Making Data Simple: A new definition of client care
00:15 Connect with Al Martin on Twitter (@amartin_v) and LinkedIn (linkedin.com/in/al-martin-ku)
00:30 Connect with Tracy Bolot on LinkedIn(linkedin.com/in/tracy-bolot-992a6b4a)
01:30 Link to Global Elite?
04:00 Lean more about the Informix Software
13:40 Check out Turbotax
27:20 Click here to learn more about information sharing and governance at IBM
Hungry for more? Check out our other podcast episodes of Making Data Simple:
- Episode 1: Making Data Simple: The big data problem
- Episode 2: Making Data Simple: End of tech companies
- Episode 4: Making Data Simple: Will machines take our jobs?
- Episode 5: Making Data Simple: Growth Hacking - Not just for start ups
Making Data Simple: Episode 3 Transcript
Enter Cognitive: A New Definition of Client Care
Al Martin: Hello, everyone. Al Martin here and welcome to the Make Data Simple Series and thank you for listening. If this is your first time listening, you’ll also find more details in our show notes so have a look.
And you can reach me on Twitter at A, Martin, underscore, (Z). So I’m just going to jump in and get started. Today I’m with Tracy Bolot. Hi, Tracy. How are you?
Tracy Bolot: Hey, I’m doing great. How about you?
Al Martin: I’m doing - I’m always good. So Tracy leads digital client care in the global elite program in analytics. But Tracy, I’ll give you a few minutes. You can kind of describe your role, your interest in what you do day-to-day.
Tracy Bolot: Yes. Sure, so I currently work in the analytics group which is - as the products that include data hybrid management products as well as advanced analytics and unified governance.
And the reason why that’s important is because we’ve had an opportunity to take advantage of the capabilities to do (so many) things around support. So couple years ago, I started focusing on digital client care which is using the data that we have in a digital way to help improve our customer support experience for our products.
And in addition to that man, we started focusing also on doing some special things for our largest customers which is where we kicked off the global lead program.
So I focused on this trying to change how we - our customers receive support from IBM so that it’s a straight line that experience that is taking advantage of her analytics products to get information they need in the way that they need it quickly, efficiently.
Al Martin: Nicely done. So, Tracy, here’s what I wanted to do today, I just want to have a conversation. Unlike other podcasts, I get the most value, I think, out of water cooler talks.
So that’s what we’ll do here today. And I think the topic is, as you just stated, is what I would term cognitive client care only because I think digital - I may change next week. You know, I may change this at the end of the podcast, actually, but I think digital content to be a little overused.
And I think what we’re going to talk about and what you’re kind of doing is cognitive care, at least if I would term it. But for those listening, Tracy is the partner in crime. She and I have worked to transform IBM support.
I even don’t like the name support anymore. It’s more about client success since I think everybody holds responsibility and I can’t sell you how much I value client experience.
Let me start with a quick story and then I’ll, you know, see what your story is a little bit, Tracy. And that is, in a past life - so I graduated in electrical engineering and I went straight into software development for a healthcare company.
And at some point in time, I get a call from a friend of mine that had graduated in electrical engineering. And he said, “Hey, you need to come to this company. And, look, it’s a great company.” He went on and on about it.
And I said, “That sounds interesting. You know, what position?” He said, “Support,” and I said, “I’m not interested.” He said, “Well, hold on,” and we started talking and he said, “You should at least, and interview.”
And so I said, “All right. Fine. Good career path opportunity. I’ll go interview.” And when I went there, it’s like my eyes were open to a lot of different things and most importantly, is a marketable the folks in the room were and I wasn’t because, at the time, I was the developer stuck in a cube someplace.
These guys knew everything about the protocol, the database, the application and I thought, boy, I - you know, this is a good marketable position. So I did start my career after a short tenure in development, I started it in support and I’m very thankful for it because I joined a company called Informix Software.
And they got it because, at the time I use the college are driven development. You know, I was fixing code. I was adding serviceability features. And now the industry is, like, renamed that and called it dev ops which I find interesting.
