As per a report shared by Hubspot,” 68% of consumers say they are willing to pay more for products and services from a brand known to offer good customer service experiences.”
If that’s the case, businesses need to focus on their customer support to achieve customer service excellence. Today’s customer support faces challenges like the rising volume of support tickets, longer resolution times, and growing staffing expenses. All of these challenges lead to operational inefficiency, which results in increasing support costs.
To prevent this, analytics plays a crucial role by providing valuable insights that can be used to identify gaps, customer behavior, common customer issues, and so on. As primary customer touchpoints, AI chatbots and virtual assistants can provide valuable analytics.
In this blog, we’ll discuss the role of conversational AI analytics in reducing support costs.
Let’s get started!
How do Conversational AI Analytics Reduce Support Costs?
1. Identifying Common Customer Issues
Understanding customer pain points is crucial for customer support. To help with this, analytics tracks frequently asked questions, deflected, handed over, and abandoned sessions, helping to figure out:
- Recurring issues or concerns customers have.
- Identifying the gaps in the self-service resources
- Reduction in queries going to live agents
By identifying all of this, it becomes easier for businesses to improve their support operations and, hence, reduce the overall support costs.
2. Optimizing Chatbot Performance
Analytics can provide insights into average response time, average handling time, and the number of cases deflected or handed over to live agents. It also keeps track of all chatbot sessions and feedback, making it easier to figure out the next big thing in action. As a result, you can:
- Assess areas where the chatbot is lacking i.e. areas of improvement
- Evaluate the quality of responses and if they need any further refining.
- Expand the chatbot’s ability to handle a broader range of queries effortlessly.
This in turn, enhances the chatbot’s efficiency in handling queries without any human intervention, resulting in the need for fewer live agents and reducing operational costs.
3. Improving Knowledge Management
Effective knowledge management is all about keeping the knowledge base well-updated. To do so, finding the knowledge gaps and filling them both are important. Conversational AI analytics can help with this by:
- Tracking user interactions to understand what stories greatly impact customer satisfaction.
- Analyzing average handling time (AHT) helps to understand whether the AI chatbots retrieve relevant information quickly or not. High AHT indicates delays in finding useful resources, which means either the knowledge is missing or it’s outdated.
- Keeping track of deflected cases: The higher the number of deflected cases, the more effective the knowledge base will be.
An effective knowledge base makes resource allocation much easier, reduces resolution time, increases operational efficiency, and thus, improves the customer service experience.
4. Reducing Agent Workload
Agents these days are bombarded with a large number of support tickets every day due to a poor self-service experience. By minimizing the number of cases escalated to live agents, businesses can lighten agent workload and improve efficiency. This is where analytics play a crucial role, offering insights on:
- Handover cases to analyze the number of tickets that are handed to the live agent through AI chatbots. A lower handover rate indicates reduced reliance on human agents.
- Average Handling time provides insights into the ticket complexity. Higher AHT indicates complex tickets, enabling intelligent case routing to live agents for faster resolution.
- AHT trends help to identify the peak support times, making it easier to optimize staffing and resource allocation during high-demand periods.
By leveraging these insights, businesses can optimize AI chatbots, enhance the self-service experience, reduce agent workload, and accelerate query resolution.
5. Predicting Support Trends
Customer support should be efficient and always a step ahead of the competition. To achieve this, one needs to stay updated on support trends and patterns to optimize operations accordingly.
In this case, analytics play a crucial role by:
- Tracking customer interactions to identify rising complaint patterns and FAQs so that recurring queries can be addressed proactively.
- Analyzing the handover sessions makes it easier for support teams to identify complex issues.
- Evaluating total sessions reveals support spikes during product launches or holidays, allowing businesses to optimize resources and avoid extra staffing costs.
Thus, analytics can ensure the proactive handling of queries without any increase in support cost, making customer support cost-efficient.
So, What’s Next?
It’s not wrong to say that analytics is your helping hand if you need cost-efficient customer support. Now, the next step is to invest in solutions that provide detailed analytics.
Multiple conversational AI solutions are in the market, but choosing the right one can transform your customer support.
To help you with this, we are offering you SearchUnify’s virtual assistant (SUVA). It provides detailed analytics, empowering you to make informed decisions.
Don’t take our word for it!
