How Chatbot Development Helps Modern Enterprises Move Forward
Chatbots have become commonplace in customer service. But the technology provides a wide range of capabilities extending well beyond a simple conversation. What’s next for chatbot development?
At the turn of the millennium, we might have believed that robotic assistants still belonged to the distant future. By now, though, AI-based solutions have permeated nearly all aspects of everyday life. Chatbots and software agents solve a host of problems, providing support with bookings, shopping, job search, or even breastfeeding. In business, they are disrupting all operational domains—from customer service and process optimization to recruitment and inventory management.
Propped up by advanced in AI consulting, chatbots are becoming increasingly sophisticated. These automated communication systems blend AI, machine learning, image and voice recognition technologies to simulate how humans talk, think, and interact. Modern chatbots not only understand people and provide intelligent answers based on amassed data but can also recognize a person’s mood and act on it. In some cases, these bots are nearly impossible to distinguish from a human. In fact, according to Solvvy’s 2021 State of Chatbots Report, 47% of respondents have mistaken a chatbot for a live agent.
Chatbots Have a Profound Impact on Business
Chatbots are not coming; they are already here, making a great impact on enterprises, mainly in the customer care area. They’re taking over business processes and niches which until recently have been predominantly occupied by people. For individuals, this may cause concern, as many fear software will push off thousands of jobs, mainly in customer services, sales, and outsourcing.
True, the influence of chatbots on our workplace is profound and permanent. However, while some occupations are facing extinction as a result of galloping automation, new jobs will continue to appear, requiring human creativity and personal touch.
Business Benefits Drive the Adoption of Bot Solutions
While cuts in expenses and improved returns on investment are the main drivers behind chatbot adoption, businesses can take advantage of chatbots in other meaningful ways, especially in the sales and marketing space.
For example, customer services automation with chatbots can bring about increased customer engagement, better conversion, and broadened reach. Deep insights into historical interactions and multi-faceted analysis of customer behavior behind such intelligent assistants allow companies to consistently deliver personalized service, improve customer interactions, and create a positive brand image.
Interestingly, even SMEs can reap those benefits now as chatbots are cheap and easy to deploy. What’s more, they require minimal maintenance and can be easily extended with new features if needed. These characteristics make it possible for smaller companies to compete with industry giants by delivering the same level of personalized customer service.
Delivering Enhanced Customer Experience
Although not all consumers are ready to move from live human interaction to conversations with chatbots, more chatbot use cases appear as the technology is progressing. A vast majority of them is found in customer service.
Customer support is a crucial ingredient of any company’s success. Most consumers need assistance at some stage of their dealings with any business, and they expect it to be delivered immediately. With 24/7/365 support becoming a new standard, it’s pretty hard to keep up with these expectations. However, they seem to be well-aligned with the technology capabilities. Services like DigitalGenius already blur the line between a real customer care rep and a machine.
According to the aforementioned State of Chatbots Report, 69% of users would use chatbot to resolve an issue more quickly, while 55% would prefer to use a chatbot immediately instead of waiting for a live agent.
Almost no organization has enough human resources to cater to all communication requirements from customers around the clock. That’s where chatbots step in, allowing companies to interact with every single buyer like a real person and at any time of day. What’s more, with the advancement of machine learning, NLP, and big data analytics, modern bots can now handle even more complex queries to enhance customer experience with a brand further.
Despite tremendous benefits that chatbots offer to organizations, the technology still has its shortcomings. Chatbots still misunderstand the nuances of human communication, resulting in dissatisfied customers and reluctancy to use chatbots in the future. In its 2020 study, Forrester reveals that, on average, only 30% of design team members are involved in chatbot design, which is far less than the 82% working on website design, for example. In a nutshell, this implies that the problems associated with chatbots stem not from the technological proficiency, but from UX design. With more effort and thought put into chatbot development and design, we can expect chatbots to provide flawless customer service in the future.
Streamlining Business Processes with Robot-based Automation
Robotic process automation, or RPA, is the use of software with AI and machine learning capabilities to automate and facilitate business processes. RPA automates mind-numbing repetitive tasks typically outsourced to offshore companies. This leads to a reduction of expenditures, lower operational risk, and enhanced internal processes. While the technology is separate from chatbots, the two can be used in conjunction, and the lines between them are increasingly blurring.
At the foundation of all chatbots, there’s a conversation. The core characteristic of bot software is the interaction with humans via voice or text. However, chatbots are evolving beyond “dumb” virtual assistants toward much smarter conversational bots that can automate specific business tasks. These bots can be integrated with RPA solutions to become a fully functional service that handles not only the conversational part of the customer service process but can also input and manage data in various IT systems, such as CRM, knowledge bases, or ERP.