So when a circle customer driven development, I think is dev ops today. And I think it’s a lot of what we’ve been doing in terms of oriented the whole organization into tribes and spots because I think everybody holds responsibility for the client experience.
I mean, that’s kind of my story in my history around client experience that I think has benefited me in terms of leaving development in a business with a client centric view. How did you get here?
Tracy Bolot: That’s an interesting story. So how I got here is, I have a degree in physics and after I got my bachelor’s degree in physics, I really didn’t want to go further towards education. I just wanted to get a job, start making money.
And so I was lucky enough to find a job opportunity in Austin working as a contractor for IBM and AIS work. So I came to the job my first day and I have to be honest, I didn’t know UNIX. I didn’t know AIS. I was a little lost.
And - but I like working with customers. I liked working with people. So the first month was really getting questions from customers walking around and talking to people and searching in our own internal database, because this was kind of before Google came about and had its big foray.
And it was - we had our own internal databases where we were to go find information. So I would look in the internal database. I would kind of walk around and ask questions from people who were the experts and I learned the product on the fly.
And then going through that, I remember thinking many times, wow, if only customers had access to the same information I do, they would need to contact me. Why would they need to talk to me and I’m learning it if only they had access the same information?
And so that’s really why I’m so interested now. I’ve left support, when into development, when into (test). I did (unintelligible) management. I do a lot of management roles.
But that’s why I’ve always been very passionate about client’s experience, is that I think there’s a lot that we can do and there’s a lot we’ve implemented to take that manual process of going and searching for information and asking around and we’ve been able to streamline it and do some neat things to improve that kind of experience so much better than we would have had many years ago when I first started.
Al Martin: You know, I just noticed, I think we’re dating ourselves.
Tracy Bolot: We are.
Al Martin: I think we should move on.
Tracy Bolot: I think we should scratch that one.
Tracy Bolot: It’s interesting but I think we should scratch that these because I think we are dating ourselves.
Al Martin: I have a couple of girls that are still in school they may need to give you a call on the physics side, you know.
Tracy Bolot: Physics, if they’re math people, they’ll design in physics.
Al Martin: One of them is. Anyway moving on, so getting back to cognitive client care support or client success, whatever.
Tracy Bolot: Client experience.
Al Martin: Client experience. What are the challenges? What are the, you know, what are the prevalent challenges that are facing client success today? Let’s start there.
Tracy Bolot: I think the first thing is, we still do - we still have - we need help. The client experience is all about, say, you have information that you need when you need it and is easy to get to?
And I think we still, in many cases, do what we’ve always done in years past. So everyone starts with a Google search. So if you think about it, so Al, let me ask you a question. Forget about work for second.
Give me an example of where you’ve needed to get help to do something, you know, trying to fix a problem with your telephone, trying to fix a problem with your car, your - give me an example of where recently, in the past month, you’ve not know what to do to fix something and you’re going for help someplace. How did you do that?
Al Martin: Well, I do my own taxes so I run into a problem, question on tax law or something and, to your point, I do exactly as you said, I go to Google even though I’m using a product called TurboTax that I know has a knowledge base that I can go to, I skip that. I go straight to Google. And sometimes it (heads to that) knowledge base but it’s a Google search.
Tracy Bolot: And so that’s a great example. So what you did there is stop what you are doing with your taxes. You may shift focus to the other browser window you have to think about what question you’re going to ask.
You had to type in that question and you had to hope that you phrased it in a way where Google can get the answer for you that you’re looking for. In a better example, while you’re in TurboTax, we would know what you were doing in TurboTax and be able to present information to you to help you get to know that you’re going to run into this problem.
Here’s some information and it would be easy to use an affront and ready to give you that information right when you’re in the middle of the experience. That’s the experience that I think we’re looking for.
