WEBVTT
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Hey, what is up?
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Welcome to this episode of the Wantrepreneur to Entrepreneur podcast.
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As always, I'm your host, brian Lofermento, and I am so excited about today's guest because we throw so many big buzzwords around in business, and one of those big buzzwords is data.
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It's something that none of us can ignore in any of our businesses.
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There's so much to think about, there's so much to compile and organize and try to make sense of, and that's why today, we've got an amazing guest who's actually going to help us make sense of our data, understand what data is valuable and, most importantly, what we can do with that data.
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And actually I didn't tell him this off the air, but he's also one of the brilliant minds behind the growth of one of my favorite companies in the world, because it is a tennis related company.
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So today's entrepreneur is in the field, across industries, doing deep and meaningful work.
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Let me tell you all about him.
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His name is Dave Aaron.
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Dave is a data and analytics leader with over a decade of experience creating business intelligence and analytics solutions.
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After stints leading data practices at Wayfair and Landing, he founded the Boston Data Company in 2023 to help businesses of all kinds create or level up their data environments.
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So if you're sitting there thinking I don't have a data environment, what is that?
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Well, that's exactly why we've brought Dave on for today's episode.
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He lives in Boston with his wife and two young children.
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He is also one of us.
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He's a fellow entrepreneur.
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He doesn't just do these things because he's a subject matter expert.
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He helps other businesses do it through his own brand.
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So I'm excited to learn from him today.
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I'm not going to say anything else.
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Let's dive straight into my interview with Dave Aaron.
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All right, dave, I am so excited for you to be here on the show today.
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First things first.
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Welcome to the show.
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Thank you, brian.
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Appreciate it.
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I'm really excited to be here.
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I will say I'm always particularly excited to have fellow New Englanders here on the show, and you and I grew up not too far from each other, so it's really great to have you here.
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But you've got to take us beyond the bio first.
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Who's Dave?
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How'd you start doing all these cool things?
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Yeah, absolutely so.
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Like you said, boston born and raised I was in school and trying to figure out what I wanted to do.
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I really liked the business aspect of things.
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I was undergrad business at BC.
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I started to get way more into technology and started building websites and iPhone apps and then I really learned about data and business intelligence specifically coming out, or really when I was still in school, and kind of went down that path and really haven't left it.
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So I've always looked for things that sort of combine an entrepreneurial passion with being more involved in technology and data, entrepreneurial passion with being more involved in technology and data.
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So that sort of led me down this career path.
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Yeah, I love that overview, dave, especially because you so correctly and quickly point out that it's intertwined with technology, with business.
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All of these things play together, and so that's why, for me having this chance to talk to you today, thinking about my own businesses, thinking about all of our listeners around the world, it's something that we're all confronted with because we probably I would argue we have more data than at any point in history, whether we realize it or not.
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I think that's the beauty of the tech piece of it is that just by doing business whether we just use Stripe, for example, or whether we have Google Analytics installed on our website we unintentionally have so much data at our fingertips.
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Where do we begin to start looking for data in all of our businesses?
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That's a great question and, yeah, it really starts with the basics and I think you can go a really long way with the basics.
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So, you know, in any business you have some sort of revenue coming through, so you have sales volume and you have transactions and you have likely website activity if you have any sort of digital presence.
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So, like you said, you know, one of the first things I recommend doing is looking at the different pieces of software that you're leveraging, the different business processes that you have, and thinking through what data can I be collecting from here and to what you mentioned in the introduction, how do we action on that and how do we actually use some of that to start making different decisions?
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Yeah, and thinking about that, obviously, today, what's going to be most fun for me is that we're going to bring it to real life business examples because, dave, you've done this across all different types of businesses and I'll share this with you so that you can directly speak to our audience.
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Is that a lot of entrepreneurs?
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We convince ourselves that our situation is entirely unique, and someone will hear you talk about data and say, well, yeah, that makes sense for a company that has a bunch of salespeople, or that makes sense for someone that sells physical products, like a Wayfair, for example.
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Of course, they're obsessed with their conversion metrics, but what I appreciate about your work is it transcends industry, it transcends business size, it transcends all of that.
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Talk to us about that universal nature of data and the importance at all different levels of business.
