Ep 43 - Michel Guillet - Leveraging product and data-driven thinking to enhance sales teams
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Episode Summary
“I think that one of the main keys is to really focus on the workflow. Workflow first, like what are they trying to accomplish? What’s their day? What are they doing every day, week, etc.?”
Michel Guillet, Sr. Product Manager at SalesLoft
This was an episode where I talked with Michel Guillet a Sr. Product Manager at the fast-growing SalesLoft startup where they help companies engage with customers, build pipeline, and close revenue, faster and we discuss how human-centric and data-driven approaches can help sales better succeed.
Sales people and sales managers are constantly under pressure to close deals and reduce executive revenue uncertainty despite an unpredictable world and the pressures of life and work. Michel and I talk about some of the challenges sales people and managers face and how human-centered and data-driven approach can help generally including some of the things SalesLoft is doing to help its customers.
More about Michel:
SalesLoft: SalesLoft
Michel’s LinkedIn: in/miguillet
go Beyond the Data podcast
Ep 43 – Leveraging product and data-driven thinking to enhance sales
Michel Guillet of SalesLoft
Machine Generated Transcript via Descript
Dave Mathias: [00:00:00] Hey everyone. A couple of quick items before getting started today. Really good to have you here. Of course. today we're going to be talking with Micel Guillet. It's gonna be great interview so I hope you get a lot from that, but before getting into the interview, just wanted to mention that you will now find this as go Beyond the Data in your feed. That isn't a mistake.
Go Beyond the Data is all about using data to make better decisions, but really going beyond the data in leveraging things like behavioral science, product management, customer experience, user experience, and other things that organizations are trying to leverage to change the status quo, make better products, have better insights and have better experiences. So we're not gonna just be covering things around data.
Now let's get into today's episode. Today I have Michel Guillet with me and Michel is an awesome person that has a ton of experience. He has 25 plus years experience leading the design and implementation of data and reporting solutions.
Hey Michel. Good to have you here.
Michel Guillet: [00:00:56] Hey Dave. Good to spend time with you.
Dave Mathias: [00:00:58] And your experiences, includes, working with structured and unstructured data across digital advertising, consumer goods, healthcare, insurance, like tons of industries. You're also a senior product manager, your day job. You're senior product manager of analytics and data science at this, this little fast growing startup.
That's not so small now, called SalesLoft in Atlanta. We'll talk a little bit more about that, but you're also an adjunct instructor of data viz and presentation, which I love the presentation part, not just the visualization at Georgia State University - Robinson, College of Business. We'll get into that a little bit more too cause I think that you'll have a good perspective also from, being in the academic world, in addition to being in the professional world. Good to have you here with us today.
Michel Guillet: [00:01:37] Yeah. Thank you, Dave. I'm excited to spend time with you. I enjoyed our conversation a couple months ago, so this is great.
Dave Mathias: [00:01:42] This is awesome. and so we're talking on a Friday morning and love to learn a little bit more about you. I know more about you. You're really interesting. Hence why we're having you on here, but tell us a little bit more about how you got to the career point where you're at right now.
Michel Guillet: [00:01:56] Sure. I'll just give you some snippets. My undergraduate was really work was in international economics where I thought I would work on a crop and agricultural issues in West Africa. My obvious French name gave me some interest in kind of, leveraging West Africa and the French nation and doing that.
And I, my first job out of college ended up being with Estee Lauder, where I got to help with a lot of the pre EDU transition, doing a lot of inventory analysis, hands on the fact. I dated me a little bit, but I started using, the Excel predecessor Lotus one, two, three in 1988.
Dave Mathias: [00:02:30] Oh, wow. Ouch I do have some distant memories of that. How does actually, as a kid, it's not good memory, so I can tell you.
Michel Guillet: [00:02:39] Know. Through no, a little bit of academic background, but just through my own training, just became a little bit of an Excel. Was it real be pan and throw through graduate school Anderson, Andersen consulting work, et cetera, just continually refine that. And, over time started to lead teams and, spent a bunch of time, doing audio analytics.
Hence the unstructured Mike, I spent time with speech works, nuance and speech recognition. we looked at largely audio analytics in a big way. And then right after it was at nuance that I largely made the jump to product management. And in a, my niche in the world is not really product management.
