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Conversational AI Doesn’t Have to Be a Risky Investment: Step 1

By Mandy Reed, Global Head of Marketing

In the technology industry there tends to be a focus on being innovative, cutting-edge, and ground-breaking. Industry awards, conferences, and articles frequently showcase and reward vendors for technological innovations. Analysts and expert speakers regularly highlight case studies of companies that are early adopters, deploying technologies in inventive ways, or finding success by taking a chance on something new and unproven.

Innovation is essential to the advancement of technology but doesn’t automatically equal practical business benefits. Having companies try out new technological developments and deploy existing solutions in creative and unfamiliar ways is important for finding practical applications for new innovations. However, being the organisation that deploys an innovative technology typically requires being comfortable with a high level of risk.

Most companies don’t have the financial flexibility or company culture to take that degree of risk, whether real or inferred. For them, proven and reliable results are more important than being innovative and flashy. Projects that get budget approval and management backing are ones that are considered safe bets because they utilise established technologies that have documented business benefits.

Conversational AI is one technology that is regularly described with words like ‘innovative’ and ‘cutting-edge’. Simply having ‘AI’ in the name makes some people think of it as being futuristic or only for companies with the resources to implement it for the cool factor. It can be easy for business leaders to associate conversational AI with being a high-risk investment.

Deploying conversational AI solutions like chatbots and virtual agents can be risky but doesn’t have to be. Your organisation doesn’t need to be an early adopter of new innovations to benefit from this technology. Chatbot and virtual agent technology has been used by businesses for over two decades as part of their customer experience and employee engagement strategies, and you can take advantage of those learnings to leverage conversational AI within your organisation.

Over the course of this three-part blog series, I’ll outline three steps for minimising risk and maximising benefits of conversational AI projects. Let’s get started with the first and most important step:

Step 1: Be selective when deciding on a vendor and technology.

The conversational AI market is oversaturated with new, inexperienced start-ups and technologies that haven’t been well-tested in the real world. The first step to reducing your risk is to choose a vendor that is established in the industry and provides a technology that has proven results. Both criteria are important when it comes to risk level.

Vendor experience is critically important because the more knowledge your selected provider brings to your project, the more confident you can be in their advice and guidance. You want a vendor that will become an extension of your own team and knows what they are doing because they’ve done it all before. Working with experts means you benefit from their many years of experience, thereby making your investment less risky even if your company is new to this type of technology.

When evaluating a vendor’s experience, ask specifically about how many years the company has provided conversational AI technologies, as these solutions may be an offering added recently even though the company has been in business for decades. Also ask about the experience of their individual team members and staff turnover rates. If they have high staff turnover and are constantly training replacements for departing employees, then you will likely miss out on the risk-reducing personal expertise you want the vendor to bring to the collaboration.

Just as critical as the vendor experience is having proof of their technology delivering positive results in real world applications. Don’t assume that just because a provider isn’t a brand-new start-up that they have a well-performing conversational AI technology. If the company has been in business for four or five years and only has one customer, you should question why more companies aren’t using their technology and if working with them is a risky option.

To reduce risk, ask about how the vendor has deployed their technology within your industry and what documented business benefits those solutions are providing. Saying they have the ability to deploy important features and functionality is great, but you want to see the technology in action in live installs. Also ask them about the length of their customer relationships as long-term engagements indicate that existing customers are happy with the technology, their results, and the collaboration. The vendor should be able to provide you with customer references so you can get first-hand feedback on their conversational AI projects.

Keep in mind that even if your company is minimising risk by selecting a proven solution with reliable results, you still want to partner with a vendor that is consistently innovating. You may not be the organisation trying out those new innovations first, but you don’t want to invest in a solution that’s not going to improve as those advancements become well-tested and are shown to deliver business benefits.

In my next post, we’ll explore building a realistic business case as part of Step 2 for reducing risk. In the meantime, check out the Guide to Selecting a Virtual Agent or Chatbot Vendor: Forget the Technology & Focus on Experience. It explains in more detail the most important questions to ask a vendor about their experience during your procurement process.

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.

Combining Chatbots and Voice for Omnichannel Experiences

By Liam Ryan, Sales Director

Last week Creative Virtual joined our partner Spitch as co-sponsors of The European Chatbot & Conversational AI Summit. The event was 100% virtual this year and featured two afternoons of various presentations, workshops, and panel debates. While being virtual is never quite the same as talking with someone face-to-face, it was great to see so many thought-provoking exchanges on the Discussion board and interesting questions asked by attendees during the sessions.

