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Successful Conversational AI: Blending Machine Learning & Human Intelligence, Part 3

By Chris Ezekiel, Founder & CEO

In February ISG, a leading global technology research and advisory firm, published their ‘ISG Provider Lens™ Intelligent Automation – Solutions & Services’ report. Mrinal Rai, Principal Analyst at ISG, and his team evaluated 19 conversational AI vendors in the report, identifying Creative Virtual as a Leader in the highly competitive quadrant.

I joined Mrinal and Jan Erik Aase, Partner and Global Head – ISG Provider Lens, recently on a Zoom session for a discussion about conversational AI. Mrinal kicked things off by diving into the conversational AI quadrant and his research. He outlined the key factors that led to Creative Virtual emerging as a clear Leader in his evaluation.

Jan Erik and I then discussed a number of conversational AI questions that ISG see coming up with their advisors as well as their clients. In my previous posts, I’ve shared the first two parts of that conversation during which we covered current trends and developments, the changing roles of contact centre agents, barriers to achieving success, and the impact of the pandemic. You can watch Part 1 here and watch Part 2 here.

In the third, and final part of our conversation, we delved into the following question: When setting project goals, what KPIs should organisations identify and what results should they expect?

This is a really important question for organisations as they build their business case for a conversational AI solution. About five years ago, there was a big focus on contact deflection when implementing these tools. While this is still important, we find it to be less important as the focus has shifted more to improving the customer experience (CX). Organisations recognise CX as an important differentiator today in competitive marketplaces.

This same sort of thinking also applies to internal solutions, whether that be a solution deployed within the contact centre or one designed for employee support in areas such as service desk, IT support, HR support, and employee onboarding. Supporting employees is more important than ever and, with the right tools in place, presents an opportunity to improve productivity, efficiency, and job satisfaction.

Check out Part 3 of our ‘Successful Conversational AI: Blending Machine Learning & Human Intelligence’ discussion for more on KPIs and results:

 

 

A big thank you to Mrinal and Jan Erik for taking the time for this discussion, as well as to Katie Dickens and Thomas Victor at ISG for working behind the scenes on arranging and producing the recordings of our session!

If you haven’t done so yet, be sure to download a copy of the ISG Provider Lens™ – Conversational AI Quadrant Report.

If you’d like to learn more about Creative Virtual’s expert consultation and see our conversational AI technology in action, sign up here for a personalised demo session with a member of our global team.

Conversational AI Data and Powerful Customer Insights

By Mandy Reed, Global Head of Marketing

These days it can be easy to feel like we’re in a constant state of information overload. The 24-hour news cycle combined with social media and the ability to Google anything always at our fingertips makes it difficult to escape the relentless barrage of information. This can also make it difficult to discern what information is genuinely important and worthy of our time.

The same is true when it comes to your organisation’s customer experience (CX). There is information floating around everywhere on customer behaviours, preferences, and sentiments. However, it can be a struggle to gather and identify the customer insights that are meaningful and most important for your CX strategy.

In fact, Gartner analyst Augie Ray identified ‘looking in the wrong places for customer insight’ as one of the key mistakes that kill CX programmes in their infancy. He points out that you can’t expect customer-centric results from a strategy that is not based on real customer insight and understanding. Likewise, you won’t be able to measure the impact of your programme if you aren’t gathering and analysing customer feedback, sentiment, and experiences.

Conversational AI is growing in popularity among businesses and their customers because of the positive self-service experience high-quality virtual agents and chatbots are delivering. However, organisations should never overlook the added benefits they gain by analysing the conversational data these tools can collect.

In a chatbot vendor selection guide, conversational AI expert Ridhi Mathur explains:

“In this era of data, a sophisticated virtual agent can play a very significant role for many businesses. The conversational data collected is very rich. It can be analysed and mined to understand your customers’ views, identify developing trends and make informed predictions, which can in turn contribute to areas such as product or service innovations and marketing.”

As you are developing your CX programme and selecting tools, be sure to keep this in mind. You want to implement a conversational AI solution that is not only built based on a deep understanding of your customers but will enable you to gather valuable customer data as well. You also want to ensure that you are properly analysing that data to assist with ongoing improvements to your CX and inform other areas of your business, such as product development and marketing programmes.

