Since becoming its CEO in 2007, Paul Sergre has led Genesys to consistent yearly double-digit growth. The company now powers more than 25 billion customer experiences a year around the world. Its customer experience platforms are used by more than 10,000 companies in more than 100 countries.
Segre has a long history in the customer service space. He joined Genesys in 2002 as chief technology officer and became its chief operating officer in 2004. Prior to Genesys, he held senior positions at DSC, Bell Labs, and AT&T in strategy, business development, and operations support systems development
Shortly after delivering his keynote address at Genesys' CX17 user conference in late May, Segre sat down with CRM magazine's associate editor, Oren Smilansky, to discuss the state of customer service and Genesys' role in helping companies overcome common industry hurdles.
Smilansky: What, in your view, are the some of the biggest problems facing businesses today?
Paul Segre: I'd say a couple of things. Let's start with what we call customer engagement. We're not necessarily offering customers the opportunity to converse or interact in the channels of their choice. We see a lot of organizations that are still offering voice only.
We also still see far too much siloed activity. This is pretty extreme, but it's true for most of our customers. Sales, service, and marketing are different organizations and often use different systems, but they don't communicate across those systems. Then also, the different channels are siloed. The people who use voice are not the same people who use chat, etc.
That's compounded in big companies that have many divisions. If you think about a big bank, they might have a retail bank, a mortgaging arm, and wealth management, and all of those can be different organizations using different systems. Having a cohesive or structured strategy for sharing information and processes and a systems strategy is a huge barrier, because without that you end up with these sets of siloed people who are suboptimal.
Related to that, and importantly, many businesses treat things as transactional, and they don't personalize the experience. We pushed out a lot of self-service that people can use, but when it doesn't work, people have to pick up the phone or chat, and companies don't take advantage of that.
With our technology, for example, if you're banking online, we can see if you're struggling, offer to chat, and get you to a person who has notes about you and knows what you're doing and struggling with so you can pick up where you left off. I's fast and efficient and gives them a good experience. And it takes out cost. We can also see if you're on the mobile app or on the web when you call out to a bank. We can see the screens you were in, we can see what you were doing, so we can be personalized but also fast and efficient. Those types of technologies aren't being used to their maximum extent.
The problem is moving from discrete interactions that are without context to journeys that are personalized.
I also don't think people use the right metrics or invest enough in their people. The easy stuff is being automated, so when you get to a person, it's hard. You need to give those agents better context. We do that by aggregating a bunch of data and delivering it to the agent, so we can say this is who the person is, here's some information about him, this is what he's been trying to do. You get to an agent who's skilled, but also quality management, coaching, and things like that.
That's why we've been investing in what we call employee engagement solutions. But the generic problem is people aren't investing enough to simplify the lives of their employees. Part of that is tools, part of that is training, and because of it, ther's high attrition.
Smilansky: The first issue you mentioned relates to channels. Is it necessary for companies to be on all channels? How should companies figure out which channels to be on, and how does your technology address the concerns surrounding this?
Segre: We serve customers from small to large, regulated and unregulated, in a lot of different countries, so there's no sort of one-size-fits-all answer to that. I think the answer comes from looking at two things. One is what their customers want, and the other is what the state of the industry is, what their competitors are doing or could do.
It's a strategic question. Do you want to be a leader or a fast follower? Do you just want to focus on the lowest possible cost?
Not all channels are required for all people. It starts with the appropriate strategy for that company.
The first point is where do people want to go, but the long-term trends are crystal clear. Consumers want omnichannel, and companies are moving to support omnichannel pretty quickly, with very rapid adoption of chat, email, and then followed by mobile and web integrations.
Smilansky: What about businesses that fail to react to the challenges you outlined? How do you see them being affected in the next couple of years?
Segre: We have lots of data that shows that companies that are customer-focused and provide a great customer experience grow faster, are more profitable, and have better returns on their stock. If you're not investing in these things, you're not going to be as competitive, not going to grow as fast, not going to be as profitable.
Smilansky: It's been six months since you closed the acquisition of Interactive Intelligence. What lessons have you been able to pick up from their customers thus far?
Segre: We segmented our products into PureCloud, PureConnect, and PureEngage. The segmentation was based on buyer personas. The Pure Engage piece—the former Genesys—offers massive scale, all the features and functionality and pretty complicated integrations, and folks are willing to invest IT in that. The PureConnect market segment wants much more of an all-in-one, out-of-box solution with simpler integrations, and they're more focused on wanting it up quickly, wanting it to be simple, and wanting it to work. It's not as feature-rich and doesn't have as many integrations. The PureCloud segment is willing to accept slightly less functionality today for just the state-of-the-art architecture with a rapid innovation lifecycle. It is committed to the cloud. It is eager to do cloud, to do multitenanted cloud, eager to do Amazon.
Smilansky: During your keynote you mentioned wanting to avoid making mistakes that competitors have made when acquiring other software vendors. Can you explain what you mean by that?
Segre: The first phase was not to break anything that's working. When we looked at our organization, when we looked at our product, we said, "Let's not change things that we think might break other things. Let's only change things if we're really confident that they're going to be successful."
