Customer Service Archives - BotCore

Customer Service and Beyond: Harnessing Chatbots to transform the customer experience

Abhishek Shanbhag — Fri, 17 Sep 2021 06:57:00 +0000

Customer Service and Beyond: Harnessing Chatbots to transform the customer experience

We are all aware of how organizations are using chatbots to provide exceptional customer service. Statistics say, “By 2023, more than 60% of all customer service engagements will be delivered via digital and web-service channels, up from 23% in 2019. Impressive, right?

However, if you think that customer service is the only viable use case of chatbots, you may want to re-consider. With the advent and rise of technologies, such as machine learning, artificial intelligence, robotic process automation (RPA), augmented reality (AR), etc., chatbots can be leveraged to lead customers through different stages of the sales funnel.

Research by Tech Republic has found “Teams that use chatbots to automate conversations are 27% more likely to meet rising customer expectations than those that don’t.”

Read the blog to know how chatbots can transform the overall customer experience, beyond just customer service.

Customer Service and Beyond: Harnessing chatbots to transform customer experience

Chatbots can be used to take customers through the entire sales funnel or lifecycle. The customer lifecycle defines the various stages a consumer goes through before, during, and after sales.

They are available 24/7 to help customers with queries, purchases, and product support, anytime and anywhere.

Here’s how chatbots can help create personalized, meaningful, and immersive customer engagement and transform customer experience in each of these phases –

1. Reach

Reach is the first step of the customer lifecycle. Your marketing efforts should be concentrated in places where your customers are to create awareness about your products.

In today’s world, customers demand an omnichannel experience. To generate customer interest around the company’s products and services, organizations can deploy chatbots on channels, such as websites, social media apps, and common messaging platforms like WhatsApp and Facebook Messenger.

Such chatbots can be used to push alerts and notifications about new products, answer customer queries around them, collect data about customer preferences through chats and email, and send personalized tips and advice.

2. Customer acquisition

Reaching potential customers isn’t enough. It is crucial to engage them with the right messaging and personalized product recommendations.

Bots can study customer profiles to make informed and targeted suggestions about what to purchase. Moreover, they can provide suggestions on how to use the product for more meaningful engagement, lead customers to the company’s online store, and assist with payment and checkout.

Examples:

Quaker’s Oats Facebook Messenger bot, Otis, guides customers with online shopping, sets reminders for overnight oats, provides consultation for the queries they raise, and offers delicious recipes.

Users may choose from some of the seasonal recipes available, or type in a food emoji for their preferred item.

This helps the bot guage customer preferences and personalize recommendations for recipes, products, and ingredients.

With Otis, Quaker’s customer engagement has increased 13% year-on-year without any other marketing support.

In 2019, skincare brand POND’S launched its Facebook Messenger bot called SAL that leverages augmented reality (AR) to provide immersive customer experiences. When users upload a selfie, the bot delivers personalized skincare recommendations across four significant areas of concern – pimples, wrinkles, uneven skin tone, and spots.

While the analysis is generated, which usually takes about a minute, the bot shares additional skincare tips to retain the user’s interest.

Upon the completion of the diagnosis, users are informed about their skin condition and offered personalized product recommendations by POND’S.

98% users stated a positive engagement with SAL chatbot.

3. Customer retention

To retain customers, organizations must continue to send relevant and meaningful recommendations and messaging to them.

As a customer interacts more and more with the chatbot, data and feedback collected by the bot can help marketers predict customer needs, analyze, measure, and refine engagement strategies, and develop creative marketing campaigns.

Predictive analytics saves costs as organizations spend less time, money, and effort on developing products that the customers won’t prefer.

Using the chatbot, organizations can then proactively inform customers about latest offers, upcoming sales, complementary products, and upsell/cross-sell products to increase revenue.

In fact, Gartner predicts that by 2025, proactive customer engagement interactions will outnumber reactive customer engagement interactions.

Moreover, quick and accurate customer service using chatbots is one of the most basic ways to increase customer retention.

Examples:

Marriott Rewards members can book travel at Marriott International hotels worldwide on Messenger, handle hospitality-related arrangements, such as business meetings, plan their vacation with the digital magazine Marriott Traveler, and chat with the customer support executives in case of queries.

Additionally, the organization leverages the chatbot to upsell to customers their premium suites and services.

4. Customer loyalty and advocacy

Once retained, customers wouldn’t have an issue recommending the products and services of the organization.

Harnessing chatbots to transform customer experience is one of the surest ways to build customer loyalty, get them to spread positive word-of-mouth about your brand, and build your customer base.

