How can AI Chatbots help the telecom industry? From improving major KPIs like customer experience and providing support 24/7 to reducing fraud and operational costs, smart chatbots and Artificial Intelligence in telecom contact centers are becoming increasingly popular but require preparation. Here we will assess key points to consider when preparing to adopt Conversational AI solutions.
Conversational AI is standing out among new technologies that are transforming how telecom enterprises operate. The most proficient telecom players have been quick to adopt AI-powered bots to improve customer experiences, drive contact center efficiency, reduce operational costs and detect network anomalies and fraudulent activities.
Considering the many uses and benefits intelligent chatbots provide to contact centers the industry, this is hardly surprising. Compound annual growth rates and investments in global AI within the telecommunication sector are rising. AI solutions are here to stay.
A major factor for this surge of interest in Conversational AI is that it delivers numerous benefits to telecoms companies over a wide range of departments. However, a focal point where intelligent chatbots have caused significant improvements is in contact center customer support and experience. Before adopting Conversational AI platforms, here are some points telecom companies should consider.
Modern consumers are tech-savvy and have high expectations of the brands they interact with. The telecom industry has a growing frequency of interactions between customers and providers. The younger the generations, the more they use devices and demand instant, personalized services 24/7.
The accelerated pace of technological development is changing customer behavior, and enhancing interest in interconnected, smart and automated features. With the introduction of Conversational AI, this decade will see more than a third of the population belong to a generation that has replaced display-focused communication with conversation-focused platforms.
This new focus will impact the telecommunications industry, where reducing customer churn is essential and where very similar products and services lead to a fiercely competitive landscape.
Customer experience has already become a major differentiator that influences the success of a business.
Such is the influence of customer support, that one-third of consumers would question their loyalty to a brand if the customer service did not meet their expectations. Equally, telco companies that provide exceptional customer journeys in their contact centers have higher recommendation rates, customer retention, revenue and a greater likelihood to cross-sell and provide additional services to their clients.
The importance of optimizing call centers and customer support is undebatable. However, implementing changes and maintaining market-leading customer services is expensive and requires preparation.
If call centers and customer support provide services that are below-par, the risk of losing a significant amount of revenue increases alarmingly. Telecoms must be well equipped to implement the appropriate solutions and make the right choices to ensure that their services meet customer requirements and keep them in the vanguard of their industry.
Many industries are wary of change and the risks it may convey. However, the telecom industry is extremely fast-paced and subject to changes in consumer habits and technology that means it must always be prepared to innovate and evolve.
In the past, key telecom and mobile companies have been swept aside after failing to evolve and adapt to industry innovations, market competition and consumer habits. It is for this reason that telecoms must always be planning on staying ahead of the curve.
Opening to new technologies like Conversational AI is an important step to take to embrace innovation and a stronger focus on customer experience. However, it is imperative to choose the right conversational AI technology.
There are hundreds of chatbot development tools available but, given the high standards of customer demand and the KPIs established for telecoms, simple bots are not enough. Advanced AI chatbots, like the Teneo-based solution, go beyond linear conversation flows and navigation-based decisions, and even Machine Learning.
Teneo’s conversational AI chatbots include natural language processing and sentiment analysis to understand a customer’s intent, regardless of how a question is phrased and the structure of the conversation flow. Not only that, the best conversational bots can carry out complex operations without having to rely on a physical agent.
Typical contact center procedures such as resolving FAQs, checking and upgrading an account, and managing refunds can be carried out with these platforms. These processes can be combined with external providers and other software, such as Robotic Process Automation (RPA) and CRMs to provide fully integrated solutions.
Conversational bots are not only limited to resolving queries and operations. Teneo’s conversational AI offers access to conversational data that provides valuable feedback on customer habits, feelings and flows. This way, decisions and optimizations can be made based on the direct views, interactions and opinions clients have towards a company.
Initial hesitancies when adopting new solutions are a common trait in businesses. Therefore, it is important to seek assurance that there will be a positive ROI. We know that chatbots can provide support anytime, anywhere. This is not only beneficial to customers who can have their queries resolved 24/7, but also to the telecom call center companies who do not have to worry about customers queuing to be attended and who can deploy their agents to resolve more complex issues to maintain customer satisfaction and loyalty.
According to Deloitte, 40% of Telecom, Media and Tech executives confirm that they have saved “substantial” benefits and savings thanks to cognitive technologies.