Now, TurboTax would take it in a certain level but, like you said, you’re not choosing that. You’re choosing to go back to kind of an old-fashioned - let’s stop. Stop what we’re doing, use another tool, try to guess and then come back, and that all takes time and energy.
When you think about machine learning and how you can do things in the future, you shouldn’t have to shift gears like that and it can save valuable time.
Al Martin: I think - you know, I think you’re right. I think the reason I use Google in that context is because I think I get a greater aperture of information and also how others have solved that problem and I can kind of look at and leverage, you know, versus, you know, I don’t know, just the one single repository, I guess in my mind, that TurboTax maintains in their answer, if you will. I want to see how other people solve it, right?
Tracy Bolot: Absolutely. Absolutely. So what you’re describing is a very easy thing to do. So part of the repository that gets presented to you can’t be just that application’s repository.
It has to be all of the data that’s out there in a structured and unstructured way, whether it’s something to do with TurboTax or normal taxes that other people have found resolutions to outside of the program.
So if you have all of the data at your disposal easily presented to you, knowing what you’re trying to do in the system understands, okay, this is the problem he’s going to run into. Let me present him with all this information - use cases, and it personalizes it because it knows your preferences.
Do you prefer a video? Do you prefer the search for content? Do you prefer for the user manual? And if it knows your preferences, you can display it - display the information you want at the time you need without you having to think about it. It’s just right there.
Al Martin: So, you know, when I look at - there are so many challenges around client success today. I mean, I often feel like it’s maybe the hardest job in the planet because you have to think about personalization, relationships, reducing time to resolution, your cost.
We’re facing probably product complexity, maybe the highest it’s ever been against simplicity. Expectations are probably the highest they’ve ever been. You’ve got to have top talent. You’ve got to have expertise.
Now that we’ve got different form factors - what I mean by that is private, public cloud, hybrid cloud, data governance. You’ve got GDPR, you know, multiple sources, you know, across different portals that you’ve got to - for data that you’ve got to take care of.
Engagement - multiple engagement methods, whether it’s chance, virtual reality or - I mean, it’s everything today. I mean, do you see it the same way?
Tracy Bolot: I do, but I don’t see it as a challenge. I see it is a great opportunity because, when you think about placing people in higher value positions, I think back to that time I worked in support many years ago, the first few weeks on the job, what value do I have?
Well, I was kind of learning on the job, trying to help customers while I got to the level that they were. The things that you’re talking about, the complexity, it gives me an opportunity to grow my skills so that I become deeper and broader in my learning that I can become that expert on unified governance or update a hybrid management.
And I can understand more about what the client’s environment is because I know more about it and I can get some value that’s above and beyond the simple search would do.
I think it gets challenging. It gives a great opportunity for people who really are interested in helping clients and have their passion about learning new technologies.
Al Martin: So I think what you just told me is you see it is turning a dilemma into an opportunity.
Tracy Bolot: You got it.
Al Martin: So fair enough. So what do you think - or what is your view of the future of client success look like?
Tracy Bolot: So I think it starts with leveraging machine learning. There was a great quote via - a few weeks ago from (Jenny) where she called AI Assisted Intelligence, and I think that’s what it is, with support.
So if you have a question, machine learning gives you options that you may not have considered might be the resolution to your problem. So I think it starts with that. It starts with using the tools we have two help assist you with finding the resolution to your problem.
It streamlines it, puts it (in app). It’s all right there, present in the experience that you’re having so you don’t have to stop and shift to something else. And then it has - it allows you to have an easy access to those experts that you only get through a person has a deep understanding of the technology they are trying to use and of your own environment.
I think those three (unintelligible). You have to start with the tools and the technology to get those easier problems resolved and then you have to train the folks who were the line to help you - help customers figure out what the answer is in resolution is to those really deep complex issues that really need a human being to help you with. So training, tooling, all of that needs to go hand-in-hand.
Al Martin: When you talk about machine learning, that’s when I kind of - I agree with you. That’s why I call it cognitive care. Again, you know, I started out with digital care but I think it’s got overused and I think it’s really about machine learning and what we can do there.
