Free Trial
EPISODE #12 | May 26, 2022

Cookies are Dead! Long Live Geospatial and Predictive Analytics!

00:36:48 minutes

Listen Now

Play Pause
Subscribe on: Itunes | Spotify | Blubrry

Sign up to be notified when new episodes are posted!

[Full Transcript Available Below]
SiliconExpert Podcast Episode 12 with Ken Sheehan of KnoWhere, LLC - Transcription

Host: Eric Singer

Producer/Director: George Karalias

[00:00:00] Ken: If you're like most companies in the U S you're so busy running the company that you can't stop and think about how you deal with the change that's coming down the pike tomorrow. And many companies will simply be like, what do I do? They don't have that billion-dollar reach to actually put a development team and staff of scientists in place.

[00:00:22] And I think what's interesting about that is that even when they do find the solution, They're not going to disperse that out to the general populace and say, here's a tool that you can use, young business to grow. They're going to keep that to themselves. And I, but the reality of it is my view personally, and part of why I founded KnoWhere is that I wanted to put the power of some of that analysis into the hands of the rest of that population of businesses.

[00:00:51] Eric: Welcome to the Intelligent Engine, a podcast that lives in the heart of the electronics industry brought to you by SiliconExpert. SiliconExpert is all about data driven decisions. With a human driven experience. We mitigate risk and manage compliance from design, through sustainment, the knowledge experience, and thought leadership of the team partners and those we interact with every day expose unique aspects of the electronics industry and the product life cycles that live with.

[00:01:18] These are the stories that fuel the Intelligent Engine.

[00:01:25] Today's spotlight is on KnoWhere, LLC. KnoWhere is a company specializing in actionable geospatial and business intelligence and business ecosystem modeling. Joining us today is Ken Sheehan. Ken received his Ms and PhD from West Virginia university, where he specialized in environmental ecosystem, modeling and prediction.

[00:01:48] He finished his postdoc at the university of New Hampshire after which he worked for several years for the U S GS before realizing there was an unmet niche within the rapidly changing marketing and advertising landscape for KnoWhere services and his skillset in the corporate world, specifically in that marketing and advertising area.

[00:02:10] Ken, thanks so much for joining us today. Thank

[00:02:12] Ken: you for having me. Hearing you introduce me. That's quite a, quite a mouthful,

[00:02:16] Eric: but there's a lot of acronyms on in there and that's as good a place to start as any, tell me a little bit about what you did in your postdoc work and, and then what you did for the us geological survey.

[00:02:28] Ken: Sure. And I think what's fun to me about this is people are probably asking how did can, or what's the value of transitioning from this ecosystem modeling, which is what I did for instance, at, you know, West Virginia university, which was through the U S GS as well, and then also at my postdoc at UNH. And so the ecosystem now the physical ecosystem is a big thing with a lot of different drivers to different patterns that are developing within that ecosystem, depending on how you're looking at it. And so the statistics for all of that are exactly the same statistics that can be utilized on any data and any system. And so it's, my background is in that sort of big ecosystem modeling world, I worked on national science foundation grants for the university of New Hampshire, got to travel around to some amazing places, but you know what, there's a lot more data in the human ecosystem. So realizing that, and that it wasn't being leveraged in the same way. A lot of times academia has a difficult time moving into the corporate world.

[00:03:32] And so there's a lot of sort of delay and it could be decades. And so there are some really interesting things going on out there that are applicable to the corporate world. And I said, you know what, let's try and put them there.

[00:03:43] Eric: Yeah, that you touched on something I find really interesting there about there's so much more data about the human ecosystem.

[00:03:51] Obviously we've studied ourselves more than anybody else, but I'm curious a step back up from that. When you talk about studying ecosystem modeling, are we talking about. The planet Earth's ecosystem or Microsystems. What can you tell us just a little specifically about what those systems were that you studied?

[00:04:13] Ken: Sure.

[00:04:13] We're talking about scale. What you mentioned is scale. And so there's the broad scale, which would be, say a global scale or say a continental scale or even regional. And then there's that micro scale, which you mentioned. So what's actually happening in a specific reach of stream, which I cut my teeth and all my statistics and looking at and predicting habitat and why things are and organisms where specifically fish in a stream in a given location, statistics related to that.

[00:04:40] So really it can be very local. And so in, in my prior world of ecosystem modeling, it was within a stream or a catchment of that stream. And in my current world, it could be in a neighborhood or it could be the catchment of a store, which is a new term that's emerging, merging those, that world of. The ecosystem environmental versus the business ecosystem.

[00:05:01] So for instance, if you're trying to draw in your customers for a given store, it has its own catchment. Like where are they coming from? How are they flowing into the store for a purchase, the

[00:05:11] Eric: drainage basin? Yes.

[00:05:13] Ken: I'm amazed that you just mentioned those words. Well done.

[00:05:17] Eric: I love that idea that visual of you've got this area from which you can draw customers.

[00:05:22] And how do they funnel down into your catchment. Really interesting. The, the complexity of the natural ecosystem seems just we use these incredibly complex computer modeling to predict climate models and things like that. How does it compare when you talk about something as relatively micro as looking at a stream and the fish within it.

[00:05:51] How does the complexity of that environment compare to say a business environment that you might be studying and helping to optimize?