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Yeah, it's my favorite part, to be honest, is going in and getting to work with businesses of different sizes, of completely different industries, and their problems are extremely similar.
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Even though their their business context, even though the actual data that they have can be very different, the core problems persist across industry, across sizes.
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So, you know, one of the biggest things that I see, pretty much regardless of industry, regardless of size, is the need to have a source of truth, a really common data set that helps you understand your core business metrics, and that is something that is a real challenge for businesses that, especially that don't have a full time data staff or have a really small team.
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That is something that's really pervasive.
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But what's really cool is you know you really do see a lot of patterns in terms of what data is being collected or what data should be being collected, what actions should be taken off of data, and you know whether you have a SaaS business where you're looking at, you know, usage of a product and retention, or you have more of a you know physical retail business and you're looking at things closer to website conversion or operations and return rate, you know you start to see a lot of patterns in the data that matters to be collected and the actions you can take off of it.
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So it's really cool when you can start connecting the dots between you know very different industries and very different organization sizes.
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Yeah, take us across that bridge, dave, because this is going to be fun.
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Just, I'm very excited.
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I'll humble brag for you about the awesome client roster that you've got you work with.
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For example, once I saw Scott Baxter's video on your homepage.
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I was just like yes, you are part of what's driving player courts leverage in market penetration across the country, which, for all of us tennis players, that's very exciting, because the more people that use that, the more players that we can find to play with.
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And so, along those lines, whether it's Scott with PlayerCore or whether it's a local web designer, whatever it may be, the goal for so much of business is, of course, growth.
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We want our businesses to have more revenue and, for sure, have a higher profit margin.
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We want to have that net profit at the end of the day.
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How's data the tool through which we get there Connect those two dots for us?
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Absolutely.
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Yeah, I'll go a little bit deeper on Scott and PlayYourCourt, because that was definitely one of my favorite engagements.
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So this is an example.
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So PlayYourCourt, like you mentioned, brian, is a tennis community folks across the country, the largest tennis community in the US and they really have two key parts of their offering.
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One is a membership platform where you can sign up and be matched with fellow tennis enthusiasts kind of like a Tinder matching style and you can find new playing partners.
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And then they also have an area where you can get professional lessons from tennis coaches.
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So for them, there's a couple angles, you know.
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One is really understanding their membership base in a lot of detail.
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They have different offerings in terms of membership quality and what you're getting, membership quality and what you're getting, and really understanding who their subscribers are, how they are being retained and where they're not being retained, and digging into reasons behind that and then really helping on the different aspects of their business operations.
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One area is marketing really understanding the different marketing channels that they are investing in and figuring out where they're getting good return on investment and where they're not getting return on investment.
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And one of the first things that we uncovered was, if we do the math.
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One of the big marketing areas that they're investing in wasn't yielding the results that we were looking for, so we made a conscious choice to make some shifts and ultimately get a lot more return on investment by doing that.
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So you know, again, it kind of speaks to regardless of the industry, regardless of the subject area, there are commonalities around getting smarter about your marketing spend, or really getting better about understanding your, your customers and your customer retention, to really dive in and make critical changes that actually alter your end bottom line.
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Yeah, gosh, I so appreciate the behind the scenes glimpse into a real life project that you've been a part of.
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I think that it's so cool because it's impactful, and when you talk about return on investment just as a consumer and as someone who's been in the industry and seen Scott's growth with PlayRecord, I think it's cool because all of those smarter decisions allow him to reach more people, allow him to grow even more, and so it's a really important function.
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I want to talk about the workflow behind that function.
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How frequently should we, or do your clients, look at their data?
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Is this something that we embed into our weekly workflow and we should have some sort of dashboard or whiteboard on the wall.
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That's constantly what we're looking at every day that we wake up.
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Is it a monthly review of things?
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How do we embed data into the decisions and the workflows that we have?
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Great question.
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Answer is yes and yes.
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So there are certain things that should be looked at really routinely.
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You know whether this is every day when you wake up is one of the first things that you check.
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I get a dashboard of my business's data sent to me first thing in the morning and what I recommend for that is if you have data that is really going to help you make decisions or change the course of your day or your week, those are really the ones that I recommend.
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You know making part of your day to day workflow, and then there are also different metrics and different lenses that are helpful to look at more on a monthly or less regular basis.
