Machine Learning is a sub-field of AI, a method through which intelligence is obtained. Machines here rely on large amounts of data to “learn” for themselves.
Know My Company
Tell us about your interaction with smart technologies such as AI and Cloud-based Customer Experience platforms.
Founded in 2009, Comm100 is a global provider of omnichannel cloud customer experience solutions, also available on-premise for those more regulated industries. We originally launched with live chat, email marketing, and a forum platform, then expanded in 2011 to include ticketing and a knowledge base. We launched our first AI-powered bot in mid-2018 and recently launched our third-generation bot.
How did you start in this space? What galvanized you to join Comm100?
I have been working in technology marketing since 2005, and in the marketing technology segment specifically since 2012. For me, joining Comm100 was easy — the company has what amounts to an awareness challenge, not a product challenge. Our platform is incredibly mature, but our state of mind doesn’t reflect that yet as we were operating more or less in stealth mode until 2017. I saw an opportunity to help get the company to the next level with the Sales and Marketing teams we’re assembling.
How do you differentiate between technologies for AI and Machine Learning?
AI refers to machines that can perform tasks more characteristic of human intelligence, involving planning, understanding language, recognizing objects or sounds, learning and problem-solving.
Machine Learning is a sub-field of AI, a method through which intelligence is obtained. Machines here rely on large amounts of data to “learn” for themselves. Other ways AI can learn include direct programming, if you’re thinking only about the execution of tasks and not the evaluation of them beforehand. Practical, real examples of Machine Learning include facial recognition relying on millions of photos, translation and speech recognition.
Here’s an example: You’ve likely heard of chess and Go matches between machines and humans. IBM’s Deep Blue was rule-based, meaning it relied on programming to beat Gary Kasparov. In contrast, Google’s DeepMind trained itself on Go moves to beat Lee Sedol by ingesting a large data set of moves for the game. The former was programmed, the latter self-taught via Machine Learning.
So, if you’re looking at technology that can “evolve” by acquiring new data and making sense of it, then you’re looking at Machine Learning.
Who are you competing with in this landscape?
How do you project the offering from Comm100 in the overtly crowded cloud-based digital conversation landscape?
I’m not sure I agree that the space is crowded. Live chat and digital customer conversations are growing, but they’re not fully mainstream yet — not every consumer or B2B site has live chat or bots. Adoption is definitely increasing, but there is still a lot of greenfields out there, as evidenced by the number of vendors. I would say that until we start seeing some consolidation on the vendor side, we can’t truly say the industry is crowded.
Now if you mean “crowded” as a reference to the sheer number of vendors, then it is noisy, but there are lots of variations in what these vendors bring to market. There are considerable differences in the scope of offering that may not be immediately apparent to the buyer, but once they dig in, these differences become notable.
There are a number of things Comm100 does differently from others in the space. We provide a broad omnichannel offering and focus on delivering deeper, smarter configuration options to our customers so they can really wrap our solution around the way they want to work and engage with their customers. For example, the range of options for chat window configuration, our highly granular routing and queue management rules, and our software deployment options help us carve out a unique space in the market.
When it comes to bots, we live by one simple rule: Make sure you keep the visitor’s interests above those of the brand. The tech you use should make their experience better and more satisfying. It’s lovely if it makes your team more productive and more cost-effective, but if it has a negative impact on customer satisfaction, you’ve missed the whole point. We help our customers design bots that fit specific use cases and offer strong monitoring and escalation points to human agents if and when the case demands it.
Tell us more about your chatbot and how your customers benefit from leveraging it.
We recently released our third-generation bot featuring extended capabilities for handling more complex customer requests and better collaboration between bots and human agents. Many of today’s bots are failing to live up to customer expectations and, according to Forrester, 2019 is the year that customers will lead a community-based revolt against poorly-executed corporate chatbots.
To us, this meant a huge opportunity to create an AI-enabled bot that was as easy as possible for our customers to deploy and execute successfully. With our solution, customers are able to deliver a seamless digital interaction to their customers that solve problems and delivers answers. And behind it all is the assurance that our bot will not go beyond its comfort zone, making sure pre-identified customers or those with tougher questions get routed to human agents for a consistently positive experience.
How do you see the raging trend of including involving AI and Machine learning in a modern CIO/ CMO’s stack budget?
Instead of a trend, I’d like to call this a revolution. There’s no doubt that AI will transform MarTech stacks and how MarTech is used, we’re just in the process of determining exactly how much of a transformation it will be. And for large enterprise, it’s already happening in meaningful ways, from fraud detection to facial recognition to energy-generation optimization.