It's really kind of data solutions where organizations need assistance, making those data solutions valuable for their customers. If that, as that is my niche in the world, or at least for the past 10 years,
Dave Mathias: [00:03:29] Yeah. that niche is getting to be a bigger, important part and less niche-y as we've gone to, obviously digital products have become a lot bigger. And at Sales Loft tell a little bit more about Sales Loft and what you do there, what the organization does. It gives perspective to you and what you're doing now as a senior product manager in analytics and reporting.
Michel Guillet: [00:03:46] Yeah, Sales Loft is great. So we are, cloud-based, sales enablement platform. If you think about contextually, like you have marketing automation have Salesforce and CRM, we are basically the system of execution. We are the way that sales folks do prospecting deal engagement, and then customer engagement.
so it's relatively new and the space, but we're an eight year old company growing rapidly, serving the needs of Small companies that need to sense a sell as well as large enterprise organizations. the part that gets to me excited about being at Sales Loft is our mission is really focusing on changing the selling and buying experience for both the seller and the buyer.
Not just producing sales software, but really focusing on experience, in our kind of our team's role, really, I get excited about our team. Our team's role is to really facilitate kind of the display and presentation of information to salespeople and sales managers, so that they know what to do and how to better, run their organizations and be more efficient.
Dave Mathias: [00:04:46] We'll get more into that. we're going to be talking a little bit about design data solutions, especially for those non-analytical customers sales, both on the buyers and the sellers are obviously fall into that category. What is the thing that you find most interesting about your job?
What gets you up and energized every day for your job?
Michel Guillet: [00:05:02] You don't I think what gets me has always gotten me excited over the past couple of years where I was doing healthcare work with nurses is, getting people who don't consider themselves analysts excited about data, right? When they have those Eureka moments of those small Epiphanes like that, they get something and they know what to do that's clearly what good gets me excited.
Dave Mathias: [00:05:21] We might as well get some tips out here and get your wisdom right now. So what is the biggest tip? if you're a person that's got a similar role or similar challenges yourself, what would be your biggest point of advice to get people excited that are non data people?
Michel Guillet: [00:05:35] So I think that as data people, often we think about the data itself. And I think that one of the main keys is to really focus on the workflow. Workflow first, like what are they trying to accomplish? What's their day? What are they doing every day, week, et cetera. As an example would be in sales, like there might be a weekly sales meeting, and just subtle things like, how do we make that meeting successful?
Dave Mathias: [00:05:56] Yes. And not so painful for the salespeople and productive.
Michel Guillet: [00:05:59] Yeah both at Sales Loft and previously at applied systems in the insurance industry, this idea about like, how do you facilitate the right conversation between the sales manager and the reps, or the sales development reps, to be able to have a fruitful, productive conversation so that both parties, walk away saying, I know what to do. I know what to resolve. I know what the next steps are.
Dave Mathias: [00:06:19] Yeah, that's great. And so let's dive into this a little bit more when you're talking about designing data solutions for non analytical customers, how do you start with that approach? When you think about things in the sales, let's talk in the sales segment cause that's where you're at now. And I've had a fair amount of experience in that sales segment too. How do you go about designing data solutions for those non-analytical customers? Like a salesperson.
Michel Guillet: [00:06:39] so obviously one of the reasons is charting or summarizing. The data becomes, a point is that the trade off is understanding, like what is the best chart to display this information versus what the chart that's familiar that may be less productive, but they're more likely to use. Alright, so a four quadrant or a scatterplot or a bubble plot may meet with multiple axes.
Three metrics might be the ideal way to display some information. However, a horizontal bar type effectively might be the way that they could look at it and not have all the information, but enough to be able to. so I describe it, Dave, is this idea about trading off between the productive versus the familiar and striking a balance between those two things?
Dave Mathias: [00:07:20] So I think one of the things that we like to show is we like to show too much data to people, especially non analytical folks and showing people the right information at the right time that is meaningful action, but balancing that also where you don't want to make people robots, you don't, we don't want our frontline people, no matter what the positions are, sales, certainly that they're just robots and certainly having a process of understanding the process and executing on it. But we also, being able to really understand sort of first principles thinking and being thinking, understanding why things are happening and being able to make adjustments on the fly.
How do you balance that with. getting information to them at certain times. I think that's always the challenge, cause I think we're facing that in a lot of our lives where it's like, how much is it are just, we're dictated by our calendars, for example. And our AI is saying, Hey, this is going to be the most efficient people to follow up with and I've just booked your full day as a sales person, versus how much you have the salesperson think or, they feel like they're just picking up the phone.