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So this is where your you know end of month stats look like.
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You know if you have estimates on to how many leads that you think you were supposed to get in a given month, how much conversion you expected from them, things that are a little bit more lagging in nature.
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Those are really good use cases for things that you check.
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You know, maybe the at the start of the month you kind of look at it as a retrospective look at what happened and then think about how is that going to alter the course of going forward, but I think in terms of things that are more of a daily workflow, it's like your operational day to day.
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Bigger situations you know, things that you would act on during the course of that week are the more important things that you want to look at routinely.
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Yeah, dave, you brought up a very important word and I want you to go deeper there, because you talk about lagging indicators.
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I think about when I was in my early twenties and I started a marketing and SEO agency in central Massachusetts.
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I was going to chamber of commerce events and I was going to BNI events and I was looking at is this rewarding me with revenue?
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And I was making those decisions, but of course, I had to stick at it for a while until it started to yield those referrals.
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Talk to us about that nature of lagging indicators, what that means, how we should gauge them and, of course, the patience element comes in here as well.
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Of course.
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Yeah, lagging indicators are a big part of your data and those are really things that, like you said, brian, really take a while to materialize.
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So things that kind of are good outcomes, such as such as revenue, such as you know, deals being signed those are not things that you necessarily know are going to happen when you take the action.
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You know, when you're going to those conferences, when you're having, you know, five discovery calls with a client, you know you're not measuring the outcome of that because it hasn't happened yet.
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So, the more you know, you definitely will need some of those lagging indicators.
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But, more important, you also need to look at leading indicators, so things that give you an early understanding of how things are going and if you're on the right track you know.
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So in this case, that could be you know, your early part of your lead gen.
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How many of those, how many leads are you talking to?
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How many of them are converting into discovery calls?
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How many of them have you been able to send an initial proposal to?
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Those are things that you can, you know, start to look at, that are earlier in the process and give you a better sense of your health, and you know if you start to see that you don't have the lead volume that you need, you know you can take action to drive more marketing or go to more events If you aren't getting the conversion you need.
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That's where you can start thinking about.
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You know how do I change my sales process?
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Do I need to have more calls, more, more collateral for my prospects?
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So it really helps you dive in and understand.
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You know where you need to improve and, more importantly, gives you the advance notice, because the last thing you want to do is, you know, be looking back on your metrics at the end of the year and you know realizing, oh my God, I missed all my sales targets.
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But if you know earlier on, you know I'm not getting the top line amount of leads that I need to generate my end result sales, then you can take action on that before it's too late.
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Yeah, very well said, Dave.
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It makes me really excited to ask you this question, although it's a bit of a nerdy one, and I trust that you're going to make sense of it for our audience who's listening here today, and it's about I guess I'll put it in the context of with my marketing hat on.
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So when it comes to paid ads, we're always obsessed with attribution tracking, because the problem with today's world is that if I click on an ad let's say I'm visiting my parents and I click on an ad on their computer and they're logged into Facebook and I'm interested in something, and then you know, a week later, because I saw that ad from my phone here in my hometown, I then go to their website and manually do it.
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That's not necessarily going to get tracked.
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It is so hard in the marketing world to master attribution tracking, and so I guess to use that as an example but still dumb it down is that it's hard always to just say this is the metric that we're tracking and relate it or correlate it directly to an intended result.
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How do you make sense of that in data, Because there's so much that's going on that can't necessarily directly be tracked.
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Yeah, that's a fantastic question and attribution might be the hardest thing collectively in data to get right.
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So there's a couple of things.
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One is you have to just be comfortable with the fact that you're not going to have those direct correlations all the time and you start to build more of an intuition around what correlations are really clean and what can you trust, as if this is telling me a certain thing that I can really go with it and not ask any questions.
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But you really have to be critical and skeptical of anything in your data environment, and there are some things more so than others, and you have to supplement the data with your intuition to your.
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The Facebook example is a great one.
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If you go off of an application where you requiring someone to click on an ad and then purchase the item, you know you're missing out on anybody who view that ad didn't click, but it became top of mind and then went and and made a purchase.
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So you know, just knowing that, you have to kind of go under with the assumption of well, I know that my actual contribution is going to be higher than what my data is showing.