The customer insights that can be provided by chatbots and virtual agents are too beneficial to overlook when you are selecting a technology to implement. Consider this as you are reviewing conversational AI pricing options. Purchasing a chatbot without the right tools for data collection and reporting functionality never pays off, no matter how much money you might think you are saving. It is also key to work with a provider that has the expertise to assist you with mining and analysing the conversational data to maximise your benefit.

If you need some help cutting through the information overload when it comes to selecting a conversational AI vendor, check out the ISG Provider Lens™ Intelligent Automation – Solutions & Services. This report provides an independent, market-driven evaluation of the industry and comparison of 19 conversational AI providers.

Customer Service Week Musings: How does a machine know if it’s wrong?

By Laura Ludmany, Knowledgebase Engineer

There are many comparisons dealing with the main differences between humans and machines. One of the recurring points is while humans have consciousness and morals, machines can only know what they are programmed to, hence they are not able to distinguish right from wrong unless they are provided data to make decisions based on that information. There have been many discussions on the self-awareness of robots, which is a topic as old as Artificial Intelligence, starting from Isaac Asimov’s three laws of robotics, continuing to the Turing test and nowadays AI ethics organisations.

One thing is commonly agreed – bots need to be ‘taught’ morals, and to achieve this there could be two approaches, both having their advantages and disadvantages. The first one contains a loose set of rules, but plenty of space for flexibility; this system could always reply to questions. However, it could also result in many false positives cases and could go wrong on many levels. The other would mean more rules and a narrower approach. The system could answer a limited number of queries, however, with very few or non-false judgements.

What does this mean from the customer service and customer experience (CX) view and for virtual agents answering real time customer queries? If we narrow down our conditions, bots would deliver the right answers at most times. However, they could not recognise many simple questions, making users frustrated. The same can happen with the loose set of conditions: the assistant would easily deliver answers but could misinterpret inputs, resulting again in annoyance.

To solve this problem, we must use a hybrid approach: an AI tool can only be trained appropriately with real-life user inputs. While we can add our well-established set of rules based on previous data and set a vague network of conditions, the bot will learn day-by-day by discovering new ways of referring to the same products or queries through user interactions. Half of a virtual assistant’s strength is its database, containing these sets of rules. The other half lies within its analytics, which is an often-overlooked feature. What else could be better training for a CX tool than the customers leaving feedback at the very time an answer was delivered? Conversation surveys are not only important to measure the performance of the tool. They are also crucial for our virtual assistants to be able to learn what is wrong and what is right.

Our approach at Creative Virtual to reporting is to follow the trends of ever-changing user behaviour. We offer traditional surveys, which measure if a specific answer was classified as helpful or not by the user and if it saved a call. Sometimes, the specific required action or transaction cannot be performed through self-service options and the customer must make a call, or else, the answer has been slightly overlooked and needs to be updated – for these cases there is a designated comment section, so users can express themselves freely.

We all know from personal experience, that we can’t always be bothered to fill out long or detailed surveys – we are on the go and just want to find the information we were looking for without spending extra time to leave feedback. This is typical user behaviour, and for this we came up with different options for our clients such as star ratings and thumbs up and down, keeping the free text box, to make the rating simpler for users. The solutions deployed are always dependent on the requirements and preferences of our clients, which are in line with the nature of their business and their website design. For example, financial organisations usually go with the traditional options for their customer-facing self-service tools, but internal deployments often have more creative user feedback options.

What if, during a conversation, a virtual assistant delivered the correct answer to five questions, but two answers advised the user to call the customer contact centre and one answer was slightly outdated? Does this rate as an unsuccessful conversation, due to three unhelpful answers? To solve this dilemma, we have End of Conversation Surveys, which ask customers to rate the whole conversation, on a scale to 1-10 and choose what they would have had done without the virtual assistant. As always, there is a free text box for further communication from the customer to the organisation. These surveys show high satisfaction levels as they measure the overall success of the conversation, which can have some flaws (just as in human-to-human interactions), but still can be rated pleasant and helpful.