Michel Guillet: [00:08:16] Yeah, great point. Let me break that down just a little bit. the idea that we're first aligning to workflow means that we're not adding to their work day. We're basically trying to make what they're do already better. So that's the baseline start. The second is that understanding kind of context about what information is right in that context, right?
So you could be saying like, this review is a QBR, but which could be a quarterly business review. So obviously can I compare this quarter to the prior quarter? That's a, that would be table stakes in that context. So I think context becomes a second big piece. And then the other part, I would say, David's the delivery, right?
Is it purely audio? Is it actually a presentation on a TV? what exactly is the delivery of the media that's being used to convey that information? I think lastly is to where you were going is determining like the degree of a prescriptive solution versus exploratory, And the idea that you can be very prescriptive in saying here's the cool, the default, but then can I then change? Can I use interactivity as a way to facilitate exploration? And then second secondarily is that can I have access to it details in case I want to be able to go and do some ad hoc analysis.
Thinking about those, I think the part where people go wrong is that they either go one extreme and saying, what I hear is, I definitely have heard this before in consulting and elsewhere is that. I talk to 10 customers each of the 10 send 10 different things. Let's just give them the data, right?
No prescriptive, no guidance whatsoever. Versus having the, kind of the ability to glean to say, what is common and across these 10, that they're all asking for. And what can we, what can use that as a starting point?
Dave Mathias: [00:09:53] Tied with this I, that I think a little bit, you can push back if I'm maybe jumping a little bit farther down, but, the tendency. behavioral design. so when I think about when you're, especially for sales, for example, is, designing software that's going to try to get to the optimal efficiency for salespeople in general, versus how much do I understand more or I try to understand the tendencies, the strains of that person. And I'm trying to get the best out of that person. Because we're not all the same. We all have different tendencies, we all different likes different things. So how much of it is, software adapting somewhat to the individual and providing insights versus trying to just pull people to a certain baseline or certain sales person type persona types, action type.
Michel Guillet: [00:10:41] Yeah. so this is a great tee up for data science. And I think what you described now is where we think about data science as being what, here's the static report, the prescriptive guidance guided narrative that we're providing for both David and Michel, but what's different about Michel and what can we enhance through suggestions in some way to differentiate because there's something contextually. It may be not the right metaphor, but it's one, we think about a little bit, is this idea about the Netflix recommendation. We all use the same medium but being able to tell me that, by the way, other people, like you also did this. Giving that little slight nudge or that suggestion to a salesperson to be able to guide them beyond what kind of the prescriptive solution is. I think that's where that sweet spot that we want to go to.
Dave Mathias: [00:11:24] Yeah, that makes a lot of sense. So tell me more about what you've seen as successful in this space and developing software, not just obviously yet SalesLoft right now, but also consulting in other places and from my, from a perspective of being a senior product manager in that space.
So you as a senior product manager, you're really focused on aligning, the business requirements where the business strategy to what the customer need is and then obviously, yeah, leveraging data and analytics. Tell a little bit more how that intersection works in what you're doing.
Michel Guillet: [00:11:58] Sure. I think, I'm biased, but I think my academic training as an economist kind of lends itself to being able to Thinking about things from a marginal utility perspective, what is the, where do I get the biggest bang for the least amount of effort? Very traditional kind of product management techniques.
But when you think about it in the context of delivering yeah. Information, it's even more important. I don't know if you're familiar with Amanda Cox she leads the team at the New York times. She is one of my favorite human beings. both in terms of her intellect, but also her, sense of humor. And she's has a quote from years ago that says, data is not like kids, it's okay to have favorites. All right. And what I take away from that is always like what makes sense, which what parts of the data are going to get my users to take action. And they get them to sit up and take notice and want to do and be motivated to do something there's contextual pieces, which are usually secondary.
What are the three to five pieces of information. And I think that ends up being really the starting point. What are the three to five pieces of information that we would change in salesperson's life? Am I having a good day or a good week or a bad day or bad week? What are those to five that are most critical to me? And then everything else becomes a contextual assist. How we deliver that after that?
Dave Mathias: [00:13:09] One of the things you were talking about earlier was also from a leadership side sales management side versus the frontline. how much does your software, thinking about those very different personas, but the objective should be aligned or hopefully is aligned the goals of each. How does your software treat those different audiences different? Or do you think these audiences should be different? How do you go about that?