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So I need to take this with a grain of salt, or you know, for an attribution situation you can do something called a holdout, which means you know you don't show ads to a group of people or a specific segment, and then you understand, you know what the true sales volume difference is between the people who got the ads versus those who didn't, to help get you a better measure of what your you know your attribution actually is.
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But it kind of goes back on a broader level to you have to always be skeptical of your data and you really shouldn't take anything at face value.
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You should always be questioning.
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You know, what are the nuances to this data?
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What are the assumptions that make this not a clear cut answer?
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So you know always approaching data with a healthy degree of skepticism.
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Yeah, I love that, dave.
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Maybe not the advice that people expect to hear from someone who works in data and business intelligence and analytics like you do, but it's cool to see because, again, that skepticism that healthy skepticism also allows you to bake in some intuition.
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You brought that up, which I think is such a powerful force to ultimately just continue making better decisions, so I love the fact that you consider that there as well.
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I want to broaden the conversation a little bit, though, because I feel like way too frequently in business conversations we get very ROI focused, and here you and I are.
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I mean, I'm a marketer at heart, so I will frequently hijack the conversation and talk about advertising and marketing, but I also recognize that many things aren't done for the end goal of necessarily ROI.
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Goodwill is a real thing.
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Brand awareness is a real thing.
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That brand awareness may not result in anything for us for a year or three years or 10 years.
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Talk to us about those other outcomes that we can positively identify thanks to using data that may not reflect when it comes to measuring ROI.
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It's a great question.
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So, yeah, there are a lot of different things that you can affect that.
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Yeah, there are a lot of different things that you can affect that.
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Whether it's a lag before you'd see any real you know revenue, output or you know are basically impossible to correlate.
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That still, instinctively, you know brand awareness or things like net promoter score is a big one where you know that if you have customers that are happy and, you know, really speak to your product, that you have a better chance of retaining them and you have a better chance of them leaving reviews or telling their friends, which spirals into into more business.
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So the you know, the biggest advice I can give is you know, stick to the things that you that are no brainers in terms of you want to improve, even if you can't make a clear connection between that and and revenue.
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You know you, you absolutely know that a higher NPS or more brand awareness is going to lead to those outcomes, even if you can't quantify it.
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So you absolutely should be making moves to go in that direction, even if you don't know.
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You know I raised NPS by five points.
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I should expect 100k additional revenue.
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You don't know that, but you do know that you know the more you increase, naturally the more business you're going to do.
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So you should take the efforts to make those moves and you know, for any of those metrics that you know may not tie directly to the bottom line, the advice, just like any other metric, is get into into the next level of detail.
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So understand, you know the breakdown of.
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You know I'm kind of harping on NPS because I think it's a really important one, but you know, understand, you know how that is divided between your, your newer customers and your repeat customers.
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Understand how that's partitioned by things like your different products and and the reasons behind that and where you're falling short.
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Because you know you really need to dig into a second or third level of data to figure out what aspects you really need to focus on improving to up-level the entire metric.
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Yeah, Dave, you are harping on Net Promoter Score and I will tell you this in over a thousand episodes, you are the first ever guest to talk about Net Promoter Score, so now I'm going to invite you to talk about it even more.
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What is Net Promoter Score?
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Why is that so important that that's the metric?
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You jumped straight there with the answer to that question.
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What the heck is it?
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Yeah, net promoter score is kind of the most common way of measuring customer satisfaction.
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So it's taken by asking a really simple question of your customers or clients, which is on a scale of zero to 10, how likely would you be to recommend this product or service to a friend or colleague?
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So a really simple way of asking and the calculation behind it is a little bit more confusing than it has to be.
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But in in short, you know the higher the better.
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You know, and ideally you want something kind of closer to the 50s or 60s or even 70s.
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But it is very dependent on industry, you know, and there's also a lot of bias between you know people who have bad experiences are going to be more likely to talk about it.
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So you have to take it with a grain of salt.
00:21:14.278 --> 00:21:27.627
But directionally, the higher the better and the more likely you are to, you know, to get positive feedback on review sites and the more likely someone is going to be to refer you to their friends and family.