Other benefits of customer-facing chatbots

Cost Savings – Customer profiling and analysis using chatbots saves costs as organizations spend less time, money, and effort on developing products that the customers won’t prefer. Moreover, chatbots can handle multiple customer queries at once, 24X7, and at scale, allowing customer service agents to focus on more complex customer needs.
Going local – With multilingual chatbots, organizations can interact with customers in their native tongue. Engaging with customers in their preferred language accelerates localization efforts, allows organizations to understand regional nuances and cultural subtleties, and makes customers feel valued.

How can Acuvate help?

At Acuvate, we help clients deploy AI-enabled chatbots with our enterprise bot-building platform called BotCore.

With minimalistic coding requirements and a graphical design interface, enterprises can build and deploy chatbots customer-facing chatbots within a few weeks. Our bots leverage the best of Microsoft’s AI, ML, and NLP technologies to understand what the customer wants, retain context, learn from past conversations, and provide seamless customer engagement.

Moreover, enterprises can cater to a global customer audience with support for multiple languages like French, German, Italian, English, etc.

To know more about BotCore, please feel free to schedule a personalized consultation with our experts.

The post Customer Service and Beyond: Harnessing Chatbots to transform the customer experience appeared first on BotCore.

Using Conversational Analytics to Measure and Personalize Customer Experience

Abhishek Shanbhag — Fri, 10 Sep 2021 12:17:00 +0000

Using Conversational Analytics to Measure and Personalize Customer Experience

Over the past year, customer behavior has evolved as customers have become more digitally inclined and reliant on online brands to meet their needs. With many brands offering exceptional customer service, customer expectations have risen, and addressing changing customer demands and behaviors has become a top priority for businesses.

A recent CCW Digital research has found that 65% of companies now place more importance on the customer experience (CX) than they did prior to the COVID-19 pandemic. Organizations must now enable engagement on their customers’ preferred channels and form an in-depth understanding of their preferences, intentions, and end-to-end interaction with the brand.

The adoption of conversational AI and self-service channels like AI-chatbots, voice bots, and virtual assistants has grown exponentially as customers look for faster and more flexible ways of finding solutions and receiving support. Conversational AI chatbots leverage machine learning and natural language processing to hold human-like conversations with customers. As per Gartner, the conversational AI market size is expected to grow from AUD 6 billion in 2019 to AUD 22.6 billion by 2024, at a CAGR of 30.2%, during 2019-2024.

Modern customers seek proactive and personalized brand engagements while upholding their desire for instant interactions. Traditionally, companies have relied on data, such as click-through rates, browsing session length, page views, etc., to get insights into customer behavior.

With the rapid growth in the use of chatbots, customers are signaling their preferences, perspectives, expectations, and intentions through their interactions with brands. Hence, many organizations are increasingly adopting conversational analytics to measure and personalize the customer experience in line with this trend.

Let’s explore further.

How Conversational Analytics helps in Measuring and Personalizing Customer Experience

With the onset of the digital era, customer expectations have transformed significantly, and nothing short of exceptional customer service shall suffice. Consequently, customers are levitating towards brands that can respond effectively to their omnichannel needs, tailor experiences, and predict what they might ask or inquire, even as customer needs become more dynamic and hard to fathom.

As customers interact with brands through chatbots and other self-service channels, real-time conversational analytics has emerged as the new paradigm in the CX world.

What is real-time conversational analytics? It is the ability to capture, analyze, and evaluate customer conversations that take place with the brand while they happen.

Word, phrase, and tonality data, coupled with sentiment analysis, generate real-time insights into customer preferences and perspectives so that organizations can better personalize customer experiences. Conversational analytics leverages AI and machine learning to generate actionable customer intelligence that measures and improves customer satisfaction, prevents customer churn, and enhances revenue.

Benefits of leveraging conversational analytics to measure and personalize customer experience

1. Using sentiment analysis to gauge customer emotion and improve their experience

Customers are emotional beings. The ability of an organization to understand customer sentiment and respond empathetically helps improve the overall customer experience.

As chatbot usage has grown in the CX industry, the need to be sensitive to customer sentiment has become more pronounced. In the process of augmenting traditional customer interactions with AI, brands don’t want to let go of the human touch.

Powered by natural language processing and machine learning technologies, sentiment analysis enables chatbots to comprehend customer mood from words and utterances that indicate a particular sentiment.

Consequently, sentiment analysis renders bots emotional intelligence by helping them measure the polarity and intensity of customer emotions and respond suitably.

