AI-empowered chatbots with some personality attached

Helping companies connect with their customers, one quip at a time

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Chatbots and customer service: a win-win proposition

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.

Performance Benefits

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.

Customer Satisfaction

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.

Broadening Range of Services

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.

Designing the Chatbot Application Stack

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.

  • Answering customer questions and/or complaints
  • Driving customer engagement, understanding customer sentiment, then acting on it
  • Improving up-selling and cross-selling and optimizing the selling process
  • Reducing customer churn
  • Creating one-to-one marketing channels for customers
  • Replicating in-store customer conversations on mobile
  • Increasing customer service efficiency

Platforms & software

  • Azure Bot Service (Microsoft Bot Framework)

  • Chatfuel

  • Drift

  • Freshdesk Messaging (Formerly Freshchat)

  • SAP Extension Suite

  • ServiceNow Now Platform

  • Live Website Chat

  • Terminus ABM Platform

  • XOR

  • ZoomInfo Chat (formerly Insent)

  • LivePerson Conversation Cloud (LiveEngage)

  • ManyChat

  • Oracle Digital Assistant

  • Qualified

  • SAP Conversational AI

  • Genesys DX (formerly Bold360)

  • Helpshift

  • KIBM Watson Assistant

  • Intercom

  • Khoros Care

  • Aiwozo

$142 Billion

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.”

Classification of Chatbots

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

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

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

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).

Recurrent neural network (RNN)- based

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.

Reinforcement learning (RL)- based

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

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

Full suite of automation services

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.

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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.

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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.

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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.

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Scripted Systems

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.

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Automation now an expectation

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

The many software invocations of chatbots

Depending upon the type of business and business needs, chatbots serve a number of primary purposes in addition to customer service.

Sales & marketing

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.

Quote generation

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.

Lead generation

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.

IT support

Management of complex IT systems involves a high degree of automation and AIOps to allocate resources and to detect system flaws. Chatbots function as an interface between automated system control processes and human operators. This application is in active evolution.

Appointment booking

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.

Development alternate paradigms

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.

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