I teamed up with Gary Williams from Spitch to present on Day 1 of the Summit. Our session, The Omnichannel Solution: Chatbots + Voice, explored combining natural language chatbots with speech recognition capabilities to create powerful voicebot solutions. We shared some industry research from ContactBabel that showed 85% of CX professionals identified creating omnichannel/connected journeys as very or somewhat important to their strategies in the next two years (download the full report here). It’s no secret that customers want and expect an omnichannel support experience.

The tight integration of chatbots and voice creates a seamless journey as users switch between channels to help you deliver that connected experience. Gary and I shared two example voicebot use cases, one for customer support and one for employee support, that showed how the user could start a process on one channel and complete it on another in a smooth, seamless way.

For those interested in getting started with their own voicebot project, we ended our session with three important tips:

  1. Work with experienced vendors – Today’s market is crowded with new start-ups and inexperienced providers. You want to work with vendors that already have proven experience with both deploying and maintaining these solutions in your industry or sector. By partnering with experts, you immediately benefit from their experience. They can help you avoid common pitfalls, guide you on best practices, and ensure compliance with industry requirements and regulations.
  2. Select reliable technologies – This is why Creative Virtual and Spitch have partnered on voicebot solutions. We both bring years of expertise and documented results for each of our respective technologies. Be cautious about vendors that have attempted to tack on their own poorly developed chatbot or voice technologies to their main solution just so they can shout ‘Me too!’ You want technologies that are secure, can scale to current and future requirements, offer the hosting options you need, and will give you reliable results.
  3. Understand the integration options – When it comes to integrations, you need to first make sure there is a deep, seamless integration of the chatbot and speech technology to have a successful voicebot. Then consider what other integrations you are going to need to create a personalised user experience. You want a solution that can easily integrate with any existing content sources, backend systems, CRMs, other communication channels, etc. so that you can create a custom experience and connected journey.

If you’re interested in learning more about voicebots, schedule a demo to see these solutions in action and discuss possible use cases. You can also read more about the Creative Virtual and Spitch collaboration in our integration overview.

Thanks to Gary and Spitch for their event partnership and to The European Chatbot & Conversational AI Summit organisers and attendees for two days of great virtual content!

How Much Does it Cost to Make a Chatbot that Actually Works?

By Paulo Barrett, Chief Operating Officer

Ask any seller of a highly complex and customizable chatbot or virtual agent system about cost and you’re likely to get an evasive answer. ‘There’s no one-size fits all.’ ‘I’d need to talk to you on the phone to give you an accurate quote.’ Increasingly, in this ever-saturating market, it’s easy to find elements of chatbot pricing (i.e., API request fees) or flat monthly subscription costs for low-end systems, but who is giving the educated bot buyer a clear, top to bottom view of what it costs to build a system that will really work?

By ‘really work,’ I mean one that will materially contribute to cost savings, improve customer satisfaction, and maybe even generate new revenue. In other words, how much is it realistically going to cost to build a bot your customers will actually want to use.

The truth is, building a successful chatbot is not purely a question of technology. Whether you are buying a platform to BYOB, getting something cheap and cheerful off the shelf, or looking for a bot consultancy to support your internal efforts, your work is really just beginning once you have the system configured and deployed. The ongoing work to improve the chatbot’s performance and to get the best out of self-service in your unique deployment is what makes the difference.

It can be difficult to predict exactly what actions your customers will want to take in the beginning. That means being able to take an informational system and swiftly evolve it as desired customer outcomes become clear is key for success. This is enhanced by using great technology, of course, but ultimately, you need the right experts (internal or external) to separate your deployment from the crowd of others (often failures) which are flooding the support world.

Think of it like buying an instrument. No matter how expensive or special it is, either you learn to play it, or you get someone else to play it for you (alternatively, it ends up gathering dust and doing absolutely nothing for anyone). One way or another, this expert training costs time and money. You have to weigh this investment against the return.

Now to the million-dollar question … pun intended. What will a chatbot that your customers actually want to use cost for a large enterprise? While it’s true that most deployments are unique to every client and require some customization, there are some standard pricing building blocks you can expect to see.