A practical example of how chatbots can leverage RPA capabilities comes from Amazon and its bot frameworks, Lex, and Elasticsearch Service. The frameworks allow building a fast search bot that supports employees in identifying and retrieving relevant documentation in an instant.
Imagine an admin worker who needs to get specific information from heaps of digital paperwork quickly. By using the solution, he or she may send a request to a chatbot, by text or voice, e.g., “I need to find Mr. Smith’s address,” and the bot will immediately fetch the documentation containing the required piece of data. That’s a brilliant solution with a range of applications across various industries.
Does that sound like replacing humans by robots (again)? Not necessarily so. By taking over mundane, repetitive tasks, bot engines can streamline work for the current staff, making it more efficient and saving time (along with frustration). Other benefits of solutions based on chatbots and RPA include better data quality, relocation of resources to higher-value tasks, and enhanced customer experience.
Providing Deep Consumer Insights
Information is the driving force behind business innovation and customer satisfaction, and now companies have more data at their disposal than ever before. Business intelligence insights provide crucial information for developing an organization’s strategy, identifying new opportunities, and adapting products and services to customer requirements.
Unfortunately, a great number of businesses feel they can’t reap enough value from the market bringing $297.28 billions of worldwide revenue these days (Global Newswire). They name data siloing, lack of time to sort through data, and insufficient resources as the main culprits.
Chatbots won’t solve all the issues tied to unused data but can provide real insights into customers’ preferences by collecting, monitoring, and tracking large data volumes from customer interactions. Chatbots learn progressively; they communicate with customers, obtain valuable details, and feed the latter to the knowledge base to enhance future interactions. They can also automate data retrieval processes and extract data from different business systems, such as ERP, CRM, internal collaboration spaces, etc., to grow their knowledge and gain a wider context in the next conversations.
Chatbots armed with AI and machine learning capabilities can be used to gather information about processes, take actions, switch to a different task, and interact with employees. They help businesses find answers to critical questions about target buyers, such as:
- When customers make purchases
- What products and services they choose
- What challenges they see with the company’s products and services
- What collateral products and services they can be interested in
Thanks to chatbots, companies can leverage a trove of customer information with ease and efficiency. However, big data analytics by bots shouldn’t be narrowed down only to consumers. Chatbots can also provide precious data for infrastructure optimization, process streamlining, or fixing technical issues. Companies that mix and match different chatbot development strategies gain the upper hand over competitors and become well-positioned for success.
Chatbot Development: With a DIY Platform or From Scratch?
By now, we can all agree that chatbots drive immense values for enterprises of all sizes. But how can a company start with this technology? One way is to build a chatbot solution from scratch, another one is to customize an existing platform.
There are a few DIY chatbot frameworks available, such as Azure Bot Service, Botpress, or Microsoft Bot Framework that can be leveraged to deploy bots for particular use cases. These platforms make it fairly easy for chatbot developer team to build a very basic bot and refine it with the desired features. However, if a company is looking to build a scalable solution allowing full control over the conversational flow, it makes more sense to build a completely new chatbot.
One of the reasons is that DIY chatbot builders are usually quite limited, and by default allow only a small number of features to be developed. Typically, those platforms have a limited scope of dedicated applications, and some of them work better in a given context or an industry than others. For example, Microsoft’s framework is well-suited to build messenger bots, Wit.ai can be used for IoT automation, while IBM Watson can be leveraged to design customer support and contextual conversation bots.
What’s more, getting tied to a particular platform may lead to several issues in the future. First of all, there’s vendor lock-in. What will happen to your bots when the platform goes down? Secondly, many chatbot platforms don’t allow external users to extend core features, which may hinder the development of your chatbot in the future.
The choice whether to create a chatbot from the ground up or use an existing platform depends on many factors. While it may take less time to develop a simple chatbot with a DIY framework, this option comes with challenges. At the same time, developing a fully proprietary chatbot to serve your company’s specific use cases seems to work better in the long term.
For years, people have been discussing the conversational potential of chatbots, and now modern technology like deep learning and cloud computing have made this a real possibility. At the moment, we can see chatbots transforming the way companies communicate externally and internally to the point when chatbots are practically becoming a new standard for customer service.
What’s next for chatbots? As the above examples show, the software is making its way into various areas of business operations. In conjunction with other technologies, such as NLP, RPA, and machine learning, chatbots can solve a slew of tasks for organizations. They offload manual routine, provide operational support, and empower business decisions, among other advantages.
It’s safe to say that for chatbots the future is bright. As far as companies are concerned, each needs to decide when and how they want to take the next step with the technology.
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