00:21:27.627 --> 00:21:41.991
So you know higher NPS is a really good indicator that you know things are going well, that your customers are happy, and even if you can't necessarily tie that to revenue, it's the one of the cleanest ways that you can measure customer satisfaction.
00:21:42.593 --> 00:21:47.598
Yeah, I love the fact that we're going here in today's conversation because it's something I hadn't even thought to ask you.
00:21:47.598 --> 00:21:56.810
But the more we're talking about this, I'm thinking that this is a great example in the fact that we're only going to obtain this data if we ask for it.
00:21:56.810 --> 00:22:04.686
So a lot of data we inherently have in our businesses we've talked about quite a few of those things today, but something as important as net promoter score you just revealed.
00:22:04.727 --> 00:22:09.501
The only way to get it is to email your customer base and say how likely are you to recommend us to a friend?
00:22:09.501 --> 00:22:19.317
And so what are other examples of either things we should be doing or questions we should be asking that will yield more valuable data for our businesses?
00:22:20.160 --> 00:22:28.838
Yeah, I think in general, just the more you can ask your customers, the better, and basic surveys really go a long way.
00:22:28.838 --> 00:22:40.236
I think one of the themes that I really believe in and that I think we're covering today is you can get a long way with very basic data, even in this age of AI and really a lot of exciting developments.
00:22:40.236 --> 00:22:43.853
It's the basics that can yield a lot of exciting developments like it's the basics that can yield a lot of information.
00:22:43.853 --> 00:23:10.665
So simply asking your, your customers, things like you know what are, what are the things that you you like about our product, what are the things that you don't, or what are the opportunity areas, and just grouping those answers and figuring out what the themes are in that can go a long way to informing you know your product roadmap or things that you might not be as aware of, just by looking at the data that you know the business is naturally collecting.
00:23:24.309 --> 00:23:41.192
A way where you can get synthesizable data to take like really clear actions is just something that I think a lot of companies leave on the table, which also makes me realize that we're this deep into the conversation and I feel like we've had such a strong bias towards quantitative data, but now you're talking about surveys, dave, how much do you value qualitative data and how the heck do we make sense of it?
00:23:42.314 --> 00:23:50.065
Yeah, you know, it's definitely valuable and, again, I think that's something we overlook a lot because, yeah, that's a really good program.
00:23:50.065 --> 00:23:56.955
Brian, I think when you think of data, we kind of jump to quantitative and just very easily digestible.
00:23:56.955 --> 00:23:59.519
But there's a lot of qualitative data out there.
00:23:59.519 --> 00:24:08.097
So, yeah, I think it matters a lot, but it's the most impactful if you can turn that into digestible insights.
00:24:08.097 --> 00:24:23.977
You know, it's one thing if you, you know, are going through and trying to read 200, 300 different survey responses or listen to, you know, hundreds of customer calls, which is super valuable to do, but ultimately it's the synthesis of that that matters.
00:24:24.817 --> 00:24:27.423
So, you know, there's a couple of methods that you can take.
00:24:27.423 --> 00:24:36.997
You know, in terms of surveying, you know you can have qualitative feedback but supplement it with categorization of you know, can you rank?
00:24:36.997 --> 00:24:41.313
Would you please rank the best parts of our product or the things that you would like to improve?
00:24:41.313 --> 00:24:45.079
That's something that you can really easily take and group and figure out.
00:24:45.079 --> 00:24:47.163
You know what are the key actionable steps.
00:24:47.163 --> 00:25:01.144
The other thing you can do is, you know, feed it into an AI tool or something of that capacity and you can cluster responses and say you know what are the different themes that are coming out of all of this qualitative feedback.
00:25:01.144 --> 00:25:16.750
So there, you know, there are definitely a lot of ways to kind of take that and turn it into something more quantitative, and that's that's really recommended in order to kind of group it and see, you know, what are the most common pieces of qualitative feedback or qualitative insights that we can get.
00:25:16.892 --> 00:25:24.699
You know, looking at all this less structured data, yeah, dave, you went there, so I'm going to follow you there and very much push you into this part of the conversation.
00:25:24.699 --> 00:25:26.277
Of course, it's about AI.
00:25:26.277 --> 00:25:30.877
Obviously, it is making our lives easier in so many ways that we're all grateful for.