I mean, think that’s the next phase, if you will, and so I tried cognitive care. I really think the magic will be in personalized relationships. I think turning that - limited opportunity, it’s almost like the challenge we have is, we want the client to almost be better off having had the problem.
I mean, because they’ve got an opportunity we’ve got an opportunity to demonstrate the fantastic experience you can’t get anywhere else. So you’re able to build rapport relationship with client on behalf of a problem that, you know, maybe they’re trying to get resolved, you know, or otherwise.
I think, again, the interesting thing here is that you got the opportunity with the technology again to offer preemptive support through ML, augmented intelligence, as you mentioned, and I think that’s where we’re headed.
Tracy Bolot: I agree. Although I would say that, I like the idea of client experience because like you said, it’s not just about solving problems but it’s about getting information you need whenever you need it.
So sometimes that’s not a problem. Sometimes you need more information on different (unintelligible) you intend on using anyone to know use cases around them.
So part of it is, it’s broader than just problem determination but you can use AI to help you out with some of that. So for example, we use machine learning to help us do prediction on where customers are not intending on renewing their - support contract.
So, and kind of the traditional support my best, that wouldn’t be kind of that part of client experience. It’s (wait) for just the contract and then we start at client experience.
In this case, we went to have a great client experience and if someone is thinking about not renewing their contract, we want to understand why everyone to predict that things are happening that they may not be happy with and maybe that’s why it so that we can put actions in place to fix it.
So I think you can make these machine learnings for predicting outcomes on things outside of your traditional support experience and that’s something that kind of extent that overall client experience.
Al Martin: So let’s take a quick step back. You and I have worked on a concept that we call a cognitive care platform and the cognitive care platform has six elements, everything from, you know, clients (for life) to machine learning and cognitive.
Can we talk - do you want to talk about those six and kind of detail a few of those I think that those are the six items that make up, you know, the cognitive care platform that we see as her future endeavor. Does that make sense? Can we talk about it?
Tracy Bolot: Sure. Sure, it does. So let me ask you a question though. Let’s start out with - so there are six elements. Instead of rattling off the six, let me ask you which one do you think resonates most for you? Which one do you think is the most important piece of that?
Al Martin: Well, I’d probably start with - I think they’re all very important, by the way, or there wouldn’t be six - we wouldn’t have six. Having said that, I think, you know, clients are the most important, so clients for life for keeping clients for life would be my most important principle in that home realm of things.
In other words, you - we need to drive clients for life in each one of those six items to make sure that we’re successful in the long term. That’s policies, lifecycles, are your products to how you address clients so that they say, you know, “Look, you’ve got the personalized experience. You’ve got something that’s differentiated that nobody else has. There’s no way I’m going to leave you to go to a competitor because I get more value in working with whatever business you’re driving.”
Tracy Bolot: I like that.
Al Martin: That would be my simple answer.
Tracy Bolot: I like that one to. I think that’s important also to show that we’re committed to working with our clients as they want. So there’s nothing worse than to be really happy with the way something is working and then find out, okay, someone has kind of bailed out of that relationship.
So I think that (hold) clients for life gives our commitments for client that we do intend to keep their relationship as long as they want that relationship to continue. I think that’s really important.
I think the other important one is - so, yes, they’re all important, but I think the other foundation to the cognitive care platform is that customized experience, so getting the customer what they want when they want it.
Some customers are more conscious of pricing and a rather be involved with things that will be - allow us to do kind of a broader view with (unintelligible), so community is another one of our cognitive care platforms, (unintelligible) is the community.
So some customers might just want the community. They want a good community that they can go and ask questions to. They don’t worry about how much time it takes to turn around. It has the forums. It has use cases.
It has references. It has documentation at no charge. Some customers want that and with our cognitive care platform, we’ve improved our community ecosystems.
So that we are continuously making more and more content available and we’ve allowed our - take a look at how we (cure) those deliverables and (place a) specific focus on things that would be kind of that no charge level.