The other thing was to keep developing on our roadmaps. If everything looks good, we're going to look for accelerating things. We're not going to look at taking things away. We did that for Engage, we did that for Pure Cloud, and Connect.
Now we're in the process of doing a couple of things. One is figuring out how to accelerate roadmaps by technology use and sharing across the platforms, and we're also looking at deploying best practices in marketing, sales, service, and support across the products.
Smilansky: With the release of G-NINE, you're addressing the Internet of Things, artificial intelligence, and other areas of technological innovation, including augmented reality and virtual reality. What excites you most about these emerging technologies? What opportunities do you think they will give companies?
Segre: Maybe we should start with what the problems are. The way people assess their performance—the data that many contact centers use—is flawed. They use old-style reporting, very voice-centric, and they use a very limited amount of the data that they get. The opportunity is there. There is a ton of data there, and there are incredible analytic tools for visualization and machine learning and AI that can use the data and correct common wisdom that's wrong.
We are investing in a family of offers that I discussed earlier today, some around visualization. We have a partnership with MicroStrategy around a visualization layer with Engage, and we're working on upgrading Connect.
We do have virtual reality and augmented reality. In our development process, we get something to a conceptual level and then we find early adopters. Our virtual reality has been in an early adopter phase; we're working with a leading telco on using those techniques to really visualize data in a 3-D, virtual reality world.
With augmented reality, we had an integration with Google Glass before they blew up, but its a similar concept. Once we understand how to commercialize what the value is, if the technology's mature enough, then we move it to general availability.
We're doing sort of a similar thing with AI and machine learning. With machine learning, we have many offers. We're using machine learning in generally available products around managing, providing speech-to-text conversion; around quality management for understanding if you as an employee are following the procedures, are efficient, or if you are deficient in certain areas. If you're deficient, why are you deficient? If you're a superstar and you are selling more than anyone else, what are you doing differently? We integrated machine learning into that, with sentiment analysis and social monitoring, and then we announced predictive routing, which is the ability to look at all the data we have plus other data that a company might feed into our system to use machine learning to both prioritize and route a call. Today, most companies route to a queue; we do much more sophisticated work; we actually match and prioritize, but it's rules-based, and this is machine learning-based. This gets you out of the rules business and allows the machine to take over.
Our AI is in that early-adopter phase. We are beginning to work with definitional customers on both the architecture as well as the capabilities in what we call blended AI. Our key thesis is that for problems of material complexity—real-world problems vs. a prototype that you might want to put on the website—those implementations still need a human the vast majority of the time. And even where AI could solve it, some of the older techniques are more efficient. Our view is that you want to blend the various techniques.
Another one of the things I mentioned was directed dialogue, which is the ability to do what an IVR does. It says, "Are you calling for sales, service, or support, press 1,2, or 3." We can create a visual that says push it to your iPhone, so you can select sales, service, or support. That's a better interface, if you will, than if you just have free-form AI trying to guess and sometimes going down the wrong paths.
Smilansky: Genesys launched Kate at the conference. I understand that Kate is a virtual assistant, but she is more than that. What is she capable of doing?
Segre: To give you an example of the types of things we can do, we have a bank client in Malaysia that offers chat. A person starts chatting, and he's chatting with [IBM's] Watson. Watson answers questions with whatever percent confidence they deem appropriate—maybe it's 90 percent, maybe its 95 percent. If the confidence goes below that, the chat is routed to a live person based on the information they got from the website and information they got from the chat with Watson. They get all that context so the customer doesn't have to start all over again and say what he's doing.
It's this notion of seamless handoff from a bot to a person, keeping all the context, leveraging a previous conversation, and closing the loop.
Companies can also use it as an internal knowledge base assistant. You can use it externally. We believe you should use the same systems for both because that's how you get good data and consistent answers.
Smilansky: Genesys has traditionally been a customer service technologies company, but during your keynote, you mentioned that a lot of Genesys' revenue is now coming from marketing and sales use cases. Can you elaborate on that?
Segre: We're working with a very large customer. I think they're deploying 7,000 seats. They have inside sales. And if you think about modern inside sales, they're not agents, they're not helpdesk people, they're sales people.
Within that ecosystem, you might also have a marketing automation system. We can integrate with marketing automation systems so that if a person calls in and chats with the company, you can do two things. You can give him a different treatment. If you know that they're in a marketing campaign for a credit card, you can get them to a credit card person who knows that they're in that campaign. You can also provide feedback to the marketing automation system to change their scores based on what happens in the contact center. Based on that, you can make offers through digital channels, an outbound call, or a paper mail campaign.
We also have access to CRM and billing systems, so we can see that these are the products they've already purchased. We have a system that scores propensity to buy, so we can get the next-best offer to this person based on what they are in their industry. Instead of it being a cold, blind call to the salesperson, they can have a very informed conversation. The trick there is prioritizing interactions as they come in, getting them to the right person, and giving that person the knowledge needed to effectively sell.
There are also outbound use cases. For example, we work with one of the largest pharmacy companies in the world, and they do prescription renewals and things like that. You might say it's just a prescription renewal, but when you think about the money, that's actually a sale. If they can get you to fulfill with them, vs. going to some other pharmacy or a mail order, that's huge, huge business for them. So those are examples of the marketing and sales use cases.