00:25:30.877 --> 00:25:35.619
You just gave us a tangible example of dump all those survey responses into AI.
00:25:35.619 --> 00:25:39.040
Let it begin to make sense of it and find those patterns.
00:25:39.040 --> 00:25:45.817
What are some other ways that AI is changing the game when it comes to data and making sense of it and turning that data into decisions?
00:25:47.112 --> 00:25:47.915
It's a great question.
00:25:47.915 --> 00:25:51.359
I mean, there are so many different things that you can do with AI.
00:25:51.359 --> 00:26:08.121
I think, from my standpoint, one of the most useful things is actually helping me build data solutions, and it can take a lot of the work off of our plate and actually, you know, building the tools and building the data sets that are ultimately going to be useful.
00:26:08.121 --> 00:26:11.656
I think that is sort of an underrated aspect of it.
00:26:11.656 --> 00:26:19.817
One of the really cool things that it can do also is produce really amazing synthetic data sets, meaning sample data.
00:26:19.817 --> 00:26:28.530
So, you know, in my case, when I'm speaking with potential clients, I really want to show them the capabilities of what we can do and what we can build.
00:26:29.713 --> 00:26:33.442
It's one thing to, you know, talk about password that we've done for another client.
00:26:33.442 --> 00:26:41.920
It's another thing to build them a mockup dashboard and in two hours, that, you know, shows them the capability of what we could do if we focus on their business.
00:26:41.920 --> 00:26:57.958
And AI can really help with that because you can give it a couple of prompts and our good friend Claude can spit out a bunch of different logic that will essentially give you a script, you can run it and it builds a sample data set for you.
00:26:57.958 --> 00:27:05.717
So one of the really cool applications of AI is actually making the data development lifecycle a lot faster.
00:27:05.717 --> 00:27:08.262
So and that's just on.
00:27:08.262 --> 00:27:11.637
You know the development of data side, that's not even speaking about.
00:27:11.637 --> 00:27:17.299
You know ways that you can interact with with data and have AI synthesize and make sense of it.
00:27:17.299 --> 00:27:20.634
You know where there's a lot of opportunity in that regard.
00:27:20.634 --> 00:27:22.070
That, I think, is just you know.
00:27:22.070 --> 00:27:23.196
We're just scratching the surface on.
00:27:23.759 --> 00:27:25.247
Yeah, that's very cool, Gosh.
00:27:25.247 --> 00:27:29.439
Even just hearing you talk about example data sets it takes me back to college.
00:27:29.439 --> 00:27:32.775
How frequently do I wish that we had those types of things, Because we were.
00:27:32.775 --> 00:27:39.882
I mean, I don't know what you were doing at BC, but at Bentley, right down the road from you, we were doing mock business plans and all of that.
00:27:42.109 --> 00:27:47.439
And we just had to make up businesses with our imagination, it would have been so much easier to have customer avatars and everything fully fleshed out using AI.
00:27:47.439 --> 00:27:59.537
So very cool to hear the way that you are in real life integrating that into your own workflows and, I'm sure, even just shifting a little bit of gears and talking to you, entrepreneur to entrepreneur, it's helping in your sales process.
00:27:59.537 --> 00:28:03.984
I'm sure People can actually see it and imagine it when you're talking to them about the powers of it.
00:28:03.984 --> 00:28:07.377
So very cool, very grateful for you sharing that transparently with us.
00:28:07.377 --> 00:28:14.460
Dave, I want to put you on the spot here because I called it out very early on in the intro to this episode about data environments.
00:28:14.519 --> 00:28:19.715
A lot of people will be thinking about their own businesses and all the different tools that they have.
00:28:19.715 --> 00:28:20.596
I've called out.
00:28:20.596 --> 00:28:28.115
Stripe, for example, is a very common payment processor across our audience, and Google Analytics is obviously a great web analytics tool and it's free.
00:28:28.115 --> 00:28:30.020
Everyone should have it installed on their website.
00:28:30.020 --> 00:28:31.633
Quick plug to all of you listeners.
00:28:31.633 --> 00:28:37.894
But, dave, what are some of those common places that people can find the data in their businesses?
00:28:37.894 --> 00:28:43.113
And then, what's a data environment so that we can centralize and access all of this data?