The biggest impact of AI and Machine Learning on the CMO is that these technologies make it possible to truly leverage customer and marketing data effectively. The average tech company has anywhere from 16-20 different MarTech solutions in their stack, creating a huge amount of data that is virtually impossible for marketing teams and data scientists to sift through and use effectively. By leveraging AI and Machine Learning, marketing departments can tie together the datasets from disparate solutions and glean deeper insights faster than any human employee could.
With access to information like the company’s most profitable customer journey, its ideal customer profile and the nuances between different types of customers, marketing teams can go beyond traditional post-mortem metrics such as clicks and attribution to real-time insights that point to why a campaign was or was not successful. This allows teams to become more agile, adjusting throughout the campaign rather than only providing insight for the next time a similar campaign is run. This can also be applied to internal marketing devices, like a blog, to help identify keywords and topics that are particularly successful.
AI and Machine Learning also help to validate and justify the existence of such a large tech stack, highlighting how each is contributing and supporting one another. Conversely, it can also pinpoint parts of the stack that may not be worth the investment, enabling CMOs to streamline and optimize their MarTech investments.
What are the biggest challenges and opportunities for businesses in leveraging technology to optimize their Customer Support and Customer Success?
As with any technology, the main challenge for organizations is the natural human resistance to change, which can’t be underestimated. Organizations cannot discount the importance of internal change management when implementing new solutions. But it doesn’t stop there; a customer cannot use your newly-launched chatbot if they’ve never been told about it. New technology rollouts must be supported with proper customer communications — and that doesn’t just mean an email blast or note on the website. Things, like including a note on invoices or incorporating a line or two about the new solution into call center scripts, can be hugely impactful in a successful adoption.
In terms of opportunities, CX technology like live chat enables companies to connect with customers more naturally and in a more timely manner. Though many companies initially think they are going to deflect customers away from the phone when implementing a live chat solution, the reality is that live chat is more likely to connect to a whole new audience who would never even pick up the phone. This creates an opportunity for companies to gain a better understanding of more of their audience, allowing them to learn what they think and feel about the brand, as well as to build loyalty and adherence.
How should young technology professionals train themselves to work better with automation and AI-based tools?
Understanding that AI is not out to replace them will be a key. There has been a lot of fear-mongering around the rise of AI, but I think we’re starting to get to a place where people — and young people, especially — are beginning to understand the ways that AI can be used to enhance their jobs, taking care of time-intensive minutia and freeing up humans to manage more strategic, gratifying work. In the call center specifically, 60 percent of respondents to a survey conducted by CCW noted that they view AI as something to complement human agents, rather than replace them.
So, my advice is for people to master the how’s and why’s of AI as it affects their given space. The truth is, most companies are struggling to find people who “get” AI, so that means job security for those who do.
What is the biggest challenge to digital transformation in 2019? How does Comm100 contribute to a successful digital transformation?
The biggest challenge to digital transformation in 2019 is supply-side adoption. We’re still very much in the early adopter phase and the barriers to moving beyond that are myriad. There are the natural ones, like an inability for many to understand AI and its applications and benefits. Because we’re still in the early stage of adoption, there aren’t enough effective proof points in every vertical to get people beyond that. There are also more organizational reasons, like lack of budget.
But with 51 percent of companies already using AI and more than a quarter planning to implement it in some form in the next two years, coupled with the ample evidence that customers are excited about and willing to use AI to interact with brands, companies must overcome their hesitancy and implement AI now, or risk falling behind as the technology advances.
At Comm100, we contribute to our customers’ successful digital transformation by providing a state-of-the-art platform that not only provides a top-notch digital customer experience but also delivers invaluable data that allows our customers to optimize their customer service.
How potent is the Human-Machine Intelligence for businesses and society? Who owns Machine Learning results?
I was at a trade show last year where the VP of Customer Experience from a large retailer said something relevant to this realm. He paraphrased consumers when he said, “Look, we all know you’re collecting mountains of data on us. All we ask is that you use it to OUR advantage, not just yours”. That’s the basis of loyalty and value in the digital age.
The potency of Human-Machine Intelligence is immense, and the impact will be felt as much on businesses and society as any other technological advancement of our time, maybe more. In fact, the fear occasionally accompanies AI is evidence of its potency, even if it is overblown.
In terms of owning results, it all depends on where the learning is acquired and on the data that’s being analyzed. For example, if a national health organization is using AI to understand patterns related to the spread of diseases, that’s public domain. On the other hand, if an auto parts chain is using AI to understand how to stock inventory on their shelves and when to raise or lower prices, that’s proprietary and owned by the business.
I think the line in the sand here is when there is personally identifiable information (PII). I believe that the individual should always own the results in these cases, and be required to consent to it being used.
Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?
