One of the main functions of a SaaS product company is to provide support for its customers. This “support” function plays a critical role in customer satisfaction, leading to customer retention. The support teams in enterprises are predominantly in the call centres answering customer queries via phone calls, email, and chat. The call center operations are vital to ensuring the enterprise customers are satisfied with their enterprise products and services. The call center provides an omnichannel experience catering to different customer personas. A whopping 58% of CEOs expect their call center volume to increase in the next 18 months according to the McKinsey report. This puts greater pressure on call centers that are already under strain. This may lead to a negative customer experience, resulting in a poor NPS score for an enterprise. Thus, call centers must be equipped with modern technological tools to help their staff assist customers effectively. This article provides a glimpse of how modern Artificial Intelligence tools can help call centers.
Evolution of call centers
Call centers predominantly focussed on phone support during the beginning of the customer experience era. Most of the call center operational staff are given ample training on enterprise products and services such that they can offer solutions to enterprise customers’ problems over the phone. Call center staff had access to a few knowledge silos present inside the enterprise, and this limited knowledge was good enough to serve a small number of customers. However, over time the volume of support calls has skyrocketed, and customers vent out their frustrations on social media if any of their issues go unresolved. This led to many call centers evolving to meet with changing customer behavior and adopt technological innovations. This can be summarized based on three eras.
Predominantly, call centers were providing phone support. The call center staff had access to tribal knowledge of enterprise products and services. The volume of support calls was low.
Predominantly, call centers provided omnichannel support in terms of phone, email, and chat. The call center staff had access to tribal knowledge of enterprise products and services. The volume of support calls was manageable.
Call centers are overwhelmed by the volume of support calls. Call center staff needs access to a single source of truth in terms of knowledge to provide accurate responses. Moreover, call center operations must be modernized with the latest Generative AI capabilities to provide personalized customer support.
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Role of the knowledge base
Knowledge base plays an indispensable role in providing a single source of truth that is trustworthy and always up to date. The knowledge silos can be integrated if enterprises choose to build a centralized knowledge base to ensure smooth operations of their support operations. The key element in building a centralized knowledge base is to
Ensure collaboration amongst different internal stakeholders
This means a knowledge management team must be formed to liaise between different internal stakeholders to have a trustworthy content and avoid information discrepancies between the internal and external stakeholders.
Have a centralized knowledge repository
There are various strategies that can be adopted to build a centralized knowledge repository; The easy strategy is to provide a universal search that spans multiple knowledge silos and provide this search tool to call center staff. Another approach is to build a centralized knowledge repository in addition to internal siloed knowledge bases. There are pros and cons to these two approaches, which need to be evaluated before an enterprise chooses an approach for its business scenario.
Train call center staff
Training call center staff on modern technology tools helps increase support staff productivity as they can find information quickly and serve the customers effectively. In fact, the chatbots are so advanced now that they can deflect most of the low-value support tickets that are often from FAQs.
ChatGPT in call centers
Embedding ChatGPT-like technology on the top of the centralized enterprise knowledge base would supercharge call center staff’s productivity. If this is rolled out to enterprise customers, then customers would get accurate responses to their queries, thus eliminating the need to contact the enterprise support team. The core element of enabling ChatGPT-like technology is the central knowledge base that houses all the enterprise information. This information is reliable, always up-to-date, and trustworthy.
Suppose the support team relies on siloed knowledge bases. In that case, there are more chances that support team members might relay different information to similar customer queries based on which information they are referring to. However, ChatGPT-like technology always provides reliable information for exact or similar customer queries. This helps customers to gain trust in enterprise products and services. The ChatGPT-like technology utilizes a Generative AI framework called Retrieval Augmented Generation (RAG). This framework works in the following manner.
- The RAG framework takes all textual information and converts it into numbers called “embeddings.”
- These embeddings are stored in a vector database.
- Once the customer types in a question, these questions are also converted into embeddings and sent to the vector database.
- The vector database returns the top 5 embeddings based on similar scoring for the customer question.
- The returned embeddings are post-processed to create a response to customer questions.
This RAG framework fundamentally differs from enterprise search which works on “keyword search.”
Thus ChatGPT-like technology can provide the following benefits:
- Increased customer satisfaction
- Increased call center staff productivity
- Reduced time taken to find the right information
- Reduced customer wait time in call center
- Increased customer retention
- Increased brand value
ChatGPT-like technology is poised to make call center staff more efficient and their customer response more effective. A trustworthy knowledge base should underpin the ChatGPT-like technology to be rolled out. The information in the centralised knowledge base should always be up-to-date and should not contain any discrepancies. The Generative AI technology will provide a personalized support service. In the future, ChatGPT-like technology will offer more proactive support rather than generic support, enhancing customer experience further.
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