Success Metrics for Chatbot Adoption across Industries

Published on Sep 18, 2019

Updated on Dec 13, 2019

According to Oracle’s Executive survey, 80% of leading consumer-facing businesses have already used or are planning to use chatbots by 2020. Chatbots are scalable and cost almost nothing in operation as compared to their human counterparts. But, how practical is chatbot adoption for your business? Let’s see.

5 Key Success Metrics for Chatbots

Different industries can utilize chatbots to serve different purposes. Accordingly, the parameters to measure ROI may vary. For instance, marketers may consider lead generation as a criterion while the sales department takes conversions from chatbots into account. But, of course, the decision to opt for chatbots depends on specific quantifiable measures — to solve specific customer support processes.

#1 NLP Maturity

It is the average maturity level of Natural Language Processing capability of bots, measured by the way bot interacts. Initiating conversations with customers is a key focus area among organizations these days. To achieve this, bots have to be well trained in industry-specific jargon.

For instance, if a retail customer has a question about a brand’s return policy, the bot should be able to meaningfully understand the user’s query and provide relevant information as it relates to that specific question, as opposed to an information dump or worse yet failing to understand the query itself. If a bot is unable to process the user input, it contributes to ‘miss-messages’. Such instances occur when the user inputs query in a regional or idiomatic language.

#2 Response Time

It is the average time taken for the chatbot to respond to customer queries, based on the total number of messages sent by a chatbot during an interaction. Typically this can average around 5-6 seconds. However, research indicates that users will leave a site if key elements take more than 3 seconds to load.

#3 Intent Prediction

It is the ability of the bot to anticipate what a customer wants in real-time. To achieve this, the bot must be paired with multiple sources of data and AI capable — in order to combine user behaviour, transactions, and profile details. Using this, the bot can determine intent based on both aggregated interactions for known and unknown users, and personalized data pulled from back-end systems.

#4 Retention Rate

It defines the number of users who willingly return to using the chatbot to address their issues. The retention rate varies according to industries. However, the clear formula for increasing user retention is to equip chatbots with the ability to understand user queries and empathically respond to them. This metric is directly correlated with the ability to personalize sales and/or customer service greetings, in 1:1 messaging.

#5 Goal Completion and Fall-back Rate

The number of times a chatbot can resolve the query, manage ticket, generates leads, or results in conversion determines its goal completion rate. However, like humans, bots, at times, might not be handle queries on their own. Such instances account for the fall-back rate of the bots.

Here’s an insightful read on why businesses should consider chatbots.

Successful Chatbot Adoption Across Businesses

Providing 24×7 support is not impossible for any organization. But, the labour cost associated is high, which makes chatbots a viable solution for instant customer support. IBM reports that globally businesses spend over $1.3 trillion/year to handle roughly 265 billion customer calls.

The following are examples of chatbots adoption for cost savings.

#Messenger Marketing Bot

ManyChat provides bot platform on Facebook Messenger for marketing, e-commerce, and support. DigitalMarketer incorporated ManyChat’s bot for messenger marketing and have reported very high returns on their ad spend (nearly 500% ROI).

#Insurance Chatbot

Religare has incorporated chatbot on its website and WhatsApp to handle customer queries. It has resulted in 10 times more customer interaction and 5 times more sales conversion.

Here are more insurance chatbot use cases.

#B2C Chatbot Offering Personalization

1-800-Flowers is using IBM Watson’s Gwyn smart virtual shopping assistant. It interacts with customers to understand their gift preferences and accordingly help them select a personalized gift for their loved ones. More than 70% of 1-800-Flowers customers are happily ordering through Gwyn bot.

Here’s a sample Chatbot ROI calculation from a financial perspective.

The Future of Chatbots

CNBC reports, currently businesses are saving $20 million per year globally through chatbot adoption. By 2022, chatbots can cut operational costs by more than $8 billion per year. Also, researchers predict that by 2025, bots will accomplish about 90% of the B2C interactions. Looking at the reduction in cost and ease of operation, investing in chatbots is worth it.

We specialize in building NLP and AI-powered chatbots for enterprises. Drop us a line at hello@mantralabsglobal.com to know more.

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses.

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Key Areas Where Chatbots Are Revolutionizing Healthcare

Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.

Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.

Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.

Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Image Credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

NLP in Healthcare: Opportunities and Challenges
HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

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