Michel Guillet: [00:13:32] Yeah, that's a great question. I think that we think about these distinct roles. I think we need to do a much better job on it, frankly. I think we're on that really good path to just saying what is the information that a manager needs every day versus kind of a, see our sales development rep, what, or how do they, should they start their day?
So I think largely that starts in the UX research side. To better understand what those things are. And that becomes a premise about how we do that. I think how we deliver it, that is making the software in a very component way. So if we were to think about in terms of dashboards, the panels on a dashboard might be different for every user will based on their role.
We might give that we might actually be prescriptive in terms of, Hey, here's what we think you should see at the top. Here's what should the detail of the bottom, but potentially also give them the ability to configure it themselves.
Dave Mathias: [00:14:19] Yes. Yeah. that ability to have some choice in some adjustment. And so what things have you seen most challenging in that software space, for sales, for adoption purposes, where they're not resonating with the data. It's not working at the level of what are those challenges that come about. And how do you think they can be overcome better?
Michel Guillet: [00:14:38] yeah. great question. no different than anywhere else, right? How do you highlight for our customers or users when clearly the problem is their data, right? Their data is a little bit off, right? Is there a way for us to gently kind of surface to be like, Hey, the data is incomplete.
And you're not going to get the full picture in that way. So I think that's the kind of, because that will be the difference among our customers. Some have very raw, they have a lot of resources, a sales, operations team, strong administrators, where the data quality is really strong. There they're constantly improving the quality and those that are strapped where they have multiple people wearing multiple hats.
How do we help those users, how do we help those customers so that they, because they don't have some of the administrative resources or capability, how do we account for that a little bit, something we're still wrestling with the honest day, but I think that's the right way. I know that in a prior life, in the insurance space, we basically knew that 13 ways data could go bad.
Right missing data misspellings, et cetera. And we would basically have a little bit of data science, mostly regression type things to make, to run a report for our team internally to suggest, Hey, here are the things that you should recommend to our customers in terms of what's where are there opportunities to correct their data might be.
Dave Mathias: [00:15:52] So related to that is the communicating uncertainty. And so part of that uncertainty is going to be obviously higher if your data's not as good. And so how do you communicate that on certainty and where you might even communicate to help the motivation. To clean data more might I also lead to that.
Where, why are we so uncertain about this, two quarters from now and things like that.
Michel Guillet: [00:16:13] Yeah. So there's some, there's a little bit of a day of science, so there's a little bit of applying some scoring methodologies to be able to Hey, I'm looking at various set of deals. You know of all these deals giving us general nudge your suggestion to me like, Hey, this information is on this deal.
It's a little bit incomplete compared to or others. Alright. So just as an example, the second one would be is just really I think one of the things I love about Sales Loft, we've made such a serious investment in design and UX and research. And I think that not only having designed concepts, the testing, those things, to see that we're getting the right feedback from customers and users about Hey, do you understand what this is?
Oftentimes it on the date of this side, we use color as a way to do that. But. Design, our design team has many other techniques to be able to convey, not just uncertainty, but just Hey, incompleteness.
Dave Mathias: [00:16:58] That makes a lot of sense. You've obviously covered a lot of industries and you, as part of that, one of the things that I know that we were talking about before we actually started pressing record is just the importance of curiosity for folks that are in either in data or in product or space like that.
Talk a little bit more, we're even talking about different ways to test for that. And, just thinking about, from our different experiences, talk a little bit about how you viewed just a curiosity in these spaces. How do you, what do you look for that? how do you encourage that?
certainly there's traits that people are more curious than others, but yours also, I think there's environments that encourage curiosity too. Cause there's curious people that get stuck in environments that really don't encourage that. So it gets stamped out. Some is part of the problem.
So can you talk a little bit more about what your experience is and how to both create an environment that has good curiosity and also how do you, help discern. Is this person going to be a good person? Cause they're going to have a good level of curiosity as one of the items.
Michel Guillet: [00:17:56] Yeah, let me talk about it two ways, if that's okay. Dave, you want to talk about in the context of a team and an analyst team and a development team, and then also in the context of users, in the context of a team, like if we had an analyst team or I've had analyst teams in the past, curiosity becomes really tied, strongly to.