Beyond 2020, aside from even more widespread adoption, we’ll start seeing more of the micro-level impact of AI and Machine Learning. Today, most of the use cases in the market are more macro-level—success stories are around predicting weather, traffic and the like. In the future, applications will be more niche and personalized—think to connect your phone and your calendar and to allow AI to determine whether or not a call needs your attention or should be sent straight to voicemail while you’re in a meeting. Is it your wife, who normally doesn’t call you at this time and may have something important going on, or is it a telemarketer calling you from a known outbound number?
The Good, Bad and Ugly about AI that you have heard or predict –
I think we’ve covered a few examples of the good. In terms of the ugly, that comes down to when companies use AI to directly affect people against their best interests. One example I’ve been thinking of: Imagine an ethically questionable company that’s trying to grow their business. They could use AI to specifically target people for whom the company’s product could be risky, but which would be profitable for the company. There’s also the legal aspect, where issues can arise if AI is being used to gather data on someone without their consent.
The bad is a little less scary and could be as simple as AI going just a little too far, but with the best of intentions. For example, say an AI solution learns that you’re a big Norah Jones fan and she is playing a concert in your town.
Because data shows how much of a fan you are, the solution may decide to purchase you two tickets. You’d probably find this a bit creepy and may be somewhat annoyed, but it doesn’t have any lasting impact on your life like the earlier “ugly” example would. Further, while for today’s adults there’s a definite creepiness to something like this, as AI becomes more commonplace, future generations won’t think twice about it.
What is your opinion on “Weaponization of AI and Automation”? How do you promote your ideas?
Similar to what’s stated above, to me the weaponization of AI and Automation has to do with AI being used in malicious ways — to trick people into situations that could be potentially harmful or when it’s used surreptitiously.
The Crystal Gaze
What Cloud Customer experience and SaaS start-ups and labs are you keenly following?
I’m following a number of companies in these spaces, including:
What technologies within AI/NLP and Cloud Analytics are you interested in?
Two AI/NLP technologies that I’m particularly interested in are sentiment analysis and real-time text analytics. I’m looking forward to seeing how these vital tools will be adopted in 2019 and beyond, because I believe they hold many clues to truly understanding what it is that customers want from the brands they choose. These two technologies can help brands learn this in very non-invasive, passive ways, and that’s important because the second you get a little too overt with your customers in asking them how they feel or what they struggle with, the chasm between you and them begins to widen. If you can learn these things indirectly—and have that supported by verifiable data—then you will achieve the same results or better with a lot less risk.
As a tech leader, what industries you think would be fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?
There are a number of industries poised to quickly adopt AI and Machine Learning, but I think what it boils down to is whether or not an organization has a wealth of legacy systems and legacy thinking that can slow adoption. Additionally, any company whose revenue is at risk without AI and Machine Learning — such as leading-edge tech, biotech and pharma companies — will be quick adopters.
Consumer products companies fit into this category, too, as do those in the insurance, banking and finance spaces. Not all of the adoption will be visible. For those in the insurance, banking and finance industries, especially, it will be more internally-focused, with companies using these technologies to gather and analyze data in order to improve internal processes that impact customer experience.
What’s your smartest work-related shortcut or productivity hack?
Productivity hack: turn off all app notifications (email, Slack, messaging, etc.); multi-tasking is a myth (except for ventriloquists and moms) — task-swapping is what you can do. Commit small chunks of time to tasks in succession, e.g. no more than 5 minutes for your inbox at a time, then at least 30 minutes for creative writing or editing, then at least 45 minutes to work on a budget spreadsheet or something more complex.
Switching often keeps you stimulated. Individual results will vary, but you have to know how your productivity ebbs and flows throughout the day. Most humans are at peak productivity in the morning, then experience a slump/fatigue in the afternoon, then a bounce of energy in the evening but without the ability to focus as well as in the morning. So later in the day may be best for creative efforts where deadlines don’t matter as much. Just don’t try building a financial forecasting spreadsheet right after lunch.
Tag the one person in the industry whose answers to these questions you would love to read.
Shep Hyken is one of the premier thought leaders and influencers in the customer experience space. Author, speaker, educator, Shep is one of the voices in the industry that everyone should know and follow.
Thank you, Jeff! That was fun and hope to see you back on AiThority soon.
VP Product Marketing and Communications at Comm100, Jeff leads a team tasked with accelerating customer acquisition and engagement through compelling messaging, positioning, and content.
Established in 2009, Comm100 is a global leader in live chat and multi-channel customer engagement. They help companies including HP, Porsche, Whirlpool, Bupa, and more across a wide range of verticals connect with their customers at every stage of the relationship — from curious prospect to vocal advocate.