For example, a customer utterance (such as delighted, thrilled, satisfied, etc.) indicates happiness and is an opportunity for the bot to upsell. On the other hand, words like annoyed, dissatisfied, unhappy, etc., show anger/sadness and may require the bot to escalate the chat to a human agent.

2. Measure a plethora of conversational data points

Conversational analytics helps organizations understand trends in user utterances and how different customers pose the same query, which can help make needed investments and upgrades in AI and automation.

Metrics, such as customer satisfaction with the product or service (measured through survey responses, customer ratings, etc.) and the percentage of chatbot interactions that escalate to a human agent, help organizations improve CX on various fronts.

3. Evolve product offerings

Conversational analytics provides valuable customer data that can be leveraged to innovate and design better products, personalize marketing campaigns, and improve customer service.

Traditional web analytics show customer reactions to what is presented to them; they do not provide information to help companies build better products and services. However, insights delivered by conversational analytics show what product features work for customers, which don’t, and how products and services can better align to their expectations.

4. Hyper-personalization of the customer journey

While customers ask similar, repetitive questions that chatbots can automatically handle, real-time analytics has the power to adjust responses to a person’s unique question and disposition (tone, emotion, etc.), hyper-personalizing customer journeys to connect with them on an individual basis.

Moreover, customer data analysis reveals if a particular customer is likely to react positively to an upsell/cross-sell attempt and helps the bot recommend suitable products to meet that need.

5. Augment real-time agent experience through AI-powered recommendations

Real-time conversational analytics can turn a negative interaction into a positive one by providing intelligent, AI-driven recommendations to agents in real-time, advising them on the next best course of action to take while handling customer queries.

Real-time targeted alerts help agents fix mistakes committed at the moment and immediately improve performance, reducing the risk of further complaints.

Such smart suggestions help agents deliver exceptional interactions, personalize solutions, avoid churn, and build loyal customers.

Moreover, real-time sentiment analysis fathoms customer sentiment, intent, and tone and helps agents respond according to the customer’s disposition. For example, if a customer expresses joy on his latest purchase, the system may indicate the agent to use it as an opportunity to upsell and collect more data.

6. Improve Customer Effort Score

Customer effort score is a CX metric that indicates the amount of effort a customer has to put in to purchase a product or get an issue resolved, a request fulfilled, or a query answered.

Advanced real-time conversational analytics helps improve the customer effort score by learning from customer interactions to collect valuable customer insights, improve the knowledge base, predict customer behavior, and shorten in-call and post-call interactions.

How can Acuvate help?

At Acuvate, we help clients build AI-enabled chatbots with our enterprise bot-building platform called BotCore.

With minimalistic coding requirements and a visual design interface, our chatbots can be built and deployed across different channels within a few weeks, with channel-specific API. Additionally, BotCore’s chatbots support multiple languages (French, German, English, Spanish, Italian, etc.) to help you reach out to a global consumer base.

BotCore also offers a conversational analytics module to provide a deeper understanding of customer needs and intent, turn insights into action, and personalize the entire customer journey from end to end.

To know more, please feel free to schedule a personalized consultation with our experts.

The post Using Conversational Analytics to Measure and Personalize Customer Experience appeared first on BotCore.

Deploy an IVR Bot to Improve Customer Self Service

Abhishek Shanbhag — Mon, 28 Jun 2021 12:11:00 +0000

Deploy an IVR Bot to Improve Customer Self Service

Automated customer interaction is the backbone of any customer-facing communications strategy. Since the advent of the COVID-19 pandemic, there has been a sharp surge in calls to customer service, particularly in industries such as banking, hospitality, insurance, and health.

Moreover, research has shown that nearly 90% of people prefer voice interaction instead of navigating a complex phone menu. However, it can be challenging for organizations to deal with the high volume of incoming customer calls.

For one, contact centers have limited resources to handle the massive influx of calls. Secondly, voice-based channels are not only expensive but difficult to scale. Additionally, many customers communicate via speech but may prefer a self-service channel to convey their requests.

In such a scenario, interactive voice response (IVR) bots can provide an intelligent, engaging, and natural self-service way to support customers – anytime and anywhere.

Let’s explore.

IVR Bot to Improve Customer Self Service

IVR bots are computer programs that can converse like people and simultaneously service an unlimited number of requests, provide 24X7 support, answer customer queries instantly, and decrease business costs.

The bots use artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) technologies to process customer requests, enable them to self-transact, learn from past conversations, provide personalized solutions to their queries, or escalate calls to a human agent when needed.

