Without personalised attention, customer loyalty is hard to come by today. 63%1?of customers expect personalisation as a basic standard of service. Anytime, anywhere access to an unlimited array of choices coupled with low switching costs, makes it easy for customers to switch to brands who provide a more delightful, personalised experience. One-third2 of shoppers, of all ages, are quick to abandon brands that don’t meet their expectations. Yet, the customer experience capabilities and mindset of many companies have failed to keep pace. They continue to use legacy technologies that are unable to scale and provide personalised attention to customers, threatening their competitiveness.
Today, instant gratification has become a customer expectation – the norm rather than a luxury. Customers expect rapid responses to their queries and concerns, and chatbots allow businesses to meet this expectation.
60% of millennials, for instance, regularly use chatbots and over 70% of those users report a positive experience. A cohort of over 83 million individuals, millennials account for a quarter of the US population and spend over $600 billion annually. Independent and tech-savvy, they will account for 75% of the workforce by 2025. Brands must proactively cater to the preferences of this increasingly important, discerning customer segment with significant spending power or risk losing market share.
Well-designed chatbots are a win-win for customers as well as businesses. Customers enjoy benefits such as 24-hour service and instant, personalized responses while companies stand to realise the following outcomes:
The State of Chatbots Report asked users, “What do you predict you would use a chatbot for?” The top use cases listed below reflect the top frustrations reported by customers.
A chatbot is a computer program capable of simulating human conversations. The interaction takes the form of text messages, voice, or both – all in natural language. The Chatbot Use Case Framework in Table1 highlights some simple use cases to help you get started.
|Use Case||Overarching Intent||Sample Customer Query||Typical Entities (i.e. data types involved)||Chatbot Actions|
|Answer frequently asked questions (FAQs)||Find answers quickly||How can I upgrade my current mobile phone plan?||Customer ID, current plan, upgrade options||Lookup the current plan for the customer and display upgrade options.|
|Capture and qualify sales leads||Improve sales conversion||I’m looking to buy health insurance.||Age, gender, occupation, medical history, i.e. the dataset needed for health insurance underwriting||Collect the needed data, send the data to the underwriting process, and display health insurance options in real time.|
|Create and manage customer service requests||Resolve a high percentage of service requests efficiently – without live agent support||I need to schedule an appointment to resolve an issue with my utilities.||Customer ID, preferred appointment time||Look up the customer ID, for plan, location, payment status and display appointment time choices.|
Table 1: Indicative chatbot use case framework
The use cases identified in Table 1 are only indicative. Chatbots can be deployed to achieve a variety of customer service goals – from getting a quick answer in an emergency, resolving customer problems and getting detailed answers to finding a human customer service assistant, making reservations, upgrading plans, and providing suggestions for next best actions.
Making the most of your investments in chatbots requires a strategic well-planned approach to their deployment. It is recommended that businesses start small, with a small pilot to build business confidence, and progressively identify more mature and sophisticated use cases over time for larger impact. Here are three things to do when kick-starting your pilot:
Constitute a multi-disciplinary team and answer the following questions – contextually and in detail.
Mail your responses to Firstsource experts and receive a complimentary session designed to help you develop a custom-built, actionable roadmap based on your chatbot priorities.
The top-level architectural components of a chatbot typically include User Interface, Dialogue Management, various APIs, Data Sciences, Knowledge Base, Analytics, Infrastructure, Security and Privacy. It’s important to find the right-fit talent for each of these tracks to ensure integrated development of all components – right from the get-go.
Not only is it crucial to identify relevant metrics across key areas (see Table 2) but also monitor them contextually. For instance, a long chatbot-customer interaction could either mean an engaging customer conversation or that the chatbot struggled to provide the right answer quickly – depending on the context.
|User Metrics||Chatbot metrics||Business goals|
Table 2: Identifying relevant metrics
Once you have successfully implemented a pilot, the next step is to leverage the right tools to evaluate your progress. Check out our other blog on chatbot strategy framework and maturity model – ‘3 tools to evaluate your progress on chatbots‘ to learn more about how to assess your chatbot journey success.