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Conversational AI Doesn’t Have to Be a Risky Investment: Step 3

By Mandy Reed, Global Head of Marketing

Conversational AI is a technology that is regularly described as ‘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.

For many companies, 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. They don’t have the financial flexibility or company culture to take a high level of risk, whether that risk is real or inferred.

The good news is that conversational AI projects don’t have to be risky. In this blog series, I’m sharing three steps for achieving conversational AI success while minimising the risk. You shouldn’t let the common misconception that conversational AI has to be a high-risk investment keep you from implementing it to improve your customer experience and employee engagement.

The previous posts in this series covered the first two steps to minimising your risk:

Once you’ve read through those steps, you’ll be ready for number three:

Step 3: Start with a pilot and expand with a staged approach.

Before you go all in with a conversational AI project, look to do a pilot or proof-of-concept (POC) with the vendor. This gives your organisation the opportunity to test out the technology on a limited basis to make sure it is a good fit for you and your digital strategy. The financial risk associated with this pilot should be shared by the vendor.

Typical pilots run for 30-60 days which will provide sufficient time for you to see results, evaluate initial performance, and make decisions about taking the next step in your conversational AI plan. A successful pilot strengthens your business case and enables you to finetune your strategy based on real feedback and user interactions. Also be sure to use the pilot phase as an opportunity to test integration points to ensure your solution will work end-to-end as you expand the deployment.

Starting with a pilot, and sharing that financial risk with the vendor, makes moving forward with a larger conversational AI investment less of a gamble for your company. When you do convert from the pilot to a full system, you still don’t need to jump directly into a massive project. Taking a staged approach to development and rollout is not only less risky, but also often the best way to achieve success.

Typically, the best method for deploying a chatbot or virtual agent is to use an agile approach, starting small and scaling the solution over time. This could mean focusing on a particular area of content, a specific use case, or a key contact channel that will have the greatest impact as a starting point. Your vendor will collaborate with you to design a staged rollout based on your biggest pain points. This reduces risk because you are streamlining your efforts in a way that supports your identified KPIs. You can also take advantage of new insights as you go to improve the tool and tweak your plan to maximise on successes and avoid potential problems.

It’s a common misconception that conversational AI is always a high-risk investment for organisations, but one that shouldn’t keep you from implementing your own chatbot or virtual agent. Being a risk-adverse business is not a barrier to deploying a successful and valuable conversational AI project. These three steps can help you join other savvy companies in taking advantage of the proven, reliable benefits of this technology while minimising your risk.

To make it easier for you and your organisation to apply these three steps to your conversational AI approach, I’ve compiled them all into a single document which can be read, shared, and downloaded here: Conversational AI Doesn’t Have to be a Risky Investment.

Conversational AI Doesn’t Have to Be a Risky Investment: Step 2

By Mandy Reed, Global Head of Marketing

Innovative, cutting-edge, ground-breaking – these are all words used regularly to describe conversational AI technologies. Being the organisation that deploys an innovative technology typically requires being comfortable with a high level of risk. However, most companies don’t have the financial flexibility or company culture to take that degree of risk, whether real or inferred.

Deploying conversational AI solutions like chatbots and virtual agents can be risky but doesn’t have to be. You don’t need to be an early adopter of innovations to benefit from the technology. These solutions have been used by businesses for over two decades as part of their customer engagement and employee experience strategies, and you can take advantage of those learnings to deploy reliable, successful projects.

In this three-part blog series, I’m sharing three steps for achieving conversational AI success while minimising the risk for your organisation. Last time, we delved into Step 1: Be selective when deciding on a vendor and technology. If you missed that post, I recommend you read it first before moving on to the second step:

Step 2: Build a business case with realistic goals.

Embarking on any business project without identifying the goal is always a risk, so it is essential that you have a realistic business case and clear objectives for your conversational AI project. An experienced vendor will be able to assist you with this process by performing a textual analysis of your existing data, such as live chat or contact centre transcripts, to identify what queries can and should be automated with conversational AI.

Starting with this analysis immediately reduces risk because your business case is being built around your own data. It’s combining the vendor’s expertise directly with the information that is unique to your customers, employees, and company. Instead of guessing your users’ self-service needs or taking a generic approach, your business case is customised to you and your pain points from the start.

Follow that initial analysis with a consultation workshop to review the results and collaborate with the vendor to identify your key performance indicators (KPIs) and set realistic goals. These business objectives will directly inform how your chatbot or virtual agent is built and implemented. Having clear goals and deciding how you will track progress and measure outcomes minimises the danger of investing in a project that won’t really meet your needs.

The key in this step is to build your conversational AI business case around realistic and obtainable goals. Being practical about what you are automating and setting sensible targets for your solution creates a solid foundation for your project. It keeps your investment focused on reliable, reproducible outcomes and business benefits.

In the third and final instalment of this series, we will talk about starting your conversational AI project with a pilot and the best approach to minimise risk while rolling out a full deployment. A great resource for better understanding the financial investment needed for a successful virtual agent or chatbot is the Guide to Enterprise Conversational AI Pricing: Calculating the Cost of a Successful Chatbot or Virtual Agent. Even if your company isn’t at the enterprise-level, this guide provides valuable insights into budgeting and calculating ROI that’s useful for all organisations.

It’s Time to Pull Back the Curtain on Enterprise Conversational AI Pricing

By Chris Ezekiel, Founder & CEO

Enterprise software pricing is often shrouded in mystery and the subject of intense negotiations between the supplier and customer. For applications where the market is immature then this is necessary as it takes some time to equate the cost with the business value. Whilst chatbots and virtual agents have been around for a long time, it’s relatively recently that they’ve become ubiquitous within the enterprise. I’m very pleased to report that Creative Virtual is stepping forward to lead the way in removing this shroud of mystery around conversational AI pricing.

Long-term relationships based on trust and transparency are attributes that underpin the culture within Creative Virtual, and we’re proud to launch the ‘Guide to Enterprise Conversational AI Pricing: Calculating the Cost of a Successful Chatbot or Virtual Agent’ whitepaper

This comes at a particularly important time as the conversational AI market is oversaturated with solutions that do not deliver the level of sophistication, flexibility, and customisation needed for a well-performing enterprise solution. These tools come with a lower price tag but end up negatively impacting the organisation’s bottom line by harming the customer experience and eroding customer loyalty.

This expert guide pulls back the curtain on enterprise-level pricing to empower organisations with the knowledge they need to properly budget and evaluate costs of conversational AI solutions.

We have drawn on our many enterprise-level customers and partners, together with a world leading amount of experience within the industry, so that organisations can have confidence not only in the pricing but also the advice on the effort and expertise required to maintain a successful solution. And whilst the technology platform is clearly a key part, the experience and expertise are often undervalued. That was the motivation for our previous whitepaper, ‘Guide to Selecting a Virtual Agent or Chatbot Vendor: Forget the Technology & Focus on Experience’.

conversational ai pricingNow, your organisation has two important complementary whitepapers that draw on Creative Virtual’s nearly two decades of delivering successful solutions in many sectors to help you develop a conversational AI roadmap designed to give your company a customer experience competitive advantage.

Download our new guide to enterprise conversational AI pricing for insider tips on budgeting for your solution, typical pricing models, and average costs for pilots and full systems.

When you’re ready to learn more and start building your own business case for a conversational AI solution, our expert team will be here to arrange a personalised demo and discuss your consultation workshop.

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.

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.