Helping companies connect with their customers, one quip at a time
Chatbots improve both service quality and customer perception of service quality. Applying the impact of this reality customer service paradigms illustrates the power of this technology.
Chatbots provide automation of several customer service tasks. They provide increased capacity and throughput in a call center. Machine learning, access to scripts, and utilization of databases allow chatbots to complete an increasing proportion of customer interactions without human input. Efficient pre-screening and triage results in more efficient use of human resources. Chatbots can apply machine learning capabilities to their own and recorded scripts. This enhances their performance, but also streamlines and improves customer service processes.
Customers have embraced chatbot interactions to a surprising degree. Simulation of human characteristics is less important than the ability to handle simple inquiries. Inability to handle the customer’s needs does not result in a negative perception if an easy pathway to a human representative is present. Decreased wait times and relief from limited hours of operation are tangible benefits for customers.
Integration of intelligent chatbot capabilities within the enterprise system provides valuable input to customer relations management. This allows individualized approaches to offering complementary products and anticipating future needs. Customer retention and customer lifetime value may be substantially improved. Deployment of chatbots on social media platforms is an expanding area that enhances marketing and sales as well as customer service.
Chatbots leverage AI capabilities to deliver measurable improvement in firm performance. Their power lies in the synergistic application of machine learning, robotic process automation, and natural language processing. The widening availability of APIs enhances the integrability of chatbots. Their external applications span customer relations management, customer service, marketing, and sales.
Azure Bot Service (Microsoft Bot Framework)
Freshdesk Messaging (Formerly Freshchat)
SAP Extension Suite
ServiceNow Now Platform
Smith.ai Live Website Chat
Terminus ABM Platform
ZoomInfo Chat (formerly Insent)
LivePerson Conversation Cloud (LiveEngage)
Oracle Digital Assistant
SAP Conversational AI
Genesys DX (formerly Bold360)
KIBM Watson Assistant
By 2024, Insider Intelligence predicts that consumer retail spend via chatbots worldwide will reach $142 billion—up from just $2.8 billion in 2019
2022 Insider Intelligence, “Chatbot Market 2022: Stats, Trends, Size & Ecosystem Research.”
Various chatbot classification schemes utilize methods of interaction, information gathering and knowledge domain, goals for application, and/or design approach. The classification of chatbots according to the design technique used for response generation is most useful for relating type and application of chatbots.
Template-based chatbots are appropriate for simple task-oriented applications within a closed domain of knowledge. Their design is based on pattern matching and/or the use of artificial intelligence markup language (AIML). The limitation of template based designs is that the more extensive the pattern database and/or the knowledge domain, the longer the response time.
Corpus-based chatbots address the limitations of pattern matching techniques in extensive knowledge domains. Structured query language (SQL) queries facilitate efficient response generation from large traditional databases. Semantic web techniques for data storage provide more rapid and efficient response generation than traditional databases.
Intent-based chatbots address multi-step tasks. They are within a closed knowledge domain. The response generation includes dialogue management, consisting of dialogue state tracking (DST) and dialogue policy optimization (DPO).
RNN-based chatbots are qualitatively distinct in that they are generation- based, not retrieval-based. Techniques such as sequence-to-sequence models applied to parsed input allow natural conversation in unbounded domains. There are potential processing instabilities such as gradient explosion and vanishing. RNN-based chatbots are used when deep learning is required.
RL-based chatbots incorporate machine learning techniques. They are response-based, but they incorporate a contextual basis in choosing query-response pairs. Context is incorporated by the superimposition of a probabilistic gradient analogous to vectoring in a semantic web model.
Hybrid designs are either inductive or sequential. Inductive designs include an RNN-based process to choose from among response candidates generated by pattern-based techniques. The basis of sequential hybrid designs is embedding one type of bot within another. The primary application of this hybrid design is to refine natural language generation.
“Chatbots are fast becoming a business imperative for businesses that want to engage with their customers. Online chat through chatbots has grown faster than any prior channel.”
Eileen Brown, Digital Marketing Consultant, ZDNet
Automation’s nuances allow for dynamic and customizable systems.
Artificial Intelligence for IT Operations (AIOps) helps make sense of the potentially overwhelming volume of data modern IT administrators handle. AIOps aggregates and analyzes growing streams of data, proactively fixes what it can, correlates related events across an enterprise, and surfaces actionable summaries and critical events. IT staff can then intervene accordingly.
Robotic Process Automation (RPA) allows knowledge workers to automate and inject intelligence into existing manual or cumbersome processes. RPA mimics selected IT tasks and automates away portions of a business’ operational burden. Once the ‘bots’ are built, tested, and deployed, organizations can look to reposition and redeploy the saved capital.
Hyperautomation offers real-time intelligence about an organization’s IT systems. Hyperautomation allows companies to cut down on manual redundant back-office tasks, error check, and streamline system processes. Knowledge workers can then be aligned to focus on the priorities of the enterprise.
As a centerpiece of popular Artificial Intelligence, chatbots simulate human engagement by interpreting a customer’s questions and completing a sequence of tasks. NLP has added a complexity to chatbots that allow them to seamlessly act as customer service agents, virtual assistants, and payment processors.
Scripted systems are created utilizing specific scripting languages. These scripted systems are then used within an application, typically a shell for process automation such as phone application updates, server updates, website updates, and data management. Predetermined scripts and shells built to develop, test, and debug software and computer programs ensure limited human error and security.
Automation solutions are becoming a staple of1 IT investment. Use cases range widely–from increasing customer satisfaction to liberating employees of dull, mindless tasks. Companies that fit automation into their processes cut costs and free up their human capital
Depending upon the type of business and business needs, chatbots serve a number of primary purposes in addition to customer service.
Effective use of chatbots in sales and service contributes to marketing by enhancing the firms brand image. Consumers with a previous positive chatbot interaction are open to being approached by chatbots. Social media and messaging applications are a powerful emerging marketing channel in which chatbots are efficient and effective. Well-designed chatbots leverage digital marketing analytics and individual consumer characteristics to seamlessly drive sales, service, and marketing.
Intelligent bots assist customers in choosing products and services that fit their needs. A greater percentage of motivated potential buyers are converted if the transaction is completed promptly. Complexities in choosing from among similar products, delivery options, and financing services require bots with deep learning capability for optimum performance and customer satisfaction.
Customer touch points mediated by chatbots allow for theincorporation of demographic and psychographic characteristics. Chatbots on social media and messaging applications can identify high quality leads for a company’s products or services. The ability of chatbots on social media to both generate and convert leads is enhanced by their real time interaction.
Chatbots for this application may be simple task-oriented designs. Implementation of bots with machine learning capabilities enables time-based departures from simple templates to achieve dense appointment scheduling. The goal is a balance between availability of appointments and the maximization of output from human resources.
Product owners and business leaders can now petition for completely new and powerful chat-based user experiences and user paradigms. This can completely revitalize a product and its future use cases.
Senior consultants with previous experience with these types of projects can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.