The first cost to nail down is the pilot fee. How much am I gonna spend to test this thing out and see if it works for me? The financial risk associated with a pilot should be shared by the customer and the vendor and typically runs around $50,000 (USD). While the client assumes some risk via the initial cost, you should expect this to be credited against the cost of the full production-level deployment if you choose to move forward. This fee will cover all hosting, software deployment, content development, technical consultancy, and transactional fees for the agreed period. Typical pilots run 30-60 days from go live to give you enough time to see material results and make a decision about the ongoing plan.

Once you convert from pilot to full system (we pride ourselves on a +90% conversion rate!), you have some choices to make about how you pay for the tools and ongoing consultancy. Some customers wish to be purely pay-for-performance. Often, they go with a tiered model based on volume with session costs starting at a dollar (i.e., a single interaction with a user, with unlimited question/integration calls in that session). This per session cost may fall based on meeting certain volume thresholds. With any variable pricing model there are pros and risks for both the customer and the vendor.

If you prefer not to have a variable rate in your forecasting, you can purchase a more traditional software/services package. A standard production system should include integration with a live chat platform as well as your CRM. This will ensure your customers get personalized answers to their questions and are able to complete transactions with the bot online, rather than just receiving flat, informational content. The cost to provide the software and ongoing consultancy, along with an adequate knowledgebase of 100-200 solutions, will generally cost somewhere between $150,000 and $250,000 (USD) per annum, depending on the number of sessions.

While this may seem like quite the investment, you have to ask yourself: What is the cost of deploying a support tool that my customers don’t want to use and delivers a negative, frustrating experience?

Ready to learn more? Our team is always on hand to arrange a personalized demo with you and answer any questions you may have about getting started with your pilot.

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.

Harnessing Human and Machine During the Pandemic

By Rachel F Freeman, Operations Director

A direct quote of the explanatory theme for this Customer Service Week says: “The impact of events affecting the world today have changed the way in which companies and their employees engage with customers.”

Indeed this is true, and all of us in our businesses and in our personal lives can feel the effects of how services of all kinds have altered in ways ranging from barely discernible to completely different (filling out forms, having temperatures taken, tape and measurements of distance being assessed amidst a lot of Perspex dividers). A main thing that became apparent in call centre scenarios was that call wait times for an array of customer services were much longer and users still are advised in recorded messages that “due to Covid-19” call wait time may be impacted.

I can testify that I’ve never had to wait 25-30 minutes to speak to a representative for whatever service I needed before Spring 2020 but experienced that exact scenario at least four times in the past few months. Speaker phones have never been so handy so that one can do other tasks whilst being on hold for extended periods of time!

Cue the virtual agents, chatbots and virtual assistants. Now more than ever before it is a no brainer that online self-help tools that are available 24/7 should come into their own in this period of uncertainty and continued delays blamed on Covid. Self-help tools need not be subject to the same rules of quarantine which makes them more reliable when it is impossible to predict when humans will be available to handle and field queries based on who is in the office and who is self-isolating.

Now more than ever, in the spirit of seamless customer experience, let’s let the self-help tools do what they are designed to do. Let’s let them share the burden of the increasing pressure on call centre agents and take advantage of them working to their fullest potential. Let’s give the machines space to help, freedom to work whilst the humans that are healthy can spend time not only speaking to customers who truly need a human but also to check in from time to time on the accuracy of the responses of the virtual agent. A smart combination of self-help and human guidance creates confidence that the job will get done with the right tools.

We’re all being told to stay safe and be alert – so let’s work in parallel with the tools to help make that happen. We can enable more efficient customer service interactions whilst at the same time prioritising the health and well-being of both customers and employees.

Check out the Neutrino release of V-Person™ to learn how Creative Virtual is delivering some of the most up-to-date and seamless self-help tools available. Also download the ‘Conversational AI Trends 2020’ ebook from AI Time Journal for virtual agent success stories during the pandemic.

The way companies and their employees are engaging with customers may have changed significantly this year, but with the right tools a positive, seamless experience is possible. This Customer Service Week let’s celebrate both the people and the technology that are delivering safe and seamless customer support in this period of uncertainty.

Satisfy Your Curiosity About Deploying AI Virtual Agents and Chatbots

By Mandy Reed, Global Head of Marketing

Do you have a chatbot or virtual agent on your roadmap? Do you need to upgrade a poor performing self-service tool? Are you curious about successful use cases for AI-enhanced virtual agents?

