guiding users through troubleshooting steps, and even assisting in transactions or navigating the website. This level of interaction mimics a real-life support agent, making the self-service experience more engaging and effective.

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Reducing support agent dependency with conversational AI

A great product sure is helpful when it comes to crafting great customer experiences, but delivering instant support is almost just as crucial. Conversational AI can have intricate conversations with customers without the need for any human intervention. It can understand tone, sentiment, and historical context to have well-structured conversations that are not only relevant but also empathetic.

Conversational AI is tireless, providing constant support to customers around the clock. This uninterrupted availability means customers can get their queries addressed anytime, leading to quicker resolutions and a better overall experience. Instantaneous responses by AI-driven chatbots or virtual assistants eliminate wait times, a common pain point in traditional customer support.

Conversational AI can deliver highly personalized support by accessing and analyzing customer data. It can recall past interactions, preferences, and customer behavior, tailoring its responses to suit individual customer needs. This level of personalization makes customers feel valued and understood, fostering loyalty and reducing the likelihood of churn.

AI can be detrimental to reducing churn

Churn prevention is more than just about addressing customers’ concerns but identifying signals that might be hinting at customers pulling away. Some of the signals that can be identified are - patterns of inconsistent product support from the team, declining patterns in product usage, too frequent support interactions, hints of an increase in frustration with the product, and consistent negative feedback or dissatisfaction with the product.

Being able to identify these signals and pre-emptively pay attention to them is a possible solution but with the implementation of AI, these signals can entirely be prevented if AI takes care of the frontline of support through self-service chatbots, empowering support agents, and conversational AI.