Job losses and unemployment are a major concern when companies and employees discuss chatbots. Many people are apprehensive of the impact automation can have on the job market and workforce. However, the focus of conversation AI solutions is not on substituting human agents with virtual agents, but on leveraging resources in a collaborative manner to provide the best possible user experience.
Conversational bots can help overcome language barriers and streamline processes such as bill payments or account information while providing omnichannel experiences. In moments of mass-network shortages, AI chatbots in the telecom industry can help with large volumes of requests.
More importantly, they can handle these routine call center requests and allow human agents to focus on high-level queries that require expertise. Conversational bots can also pick up information and details that can be relayed to human agents, who will not have to ask the customer to provide their personal details again.
With this, contact center processes and query resolution rates are improved, and agents can meet their KPIs more proficiently and focus on applying their expertise to resolve customer requests.
We have seen that conversational AI provides more than simple answers to easy questions. In order to fully optimize ROI, conversational AI must be as complete and adaptable as possible in accordance to industry expectations.
Choosing the ideal platform may not seem easy, however steps can be taken to ensure that at least thorough research is carried out to know what is needed in order to select the idyllic platform for a telecom enterprise’s call center.
The need for these platforms to be conversational and to provide personalized, human-like interactions across different channels and a user’s native language is a given. Conversational platforms must be controllable, so that machine learning processes are not fully exposed to external inputs that may be vulnerable to abuse or manipulation. Hybrid approaches that include both machine learning and human-crafted linguistic models allow AI development tools to get the best of both worlds.
Telecom enterprises must also look for features that aid speed of development and allow flexible integration with external systems.
Conversational platforms like Teneo can be easily built once and deployed in multiple languages and channels, with integration with other applications. Additionally, these scalable AI platforms can be recycled from one project to another. With Teneo, for example, about 80% of a platform can be taken and reused. This saves times, resources and data.
Additionally, these platforms must have the ability to maximize the potential value of data, while complying with data privacy and security regulations like GDPR and be tailored to each organization’s demands.
Choosing the right platform is an essential step, but it is important to be able to measure the productivity and performance of a conversational platform to determine that it is up to standards and meeting business needs.
Beyond measuring ROI, different metrics can be used to measure the success rate of the telecom AI chatbot. From determining customer satisfaction to seeing whether conversational platforms guide users to take certain targeted actions in a contact center, we will explore some of the principal Key Performance Indicators (KPIs) here.
Containment Rate or Goal Completion Rate
Chatbots analytics can help identify business ROIs, and the issues that most concern your customers. For example, spotting and measuring case resolutions in a contact center can help measure the success of a bot and identify repetitive questions to make decisions based on this feedback. However, if the reason for the platform is to promote other services, better deals or upgrades, then a different goal completion rate would be considered to prove the business value of this AI solution.
If customers are comfortable using the chatbot, this can be reflected by seeing the percentage of users who have consulted the service within the call center during a given period. This feedback shows customer acceptance of the bot and highlights areas of improvement if certain queries are made frequently.
Bounce Rate and chat volume
The number of users who access the contact center and leave without interacting with a chatbot is seen in the bounce rate. High bounce rates signal that something is wrong with the bot’s ubication or the website’s design and affects customer experience. A high chat volume, however, indicates a higher user base and acceptance of the bot.
Fallback Rate (FBR)
Chatbots need to learn, and there may be certain queries that bots do not understand. The fallback rate gives insights about when a bot is unable to understand a user request and solve a query. In Hybrid approaches, as there is greater linguistic control of the bot, it is easier to spot moments when a customer has been received the reply “Sorry, I do not understand”, and act accordingly. However, fallback rates are also more relevant and easier to detect in rule-based bots, as Natural Language Processing allows more leeway for personalized interactions.
Customer satisfaction is the best way to identify the quality of customer experience. Metrics such as Net Promoter Score (NPS) or qualitative and quantitative surveys can be used to measure whether customers are happy with the way the bot has solved their requests.
Digital, device-dependent customers have raised the bar in their demands for better customer experience, and a key part of it is based on how telecoms companies reply to their requests. Speed, efficiency and personalization are key elements that are being sought and Conversational AI platforms have been identified as a clear solution. Companies within the telecommunications must establish plans that are measurable, realistic and attainable and find the best ways to keep in track with new market trends and be part of the digital revamp the industry is going through.
Would you like to know how other companies are using Conversational AI to gain a competitive advantage? Click here for our webinar on how Swisscom has taken customer experience to the next level.