If so, you’ll want to reserve your spot now to join Creative Virtual and Engage Customer for their upcoming webinar, Tips for Deploying AI Chatbots & Virtual Agents.

On Thursday, 4 June 2020 Chris Ezekiel, Founder & CEO of Creative Virtual, will join Steve Hurst, Editorial Director at Engage Business Media, for this live webcast. Through a series of live demonstrations, they’ll explore:

  • Best practices for deploying and optimising conversational self-service
  • Questions to ask when selecting a chatbot or virtual agent solution
  • Tips for using AI and machine learning to improve performance
  • Guidelines for implementing seamless handover to live chat
  • Current live use cases and results from organisations around the world

Advancements in chatbot, virtual agent and conversational AI technologies have made them a go-to solution for providing easy-to-use and cost-effective customer support on digital channels. Customers are becoming more comfortable with using self-service options and appreciate being able to get help instantly at any time of the day or night. Companies benefit from reducing demand on contact centre agents and improving their customer experience.

The realities of the ongoing global pandemic have placed an increased pressure on organisations’ digital channels, including their existing virtual agent solutions. For many companies, the flexibility and robustness of their digital strategy is going to play a key role in how they maintain and build customer loyalty during this time and in the future. Offering reliable self-service is an important piece of that strategy.

Register now for the webinar Tips for Deploying AI Chatbots & Virtual Agents to learn more. Can’t attend the live event? Don’t worry, a recording will be sent to all registrants after the webinar.

You can also request a personalised demo to see how Creative Virtual’s technology can help you deliver seamless, consistent self-service and improve customer loyalty.

The Ever-Changing World of Customer Service Chatbot Creation

By Jeff Clifford, Project/Account Manager

I started building virtual agents and chatbots for customer service more than 12 years ago. A lot has certainly changed in that time. I talked about some of the changes in my Meet the Team interview a couple of years ago, but the industry has continued to evolve since then.

In my experience, there has been a major shift in customer expectations since 2015/2016 in the customer service chatbot industry. Pre-2014, most companies were looking to deploy chatbots that were pretty straight forward and consisted largely of FAQs, scripted conversation flows, keywords and a flat or standard UI. Some forward-thinking organisations were exceptions to that, and Creative Virtual worked with companies that took their solutions to the next level with innovative functionality like account integration to give personalised answers to logged in users.

Now, as we approach the end of 2019, chatbots and virtual agents are increasingly becoming the face of company help centres. Simple chatbot implementations are no longer enough to meet customer expectations. Customers also now expect self-service on new channels, such as Facebook Messenger and Amazon Alexa, that weren’t popular for customer service even a few years ago. The look of chatbots has also changed, with many companies now embedding their virtual agent into their own UI to give a cleaner, streamlined look and experience.

While previously personalised self-service was a major differentiator, now customers expect an experience tailored for them. Chatbots designed for enterprises have the features, functionality and integration options to deliver that. For example, chatbots are able to detect a customer’s language and country via integration with their user profile. This means the chatbot can display the user’s preferred language and can also return location-specific responses, such as a correct payment cut-off time that varies by country or time zone. Chatbots can also provide customised responses depending on where it was launched from, such as a section of your website about a particular product or service.

The goal of a seamless, omnichannel experience is becoming standard practice in organisations. Chatbots that can be deployed across channels are helping companies move away from a siloed approach to their customer service. When backed by the right orchestration platform, a single knowledgebase can be used across all channels while still delivering a specific answer based on the user’s device (such as a shorter answer on a mobile). A tight integration with live chat allows the virtual agent to pass a user over to a live agent seamlessly in the same panel while in the background passing over a full history of the conversation.

Perhaps one of the biggest changes in the chatbot industry is the use of more artificial intelligence (AI), and companies now want a chatbot that has AI capabilities. A good chatbot technology brings together different methods including semantic algorithms, deep learning, neural networks and machine learning in a way that still gives companies control. Chatbots are able to learn customer behaviours based on how users interact it with it and what suggested questions they are selecting in order to continuously improve. However, this shouldn’t be a black box. To be successful in a customer service role, the chatbot needs to have some level of human intervention and sign-off on content to keep information accurate and compliant.

Unfortunately, there are still a lot of simple chatbots out there today that leave customers annoyed and with a bad impression of the technology. The team at Creative Virtual are on a mission to help save as many of these chatbots as we can by transforming them into tools that meet customer expectations and are worthy of being the face of the company’s help centre.