Let’s take a step further – how can the virtual assistant learn if it was right or wrong if none of these surveys have been taken up by the user? Is this valuable data lost? Our Creative (Virtual) analytics team have levelled up their game and came up with a solution! During voice interactions, such as incoming calls to customer contact centres, there is a straightforward way to understand if the conversation wasn’t successful, even if it wasn’t stated explicitly, as the tone might change or the same questions might be repeated. But how can we rate a written communication with our customer? There has been a specific platform developed, which sits on the top of our previously described survey layers. This platform classifies the whole conversation, with a carefully weighed several-factor-system, which can be tailored to our client’s needs, containing factors such as if there has been more than one transaction, whether the last customer input was recognised by the virtual assistant, if there have been negative user responses recorded, etc.. The primary ‘hard’ indicators remain the user-filled surveys, so this is just a nice icing on the cake, as our mature deployments show over 80% of successful conversation rates.

With our proactive approach and multi-layer analytics tool sets, we can be sure that our virtual assistants will learn more and more about what is right and wrong, to increase the customer satisfaction level continuously. However, I think no machine will ever be able to answer all questions correctly, as this would mean that deployments have stopped being fed up-to-date real-life data. Our world is changing rapidly as are our user queries. These cannot be fully predicted ahead, just analysed and reacted to appropriately. As long as AI tools serve customer queries, they will always face unknown questions, hence they will never stop learning and rewriting their existing set of rules.

As we celebrate Customer Service Week this year, we need to recognise the role customers play in helping to teach our AI-powered chatbots and virtual assistants right from wrong and the experts that know how to gather, analyse and incorporate that data to help train those tools. Check out our special buyer’s guide that explains why experience matters for using this hybrid approach to create reliable and always learning bots.

Debunking the Myth that Digital Customer Solutions Seem Labor Intensive and Costly to Maintain

By the Creative Virtual Team

Any company considering a new system that will bring about broad changes to their operation has to think about cost. A valuable business platform has a labor and capital cost involved to install and maintain the system: V-Person™ is no different. However, our solution has a few important characteristics that help you keep maintenance costs lower than you might think.

Data Tracking and Analytics

The key to getting better at a high-volume activity like customer communication is tracking and analyzing data. Our V-Portal™ platform features a powerful reporting functionality that can track metrics like:

  • Call deflection
  • First contact resolution
  • Conversion rates
  • Customer satisfaction levels

You can also use V-Portal’s reporting system to determine which questions or concerns are most common. This saves time and money for your support and sales departments because you can update your knowledge databases in accordance with issues that your customers and prospects are actually concerned about.

Additionally, since our virtual assistant is able to analyze your current customer requirements based on customer service chat or phone transcripts, much of the work required to set up a chatbot solution can be done without any labor. We can usually configure your virtual assistant to answer about 80% of your customers’ questions just by reviewing your old customer service transcripts.

Semantic FAQs

One of V-Person’s most powerful features is its ability to make intuitive presumptions and get to an answer on its own, without any direct oversight or input from a team member. These presumptions are based on loose connections made between relevant topics.

For example: if a power company was using V-Person and a customer went to their site and asked the virtual assistant about thunderstorms in their area, the VA would connect thunderstorms with service outages and suggest solutions for a service disruption.

Even though V-Person might not have any information about thunderstorms specifically, the semantic FAQs feature allows it to make an intuitive connection between storms and outages to help the customer. With semantic FAQs, you’ll cut down on customer service calls and minimize the amount of human input necessary to resolve customer issues.

Costs Compared to Support Agents

Though there is a cost to implementing and maintaining the V-Person solution, consider the cost of hiring agents to handle customer concerns. Research shows that the cost-per-contact of a virtual assistant is about 25% of the cost of a live agent chat, and about 10% of the cost of an agent phone call.

In some cases, a virtual assistant solution can even help a company deal with a large-scale staff shortage. Verizon has used virtual assistant technology to improve its customer service and eliminate millions of calls each month, despite a huge reduction in its workforce.

Consider the Lifetime Cost vs. Benefits

There will always be installation and ongoing maintenance costs when using any new business system, especially in an enterprise setting. To determine whether the labor and financial investment is worth it, you must consider not just the cost of the solution, but the cost and labor required to achieve the same results without the solution.

Your company doesn’t need to have access to highly advanced technology or already have artificial intelligence solutions in place. An AI virtual assistant is a solution that can grow with your business – as your organization grows and changes, your virtual assistant will continue to improve its ability to provide excellent customer service and contribute to your business goals.

As the leader in virtual customer service, Creative Virtual specializes in the science of conversation. Sign up for a free demonstration here.