Your ability to clarify, not just the problem, but the big problem. So as you can imagine in a sales context, that might be like, what can we do to help our customers grow their revenue this quarter? You need, you require curiosity on the team. Even if the team doesn't really know. a lot of our dev team.
Has never done sales, but how do we facilitate a curiosity in them to under better understand sales? And I think it really goes to framing a bigger problem, not just a tactical, Hey, we need to put a number on this screen. So I think that's one way and I think it's also related to the team as is.
Giving them the opportunity to like, get excited about that. Hey, you're gonna have, one of the things we talk about with teams, so it's like, Hey, this is going to impact greater than 30,000 users next week. And I love the fact that our team gets excited about doing that.
They don't shy away. They don't shy away from it and shy away from a big problem. They don't shy away from having a big impact. So I think that's curiosity can be largely rooted professionally in that way. I think that when it comes to users, it's really this ability to you think of the visual cue I would give everyone is just like this idea of an ellipse, right?
There's a little bit more right. Give them a little bit of a nugget, but give them a place. If they want it, they can go more and then take an interest from a product analytics standpoint. Like where are they asking for more? What part of the app are they asking for more? Are they more curious about that?
But I think there's always a path to being able to give them a, just a little bit more. in fact, in some ways I think it's, we make the mistake of putting too much information on the page very often versus saying, let's just give a few pieces of nuggets of data, and then if they need more, there's a place to get to it.
Dave Mathias: [00:19:47] Yeah. And I think that more, but also in prioritizing sort of the impact, the, again, that big goal that your, the user's having to. and so I think what I've seen a lot, especially in the sales context is on the sales management side. There they'll slice and dice, as soon as they get. Something to start looking at, they'll just find, and they'll find what they want to find almost, where you think it's going to be a tough quarter.
You're going to find as much, many reasons why it's going to be tough. And so how do you help users get out of those? Some of those natural cognitive biases that we face that are probably not either overly confident or overly negative and both. And I think a lot of these, just like being an ass fleet, I think salespeople, I always think of A good salesperson oftentimes feels like that because there's so many ups and downs and you're going to have the wins, but you're going to have so many losses and part of it's grinding it out and showing up and trying to keep that good spirit throughout.
So how do you, help that from a datas, from a sales product side that can help those salespeople, whether it, but also helps the sales managers, where they have obviously their own pressures on that front.
Michel Guillet: [00:20:50] Yeah. I think that the nature talking to customers and even internally on our services team is convinced them that we don't have, we may not have the answer, but we're low. Our goal is to give them the ability to ask better questions, right? To drive them to a point to be like, I don't have to, I have to go give Dave some bad news because his performance is underperforming. Give me the right information. So I can go ask Dave the right questions about what's good. The other, this is something I've I there's little bit of debate in kind of the community about whether data Abbott tells you why skewed towards the idea that data really never tells you why.
Okay. So you won when, how, what, et cetera, a conversation is the way the Y happens, right? That's where like Dave had a crummy week. I don't know why they've had a crummy week, but I can at least better understand, like Tuesday was awful. let's go talk specifically about Tuesday, Dave, tell me about Tuesday.
Dave Mathias: [00:21:41] Yeah. it is interesting cause I do wonder how much in the AI front, how much we want to more encourage that behavior in those conversations and trying to not just over be overconfident in that we see these numbers that this is, not that though. We want to ignore the data, but we want to use the data in addition to those human conversations and not.
And my fear is that we're moving too quickly and we want to go down that path too quickly. So
Michel Guillet: [00:22:07] Yeah, so sales to me. And the way we think about sales and Sales Loft is all about the relationship, right? It's about improving that experience. So the idea that we would decouple, Have information decouple that is, would be different, right? The idea that we should support those conversations.
Dave Mathias: [00:22:22] Definitely. Definitely. I know we're running up on our time a little bit, but one of the topics wanted to hit on a little bit more is data ethics and product managers. And it's certainly a big topic nowadays and love to. from a, from both a product person and a data person like yourself, and we're both fall into that category.
What is your view of what responsibility product managers have around data ethics? What can they do to meet those responsibilities and such.
Michel Guillet: [00:22:48] a little plug. I have a workshop at, GSU for data ethics, where I work with data scientists, getting them to the way we frame it up or the way I frame it up is, it's not, if you're going to be an ethical conversation about data it's way. Okay. So broadly what we do with the students and I do with our internal teams is we're developing the muscle.