The chatbot and virtual agent industry is an exciting space to be in, and I love being able to help my clients implement the newest developments in their virtual agent projects. At Creative Virtual, we’re always pushing the boundaries of what the industry thinks is possible and consistently striving to bring new innovations to the table. It gives me a real feeling of pride to see long-standing chatbot implementations evolve along with these changes and continue to deliver the service that customers expect.

“Virtual Moron-Idiot!”: Why Chatbots Fail and the #ChatbotRescue Mission Saving Them

This post originally appeared on AI Time Journal as part of their Conversational AI Initiative.

By Chris Ezekiel, Founder & CEO

It’s hard to find anyone involved with the chatbot and virtual agent industry who hasn’t heard the cautionary tale of Microsoft’s AI chatbot Tay. In less than 24 hours, Twitter users trained Tay to give offensive, racist and inappropriate responses which resulted in Microsoft taking Tay offline. Described as a ‘machine learning project designed for human engagement,’ Tay ended up becoming an often-cited example of an AI chatbot gone wrong.

As someone who has been working with virtual agent technology for nearly 20 years, Tay reinforced for me that pure AI is not the right answer for customer service and employee support virtual agents and chatbots. Yet, when Facebook announced the launch of chatbots on their Messenger platform and the media frenzy around AI and chatbots took hold, some conversational AI vendors jumped on the AI bandwagon. The industry suddenly became saturated with both false promises about the capabilities of the technology and a plethora of new start-ups claiming to have AI-powered customer service bots.

Fast forward a few years, and the chatbot and virtual agent landscape is now littered with poor-performing implementations and failed projects. In some cases, these failing projects have garnered negative press for companies. Telecommunications company Telstra was in the news when their virtual agent Codi was branded a ‘virtual moron-idiot’ by customers for failing to answer even basic questions. The National Disability Insurance Agency (NDIA), a government agency in Australia, was criticised for spending more than $3.5 million AUD on a chatbot project that never even reached the testing stage. In other cases, enterprises are struggling behind the scenes with internal chatbot projects. It’s not unusual to find companies with more than 10 projects in progress, but none of them delivering on their potential.

This is a common theme in organisations around the world. Yet, it’s not all doom and gloom for the industry. While there are many chatbot and virtual agent projects failing or never coming to fruition, there are also lots of highly successful implementations that have been in place for years. For example, at Creative Virtual our very first enterprise customer is still a customer today – that’s over 15 years of consistently delivering successful virtual agent solutions for them. So why do some chatbot projects fail while others achieve long-term success? There are two main pieces to the puzzle – the technology and the people.

As with any other product or technology, not all chatbot and virtual agent solutions are created equal. Here are just a few of the common problems enterprises are encountering because they don’t have the right virtual agent technology in place:

  • Channel-specific solutions – While providing 24/7 self-service on one channel can be a great way to get started with a chatbot, organisations are discovering that technology designed only for one channel is now creating a disjointed experience for customers because the tool can’t be linked up with any other channels. These companies are struggling with the challenge of having yet another siloed tool to maintain that makes it harder to deliver a seamless, omnichannel customer experience.
  • ‘Dumb’ solutions – Basic chatbot solutions are designed to do just that – have basic interactions. Organisations using these platforms are struggling to create unstructured conversation flows and deliver intelligent self-service that can help users solve issues using natural language. Without options to integrate with existing content sources, other support options and account information, simple chatbot solutions don’t allow for the easy, personalised experience users want. They also don’t have the right combination of machine learning and human input on the backend to help them continually improve in a reliable way.
  • Tough-to-grow solutions – Some enterprises thought their chatbot was on-track until they tried to grow their solution. Not all platforms give organisations the ability to scale their chatbot to other touchpoints, to support millions of users, to expand into other business areas, to link the contact centre to digital channels, to meet specific security and hosting requirements, to control the amount of machine learning and human input used – the list goes on and on. A self-service tool that can’t grow with the company won’t deliver long-term success.
  • DIY solutions – Lots of companies jumped at the chance to build their own chatbot only to discover that they don’t have the experience, know-how and data to create a tool that will meet their customer and/or employee engagement goals.