Then we have another level that is - I want to get to my expert immediately and one click, one call gets the person who can result your problem. Direct to expert is another part of the cognitive care platform that’s extremely important.
In that cognitive insight that we talked about which is built in to both our community ecosystem and are direct to expert. And with that allows you to do is to use this machine learning to help find a problem that you’re looking for.
So their communities we have burning building through there. The way that you get to the expert you need, we have machine learning in there. And then how that expert can help you resolve your problem, again, she learning is built into the there.
So we have machine learning kind of underneath all of these six portions of the cognitive care platform which I think is what makes it unique where we would have been, you know, years ago.
So those are some - clients for life, the customized experience, direct to expert, community ecosystem, cognitive insight, and then one more which is integration expertise.
So whether you’re using multiple products that are IBM’s products or whether you’re using a mix of IBM products and other company’s products, we have experts to understand how those products integrate together.
And again, that’s something that’s a unique experience that is a deep, but broad set of skills, that we have on our team that help ensure that that integration that you have in these complex environments can be understood and we can help the customer with the best client experience possible.
So those are kind of the six areas. It’s interesting that you said clients for life is the most important. I think I know of underlining all of these is that personalized customized experience, giving our clients what they need when they need it.
Al Martin: Look, I think all of them are - you know you have it right if all six, can’t decide which is the most important, quite frankly, because I debate myself right now in terms of, you know, I think direct to expert.
Now - because it starts with talent and it’s backed with the challenge you have in your organizations. You don’t have products. You don’t have clients unless you have top talent.
See you could say talent backing this whole ecosystem is number one. Then you could make a case, as you have, that, well, wait a second, this needs to be a relationship so it is that customized experience.
Then you could take it further and say, yes, but the relationship is really coming today, built in that whole ecosystem with social, you know, everything from sentiment analysis to Twitter forums.
And then come you know, we’ve already talked about cognitive. So I tend to think that we have the right it’s because I were to name them again, number one would be clients for life, two, customized experience, three, community ecosystem, four, direct to expert, five, cognitive insight.
The one that could be maybe outside looking in, still important, but, you know, maybe not within the top five would be the integration experience. What do you think?
Tracy Bolot: I think so. I think also, have a look at which one is the most difficult to do well, that integration expertise would be the one. I think that, as you build on what you learn from feedback from our customers, so as you know, we’re all focused very heavily on our MPS responses to understand how we can continuously improve, we can make headway into the other five a little bit easier than we can with that integration expertise.
That’s a harder nut to crack I think, so I would agree. That’s kind of an outlier plus it’s one that can be more difficult to do, frankly.
Al Martin: I agree. So we have five most important and one challenge, a little outlier, but integration expertise, you know, something we need to focus on as well. Hey, so I want to talk about two things.
One is data, the data that backs the whole ecosystem, if you will, and then I want to come back to machine learning real quick. So, you know, for some of those folks listening, I think this might be interesting to start with this question, and that is, what sort of data is typically selected by an organization and how do you see it at bettering the overall client experience?
Tracy Bolot: So that something that we’ve had a hard time with in the past, I think a lot of companies have. We have data all over the place. We have data on what are - what products we have out there in the market today. We have data on what documentation goes behind the product.
We have data on what problems customers are experiencing. We have data on how many people are building those products. We have data on what our MPS and client passports are. We have tons of data.
The challenge is trying to get the data in a situation where we can pull insight into it. And also then, to make it easy enough to use so that people actually look at it and use it on a regular basis.
So what we’ve done is, we’re in the process of pulling all of that data together and then (displaying) it in a dashboard that can be reviewed weekly. And I think that the combination of the two, having access to all that data, finding insights from it, and then making sure it gets looked at, at least on a weekly basis starts to create a pattern of usage.
That’s really important because what I found is that, even if you can collect all of this data, unless people are looking at it and using it, it’s kind of hard. I - people want - you know, (unintelligible) a tool, one more tool that people ignore.