We're practicing and thinking through scenarios to develop that muscle so that when that real scenario happens, can we're ready? I think broadly for product managers. Obviously our legal groups are going to keep us honest from a privacy standpoint. I think the two places where things go awry or one on the fancy word called Providence, which is where did the data come from?
we scraped, we were in you're in a startup. We scraped it off some of the site. You know were we allowed to do that? Any risk there? Secondly, so Providence becomes a kind of a concern from the product manager side. Secondly, I think particularly in the context of AI and data science, that the biggest risk that all of us face is, the issue that we're going to negatively impact one audience. That audience could be small. It could be. So for instance, if you view sales off an example, Oh, this only affects 1% of our users. 1% of our users, it could be 300 people. I think that we have to be aware of those smaller communities or the smaller groups of users.
and if you're in if you read all the stories about where AI go goes bad from an ethical standpoint, it's not as impacted the majority. The bank loan who did get a bank loan it's who didn't get a bank loan. So I think that's the big thing that we have to be really focused in on is understanding our product manager training is very much the marginal unit.
most people will get the benefit from this. But particularly when it comes to ethics and data and AI, I think that's the risk. We have to be hypersensitive to.
Dave Mathias: [00:24:33] And so is that risk to be born by the product manager or how much is it just the product manager engaging legal? cause I do wonder on some of these things where I think we're trying to make product managers to too much for unicorn type of role and how much can they really understand things like the CCPA and the GDPR and all these other things.
And they should understand honestly, basics of those types of things. but, what's fair use for certain scraping and like what way you're using. one thing that I've always recommended for product folks is just to engage the experts early in whenever. And just your biggest role is in that standpoint is issue spotting.
Like you need to be able to spot. there could be an issue here and getting people that are really a lot more versed in that than you. certainly if you're in a small company, you may have to wear so many hats and probably not do a great, but, at the same time, I think for, especially when you're a more sizable company, it's just a matter of getting people involved early, in that, is that fair to say? Or what are your thoughts?
Michel Guillet: [00:25:31] Yeah, I think we're thinking the same way. I think that product manager has to own the conversation, This conversation needs to happen with legal. I need to own the conversation and make sure that the conversation happened. We document it and the decision we make collectively as an organization.
But I think the product manager needs to own the conversation and make sure that it happens and not avoid it.
Dave Mathias: [00:25:49] Yeah. The key is a lot of these conversations early, before a lot of investments spent in that it's wait a sec, we can't do this product, that we were depending on that we spent all this money developing, seeing that, too often. Okay. any other words of advice. For a person that's a, in your case, more of a data person turned product person.
If somebody would want it to follow along in a career path, similar to yourselves, what advice would you have?
Michel Guillet: [00:26:10] Feel comfortable knowing the customer, I think for a lot of technical folks or data folks, the idea about, bias confirmation bias becomes a big issue. Oh, I know what you need because I know what this is what I would use. So I think on a product, the data person going into product person, the biggest risk is the confirmation bias.
You like, I know exactly what you need. And I think you have to walk into it saying. All right. I'm a complete, like for me right now, I've had experience working with salespeople previous lives. I have to go into the conversation. Like I don't understand sales, but explain it to me as if I don't know anything.
And so I think that's the best advice I think, on the reverse product manager, wanting to know more about the data is I think just thinking in the context of a basic Excel, right? Those Excel metaphors play out really well. Yeah. if you can mock it up in Excel, if you knew how to create an example in Excel, we're just talking about scalability and sophistication, but if you can talk the talk in Excel, you're more than half, more than halfway ready.
Dave Mathias: [00:27:06] I know that those are both great piece of advice. We're going to hit a few. actually, one of the questions I want to ask, we're not going to end on this, but were you, is what is your biggest failure that you think you've had in your career that shaped you where you're at today and what did you learn from it and how did it shape you in a positive way?
Michel Guillet: [00:27:23] So I can I'm sad to say. I've probably made the same mistake a couple of times, and I made it a few times to get it right. There are a couple of times others raise concerns to me about data, and I knew the data and I knew the situation and I made him, I said, I'll give you a little detail. So there was a piece of some information for a large bank that we were working with.
The information was wrong, but I knew the information would be wrong, but I knew it wouldn't be significant. So if you think about a large bank, we're talking like, it was call center data. It was tens of thousands of users, right? The data was off by 4%. I knew it was off by a small number.