Customers can get immediate and accurate responses in the language of their choice – it’s fast, scalable, cost-effective, and improves first-call resolution rates.

Therefore, deploying IVR bots infuses agility in customer service operations, allowing agents to focus on the most critical customer requests and tasks that add value to the organization.

Using Microsoft Azure AI to build an automated IVR chatbot

For Microsoft customers looking to build IVR bots, Microsoft Azure AI boasts all crucial elements that help build an automated IVR solution.

Backed by the Microsoft Azure cloud platform, the solution can manage speech requests via Teams, Skype, and the Microsoft Bot Framework with tools for data ingestion, data storage, data processing, and advanced analytics.

Core Azure Services and Microsoft technologies that help build the IVR bot

Microsoft’s Natural Language Processing technology

IVR bots leverage natural language processing (NLP) to understand the nuances of the human language, including grammar, synonyms, and slang.

Speech service (Bing Speech API or Cognitive Services Speech Service)

The Speech service transcribes raw speech data into text form.

Natural Language Understanding (NLU) technology

Language Understanding (LUIS) identifies customer intent and spoken entities from the transcribed text.

Example – If the bot says, “I want to place an order for a grey washing machine.”

Here, “place an order” is the intent, and the “grey washing machine” (product category) is the entity

Microsoft Bot Framework

Manages the call workflow and processes conversation results.

Other supporting technologies

Azure Web app, Azure SQL, Azure Cosmos DB, etc.

Learn More: Interactive voice response app with bot

Essential features of an IVR bot for improved customer self-service

Intent extraction and analysis
Ability to pause, listen, and respond accordingly whenever a customer barges in during an ongoing interaction
Ability to process information and act at the same pace at which the customer speaks
Optimal integration with the contact center database to know the caller history, identify the likely reason for the call, and tailor suggestions based on the customer’s persona.
Ability to hand over the call to a human agent or subject matter expert (SME) when required.
Machine learning capabilities to improve response accuracy and learn from past customer conversations

How can Acuvate help?

As a Microsoft Gold Partner, we at Acuvate helps clients build IVR bots and improve customer self-service with our intuitive, low-code bot-builder platform called BotCore.

The low-code, visual design interface allows the quick deployment of bots.
Seamless integration with legacy enterprise systems and AI services
Multilingual capabilities enable organizations to engage with customers in their native language
We leverage the best of Microsoft technologies, including AI, NLP, Azure Cognitive Services, and LUIS.

To know more about BotCore, please feel free to schedule a personalized consultation with our experts.

The post Deploy an IVR Bot to Improve Customer Self Service appeared first on BotCore.

Using Human Hand Off in Power Virtual Agents for Better Success

Abhishek Shanbhag — Tue, 25 May 2021 08:12:00 +0000

Using Human Hand Off in Power Virtual Agents for Better Success

Today, AI-enabled chatbots play a significant role in addressing both simple and complex queries of users. Statistics show that 3 in 5 millennials have used chatbots at least once in their lives. In fact, teams that use chatbots to automate conversations are 27% more likely to meet rising customer expectations than those that don’t.

Moreover, by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis.

While chatbots act as the first line of support, there will be situations when the conversation needs to be handed off to a human agent.

Microsoft customers using Power Virtual Agents (PVA) to build employee/customer-facing chatbots can explore the human agent hand-off functionality for better success.

But, what is PVA? Power Virtual Agents is a low-code app development SaaS platform that allows pro and citizen developers to build and deploy chatbots in a guided, no-code visual interface quickly.

Before delving deep into the nuances of handing over conversations to human agents using PVA, let’s understand why a human-in-the-loop is important?

Why is human-in-the-loop essential?

Chatbots help organizations automate customer and employee support – 24X7, at scale, and at significantly lower costs. They enhance efficiency and productivity and allow users to focus on tasks that add business value. Chatbots answer FAQs, send the required information, and provide smart suggestions based on the customer/employee profile.

Meaning, they augment human effort; however, in case of an escalation or upon the user’s request, the bot must be capable of seamlessly transferring the call to a live agent.

Let’s understand why a human-in-the-loop is critical.

1. User Sentiment

At times, the user is angry, frustrated, or annoyed. In such a scenario, the chatbot must be capable of detecting the mood or tonality of the user and handover the chat to an agent.

2. Critical conversations

Sometimes, the subject of the conversation may be sensitive/critical, the customer may be at a higher risk of churning, or it may be a high-value transaction. In such cases, it is prudent for the bot to hand over the chat to a human agent.