So I think what we’ve done in the last few months is pretty important, which is have a weekly meeting with all of the leadership team from first-line managers, to team leads, two directors and VPs, to go through what insights that data showing us and to talk about actions that we’re taking on the insights.
Al Martin: So, to restate, then, there’s proprietary data. There’s non-proprietary data. There’s structured data. There’s unstructured. There’s data that clients have themselves.
There’s data that, you know, about the - data that a client will have with their provider in terms of, you know, some of the support interactions, by example. And then there’s, like, data to solve issues, like, knowledge-based forums.
Where does the privacy, in the data governance (unintelligible)? I mean, that’s a big challenge right now, right?
Tracy Bolot: That’s a big one. So, there’s privacy in terms of - so when you want to look at data, you ideally want to see all of the data which does include sensitive private information.
It may include information that customers have sent us that’s private. It may include information about our employees that’s private. We have a worldwide team, so every country has different regulations on what we can show others have what we can’t.
So governance around the data is extremely important at this extremely critical. So, you know, luckily being in the analytics - data and analytics team, week have access to tools that allow us to do - to protect the data well and make sure that, from a governance perspective, we’re doing all the right things to ensure that the people have access to the data, only have access to the data that they truly are allowed to see.
So with GDPR coming up next year because we have a worldwide team, we’re relooking at all of the data that we are sharing. We’re making sure that we follow those regulations for GDPR.
In the past we’ve done some work around HIPAA and making sure that our clients are - our healthcare clients are only giving us the data that we need to (give out) the problem, and that anything that may potentially contain private information gets protected the way HIPAA requires.
So we’re constantly looking at various regulations to ensure the data that we have and we’re making available to others is only the stuff that they’re allowed to see.
Al Martin: So - so good. So then you take all the data in those disparate sources and then you just transition them to the machine learning because you train models to make predictions on said data? Is that the idea?
Tracy Bolot: Exactly. Exactly. Exactly. So, we have a large data warehouse where we pull all the critical data in. When we do, we preserve authentication to ensure that the - only the data that can be seen is exposed and then we use machine learning to predict things like (S&S) - support renewals, to predict client dissatisfaction, to predict product usage.
So we have predictions that we’re doing to try to understand what our clients will experience before we actually get to the point where they have a bad experience.
So we do sentiment analysis. We look at the sentiment analysis across the group of products to see if there are any trends there. And we’re doing this all with predictive modeling.
Al Martin: Will that be - you talked about in-app. Would that be built into the product itself, you see in the future?
Yes. Yes. So some of the things that I’m talking about are internal to IBM. We want to understand what our clients are experiencing. Other things are things that we would expose within the app to our clients.
For example, performance history and can you predict when a performance issue will occur? Can you predict what the next problem is that a customer may run into based on the products that they’re using and how they’re using it?
Giving those predictions and then having that available to clients in-app is exactly where we intend on going over the next 12 months.
Al Martin: So kind of - it’s kind of in the same way that insurance companies are trying to predict churn through their client behavior, a question, by the way, that I get a ton when I’m visiting clients. So to support organizations, you’re going to have to predict product issues via both client and product behavior, it sounds like.
Al Martin: I don’t think that was mentioned that, really, it’s advantageous, I think, is a future state is what cognitive can do with natural language processing. By example, if you had a chat with the customer that has a question, they could be chatting in Mandarin.
It could come across to an English speaker where you have critical mass. They can answer the question. That goes back in Mandarin which would be pretty cool if we could do that at some point. And we thought that technology today and we’re working on it, right?
Tracy Bolot: Well, exactly. Plus, we’ve also noticed something. I saw a study that we had done just recently with understanding how machine learning and predictions will work with other languages. And it turns out that you don’t have to have an exact translation in order to get a valuable AI machine learning response.
So, there’re some things that we can do there that may help us with our answers to questions in that we don’t always have to be exact in the translation, but yet, machine learning can still give a very valuable assistant response. So, many things coming there from a natural language processing perspective.