It's an insignificant, they weren't going to change the business, but the customer and ironically the service people, they were very concerned. And my body language and my attitude did not reflect the urgency that they had. So I knew what to do. We fixed it within a day, but I think in multiple cases that like a turret, my, a little bit of my credibility, sometimes, I've been in, I tend to not, I tend to not panic or get worked up about things.
And that sometimes that works against me.
Dave Mathias: [00:28:23] Yeah, that's great advice. so certainly you faced that a few times, but we're talking about how to have greater empathy for the audience. you're talking with them. What ways have you been able to learn and apply how have you been able to change some I'm? sure you've obviously recognized it. So first step is getting over denial. So admitting, can you, tell a little bit how you've adapted, based on that.
Michel Guillet: [00:28:46] Yeah I think my interviewing techniques, I'm playing a lot more advanced interviewing techniques that you would use in product management, but really restating the problem back so that I have their perspective and it's not mine. So that becomes the easiest way. Oh, The meeting, you're going to use this information in the meetings wrong. That meeting is really important for these reasons that I get that right.
Dave Mathias: [00:29:06] That's great advice and something. Everyone should be taking the count if you're not doing it already. Yeah.
Michel Guillet: [00:29:10] And data nerds tend to have a little bit of a purse, especially when you're talking with non analyst, you can come across as arrogant or others are concerned about, Not saying the right thing. So there's already a level of anxiety when the non data person's talking to a data person in some cases, and you have to you have to be able to work with them versus take your own perspective.
Dave Mathias: [00:29:29] Yeah. using things like not using acronyms and trying to create an environment, that's more equal playing field that you're not, trying to, show things in let's go draft this dashboard together using. Tableau right out of interest, do it together versus let's just go to a whiteboard and those types of things.
I think, ways that you can try to create greater parody. So can I ask you a couple of quick rapid fires and then we'll go into wrap up. Cause I know we both have things coming up, got to ask, since you are a data viz instructor, as one of your things as a, at the university is if you were a database, what date of his would you be?
Michel Guillet: [00:30:01] I would be a, sparkline a simple, single purpose, very focused, one goal only that would represent me. My wife would probably say a scatter.
Dave Mathias: [00:30:13] Is he, at least my wife would say scatterplot. And honestly I say I'm scatterplot too. Cause I've admitted by now.
Michel Guillet: [00:30:20] maybe I'm still in denial.
Dave Mathias: [00:30:22] what is your favorite book? it doesn't have to be a data or product or anything, but it could be just pure pleasure book. What's your favorite book?
Michel Guillet: [00:30:28] Yeah. it's, it changes, years ago, my mom gave me the book Small Is Beautiful Economics as if People Mattered. So that was really her way of getting me to think things. So I think that had a really strong influence on me early on. I think there's two things right now.
The Checklist Manifesto by Atul Gawande, is my favorite business book and the one I recommend to folks the most and then pleasure reading goes, the Drunkard's Walk. It's how randomness rules our lives is something I recommend as well.
Dave Mathias: [00:30:58] Great that's a few recommendations. And so before we go how are people gonna get ahold of you? Where should they follow you? That kind of thing. What's the best spot for them to, if they want to learn more, want to connect with you?
Michel Guillet: [00:31:10] I think LinkedIn is probably the best way.
Dave Mathias: [00:31:13] So on linkedin that's M I G U I L L E T so M I G U I L L E T looks like the right way to get ahold of you then .
Michel Guillet: [00:31:24] I am on Twitter, but I generally just use it to follow folks that I'm intrigued about. or when I'm looking to learn about, for instance, I'm deep in some machine learning Things I'm following a few folks on the machine. So I'm using it as a way to consume information, not necessarily to share or publish, but LinkedIn, I am more inclined to share or interact with folks.
Dave Mathias: [00:31:42] Excellent. So we'll put the link out there for your LinkedIn and hopefully some folks will connect with you So any other, things you wanted to hit on before we drop off?
Michel Guillet: [00:31:51] No, this has been wonderful. Thanks Dave, for kind of making this happen. I really appreciate it.
Dave Mathias: [00:31:54] Yeah, great talking with you again and hope all is well, you have a great weekend ahead and we'll talk to you soon.
Michel Guillet: [00:31:59] Thank you, Dave stay well.