That last issue is just part of the reason why people are the other main ingredient for a successful chatbot implementation. As I mentioned in my Conversational AI interview, I truly believe that the key to a successful chatbot/virtual agent/conversational AI strategy is to work with an experienced team of people. There are lots of confusing options and challenges in the industry today, and enterprises need to be smart about the choices they make. Organisations need to work with an experienced partner that can help guide them in creating and implementing a chatbot strategy that will work today and also set them up for future innovation and expansion.

Often chatbot projects fail because the organisation isn’t working with a vendor that can provide consultation experience as well as the right technology. It’s important to work with a team that will collaborate closely to design a customised solution and provide guidance on both sector-specific and general industry best practices. This expertise needs to go beyond the initial implementation process to include experience in ongoing development and optimisation. New start-ups typically can’t provide that type of insight and support, and most organisations don’t have that expertise internally.

The good news for enterprises struggling with poor performing chatbots and projects that never got off the ground is that there are options for getting their projects back on track. Instead of abandoning these projects, they can save their investments by leveraging what they already have and building on that to create a successful chatbot by upgrading to the right platform. As someone who has been involved with this technology since its infancy, I’m passionate about helping these organisations save their investments. The expert team at Creative Virtual and I know intimately how well this technology can work for enterprises and don’t want them to continue to miss out on those benefits.

If your organisation is struggling with a chatbot or virtual agent project, I encourage you to reach out to learn more about Creative Virtual’s Chatbot Rescue Mission.

If your organisation hasn’t started out on your conversational AI journey yet but is worried about selecting, deploying and maintaining a successful solution, I recommend downloading these Top Tips for Implementing a Chatbot or Virtual Agent in 2019.

We’re Coming to Rescue Your Failing Chatbot Project!

By Chris Ezekiel, Founder & CEO

In my role leading a global company, I have the opportunity to travel all over the world speaking at conferences, meeting with enterprise executives and collaborating with other industry experts. Everywhere I go, I hear stories from organisations that have started on chatbot projects which is exciting for the industry. Yet all too often that excitement turns to disappointment in these stories as we are seeing many of these projects failing or never coming to fruition.

I’m not alone in seeing this common theme. I’ve had many discussions with industry analysts this year who have echoed this same concern, and we’ve encountered this issue first-hand with some of our most recent customers who came to us for help after struggling with other chatbot products. Failing chatbot projects have also garnered negative press coverage for companies. Telecommunications company Telstra was in the news when their virtual agent Codi, a joint project with IBM and LivePerson, was branded a ‘virtual moron-idiot’ by customers. Failing to answer even basic questions, it left users frustrated and sharing their negative experiences with the world. The National Disability Insurance Agency (NDIA), a government agency in Australia, was criticised for spending more than $3.5 million AUD on an IBM Watson chatbot project that never even reached the testing stage. It’s not uncommon today to find enterprises with more than 10 internal chatbot projects in progress – and none of them actually delivering on their potential.

As someone who has been involved with virtual agent and chatbot technology since its infancy, I felt passionately that it was time for my company to act – and the expert team at Creative Virtual agreed. We know intimately how well this technology can work for enterprises and don’t want them to continue to miss out on those benefits. That’s why we are now on a Rescue Mission!

The goal of this mission is to rescue organisations struggling with poor performing chatbots and projects that never got off the ground. Instead of abandoning their failing projects, we are helping companies save their investments by leveraging what they already have and building on that to create a successful chatbot. We’re offering a no cost consultation workshop and initial chatbot upgrade to our award-winning V-Person™ platform to get the transformation project started.

Creative Virtual is in a unique position to rescue these failing chatbot projects. As a company, we have over 15 years of experience in the virtual agent and chatbot space. Our very first enterprise customer is still a customer today – that’s 15 years of consistently delivering successful solutions for them! We’re able to do that because we have a highly experienced team that delivers best practice expertise alongside our innovative and award-winning technology.

Earlier this year Frost & Sullivan named Creative Virtual the AI-Enhanced Customer Self-Service Product Leader. In their independent review, they praised not only the capabilities and performance of our technology but also the effective way we provide the guidance of an experienced strategic partner to our clients. That combination of people and technology is what makes us perfectly suited to lead this Rescue Mission!

If you’re struggling with a poor-performing chatbot/virtual agent or are unsure about what to do with a failing chatbot project (or 10 failing projects!), we want to help. Let Creative Virtual save your investment by transforming your failing chatbot into a successful conversational AI solution. Sign up today for your no cost consultation workshop and initial chatbot upgrade.

We’re coming to rescue you!