Al Martin: You know, I actually think, and - I actually think in terms of this and ramping this kind of up, I also think that in the future, given all the likes and, you know, things with forum, et cetera, you know, I think gamification and potential incentives for those that are providing client care are going to come into play. You see that the same way.
Tracy Bolot: I do. I do. But let me turn it back to you and ask you for an example of that.
Al Martin: Well, I think that - I think there’s - you know, people are prideful on the, you know, in (flack) overflow, and other forums and our forums, they’re prideful and the answers, the accuracy, quality of the answers that they’re providing.
And I think that lends itself to the gamification as, you know, the community rates or evaluates the answers said person is providing. I think that, you know, people get competitive in that regard and it’ll end up producing better quality answers that will also be leveraged in the same machine learning and cognitive (bots) that you’re referring to.
Tracy Bolot: Absolutely right. Absolutely right. Yes, I was kind of curious if you had a recent experience with that that you wanted to talk about in terms of where you’ve seen that.
So I can tell you, just in terms of every day when I go and look at new products or new services or the other day I was looking for a new dentist, and the first thing you look at as you look to see what other people are saying and you take those reviews to heart, right?
All it takes is a couple of people to say great things or poor things to weigh your (adjustment) on what you’re going to do next. I think when you look at machine learning and cognitive and how we help clients find out answers for whatever question they have, that weight of other people giving their own experience is far greater than just finding some anonymous tip in a database somewhere.
So I think the quality of responses is also quite built on how many other people who work kind of you see are like you give that answer as a good answer. So I think it also provides some meat behind the recommendations that you find as you’re going through trying to find an answer.
Al Martin: Yes, I totally agree. By the way, I think I might have said (flack) overflow. I meant (stack) overflow, if I did. I don’t know. Anyway, hey, let’s - look, you and I could probably talk forever but we can’t. We’ve got other things we’ve got to get done.
So I want to ask you a few questions which I call the lightning round, more personal questions just about you, if you’re okay with that. They’ll be simple, I promise you.
All right, the first question is, hey, what’s the one thing that you want to learn right now that’s kind of at the tip of your tongue that you say, boy, you know, this is kind of on my list of to-dos to learn either now, next year or sometime soon?
Tracy Bolot: So right now, I’m trying to figure out how to give the best in-app experience as possible. So I have some discussions set up with our design team. I think you’re probably well aware of - I’ve been focused on design over the past couple of years.
I have an expert in design where I’m hoping to learn from them how to best streamline the experience within (app). So that’s the thing I’m hyper focused on over the next couple of weeks.
Al Martin: Fantastic. What’s the most important habit you think leaders need? Or let’s ask it a different way. What’s the best advice in terms of a habit or something of leaders that were provided to you?
Listen to your team. Listen to the people who are working with you. They work with you, not for you. Listen to what they have to say because everyone comes from a different experience.
Everyone has a unique value to add as you’re working through issues and questions and plans. Spend the time to know, meet, understand your team because it’s an important piece of the overall (unintelligible) of how your product is going to do in the market.
Al Martin: What’s the best leadership book that you’ve read that you can think off hand anyway? Got a good one?
Tracy Bolot: I don’t, off the top of my head.
Al Martin: Okay. That’s all right.
Tracy Bolot: You caught me there.
Al Martin: That’s all right. That’s all right. These are blind questions so I understand. So people listening know that these aren’t set up by any means.
Good. I think we’re done.
I’ll give you the last word if you would like to say anything, but I appreciate - I think - I appreciate everything you do and I think this has been a great conversation.
Tracy Bolot: No, this has been a great conversation. And I think for those listening, it would be great to hear other thoughts on how other companies are looking at the cognitive care idea and what types of things that they look for in the future. I would be interested in hearing ideas on that.
Al Martin: Yes, I totally agree. So I think we’re done. So until next time, thank you for listening and wheels up. Talk to you next time.