3. Highly-complex issues

Customer queries are diverse and evolving. At times, the chatbot may not be able to handle them and should recommend the “Chat with an agent” option.

4. User request

Users may be in a rush or prefer to talk to a live agent instead of a bot.

5. Training and Feedback

Training, calibrating and explaining AI-enabled systems requires human-in-the-loop architecture
Gartner

A chatbot needs a human-in-the-loop feedback system to constantly learn and become intelligent. Both users and agents can influence and improve its intelligence. In the case of consumer-facing chatbot, even the smallest customer’s feedback like “click here if you are satisfied with the solution” can help improve the machine learning algorithm of chatbots. In addition to consumer training, contact center agents can also classify outliers and exceptions and help modify the chatbot training data and behavior accordingly.

Initiating a handover to a human agent with Power Virtual Agents

Microsoft’s Power Virtual Agents (PVA) allows you to seamlessly hand over critical conversations to live agents.

Power Virtual Agents is a low-code app development SaaS platform that allows pro and citizen developers alike to build and deploy chatbots in a guided, no-code visual interface quickly.

While PVA is a part of Microsoft’s Power Platform, it leverages the Microsoft Bot Framework and Azure Bot Service and Cognitive Services to build chatbots and configure human agent handoff.

Pre-requisites:

Engagement hub that interacts programmatically using APIs or SDK
Product license for Omnichannel for Customer Service and a license for Power Virtual Agents
A chatbot built with Power Virtual Agents.

Types of agent handoff triggers:

Internal Triggers – The bot is unable to determine the user’s intent as there is no topic or matching option within a subject. Or the user requests an agent handover immediately or in the middle of a conversation. On detecting such an escalation, the bot redirects the user to the Escalate system topic.
Explicit Triggers – When bot developers create bot content, they may determine that certain topics may require human intervention. Consequently, they may add the “Transfer to agent” node to such topics.

Learn More: Adding transfer to an agent node

Handoff integration models:

The Microsoft Bot Framework supports two models for integration with agent engagement platforms.

Bot as an agent – This model helps onboard existing bots to the agent hub with minimal effort. In this mode, the bot connects to the agent hub and responds to user requests as if the requests came from any other Bot Framework channel. When the conversation is escalated to a human agent, the bot disengages from the interaction.
Bot as a proxy – In this method, the user talks directly to the bot. When the bot decides that it needs the assistance of a human agent, the message router component redirects the conversation to the appropriate agent but stays in the loop to collect transcripts, filter messages, or provide additional content to the agent and the user.

Contextual variables transferred upon hand-off:

When a bot hands over a conversation to a human agent, it shares the full context as well as user-defined variables to help agents resume the conversation smoothly. Microsoft has defined the contextual variables available upon handoff, which are as follows –

Context	Purpose	Example
va_Scope	Helps route escalations to a live agent.	“bot”
va_LastTopic	Helps route escalations to a live agent and helps ramp-up a live agent. Includes the last topic that was triggered by an utterance from the user.	“Return items”
va_Topics	Helps ramp-up a live agent.	[ “Greetings”, “Store Hours”, “Return Item” ]
va_LastPhrases	Helps route escalation to a live agent and helps ramp-up a live agent.	“Can I return my item”
va_Phrases	Helps ramp-up a live agent.	[“Hi”, “When does store open”, “Can I return my item” ]
va_ConversationId	Helps uniquely identify a bot conversation.	GUID
va_AgentMessage	Helps ramp-up a live agent.	“Got a gift from: HandoffTest”
va_BotId	Helps identify the bot that is handing off a conversation.	GUID
va_Language	Helps route escalation to a live agent.	“en-us”
All user-defined topic variables	Helps ramp-up a live agent.	@StoreLocation = “Bellevue”

Additionally, a transcript of the conversation that took place between the customer and the bot is transferred to the live agent.

Learn More: Configure hand off to any generic engagement hub

Learn More: Configure seamless and contextual hand-off to omnichannel for customer service

How can Acuvate help?

As a Microsoft Gold Partner, Acuvate helps clients deploy AI-enabled chatbots with Power Virtual Agents for customer and employee-specific use cases, such as HR support, IT service desk, sales and marketing, etc.

Organizations can then define scenario-specific workflows for seamless transfer to human agents when needed.

To know more about our services, please feel free to schedule a personalized consultation with our Power Virtual Agents experts.

The post Using Human Hand Off in Power Virtual Agents for Better Success appeared first on BotCore.