[00:05:59] Ken: So I look at any system really, it's almost a misnomer saying that the human ecosystem is unnatural. So that's a big philosophical debate, but considering that we're all on planet earth, and we're all operating within this, the sphere of the globe. I'll consider us for this conversation, part of that natural ecosystem. And so that being the case, we operate within our own set of complexities and laws and boundaries and things that drive our behavior and patterns within them as well. In terms of complexity. Sometimes I view it as that Mandelbrot set, right?

[00:06:30] If you've ever gone onto YouTube and you search Mandelbrot equation, and it comes up with those really crazy or neat things that no matter how much you zoom in or away, it's like the same pattern. You can really zone out on it. If you were in your dorm room in college. And sometimes I view the, the complexity that we look at is that meaning anytime you look at it, whatever scale, there are a lot of complexities going on at any given point.

[00:06:50] However, if we're looking at the business ecosystem and also the physical ecosystem, you want to break it up into channels. And so that channel could be Facebook or it could be tick-tock, it could be your event schedule and in the natural world, it could be you break it up into rainfall or proximity to a stream bank or a Boulder or flow rate within a river.

[00:07:14] And so it's all you break it up and parse it as discreetly as possible, and then understand the interplay of all those variables. And I think that gets at the crux of it, right when we look at this, whether it's a physical ecosystem, like out in the world, you're taking a beautiful hike and how all of those things interact to create that landscape.

[00:07:32] We're also looking at the business ecosystem landscape as a whole and how all those different parts interplay impact one another and then create that ultimate result. And for many CEOs out there for a lot of companies it's how does that drive sales or how does that drive interest in what I'm doing?

[00:07:48] And you can, you just have to know what question you're asking in terms of how. Uh, look at the interplay of those variables.

[00:07:54] Eric: Is the business ecosystem, in this example, more or less unpredictable than the natural one?

[00:08:02] Ken: I would say that if it was entirely unpredictable, it would be complete entropy, which is zero ability to predict anything, all every single bit of statistics, for the most part, I would say the majority can never say a hundred percent, is that we leverage patterns to understand what's going on in machine learning in a lot of the upcoming statistics that are bridging this gap between the statistics of old and the upcoming sort of black box statistics of Google algorithms, et cetera, and machine learning and artificial intelligence.

[00:08:34] They're all based on these different rule sets and parameters. And you have to understand the variation, everything within them.

[00:08:41] Eric: I want to talk a little bit more about patterns, because that seems so key the identification of those patterns. And then going back to your positioning statement, you specifically talk about actionable intelligence.

[00:08:58] So let's talk about how you use patterns to recognize something and then do something about.

[00:09:05] Ken: Sure. Let's put it into a way that, that the listener or anybody can visualize like ability to visualize your data and understand what's going on is really important as a side note, but let's sort of paint a picture.

[00:09:16] I think maybe that's important. You know where for instance, you live and we'll talk just general demographics. Everyone in business world is used to talking about demographics. The new census just came out. So when you look at that and you just go and Google it and you decide to look at say income distribution or where certain home values are located. There are patterns there, right? And so those are leverageable pattern in our daily lives. And so if, for instance, you are, and this is an exact example that I recently worked on for a roofing company. And the question at hand was where is it that I will be most likely to do a few things.

[00:09:54] One is what's my competition doing? What's the competitive landscape to where is there a potential opportunity for me? And as it were, I've been doing business. So one, that's a predictive question. Meaning, Can you tell us where to go in the future and then what have we been doing in the past? And so you can start parsing all of that apart, and you can ask that question and the key to answering it is identifying patterns.

[00:10:18] So for instance, if there is a pattern of. We pull all your historical data, by the way, a company's data is a gold mine. If you're not collecting data appropriately, then you're really missing out on future opportunity and efficiencies, whether that's bringing in clients or whatever the case is, or just managing the company, make sure to collect your data.

[00:10:39] Um, but in terms of back to the patterns, we can analyze all of that from a geospatial angle. From we can leverage typical regression statistics, a whole variety of different aspects of things. Um, I love for instance, a key, you can use information criteria and if anyone wants to look that up still very useful and it's just model selection.

[00:10:59] And essentially what we want to do is identify patterns within data and leverage those patterns and then even find out more. So it's all about, it's a numbers game, Eric. And I think that in this case, we want to lower the numbers of people or the amount of money that we spend to bring in a new client.

[00:11:17] It's a, an exercise in efficiency pattern allows us to do that. So, you know, I'll ask you a question about your home. Do you live in a neighborhood of similar homes.

[00:11:25] Eric: Yes.

[00:11:26] Ken: And so that probably has a certain demographic, like your neighbors are somewhat similar to you in a certain way. Maybe they have a certain level of education or success.

[00:11:35] Is that kind of the case?

[00:11:37] Eric: I would say absolutely.

[00:11:39] Ken: Okay. And so that is a leverageable pattern at its very simplest, right? And so if you take that to the nth level and you add in say several dozen variables into a model. You can actually get a really strong understanding of exactly how, and here's a new word I'll introduce when things will be most opportunistic for you to make a decision and how to make that decision.

[00:12:01] A lot of companies will say, well, I'm expanding. I'm really growing. Where do I open that new brick and mortar store that equals the success that I had in a prior one. This is a whole separate field in and of itself. But it's predictive modeling and that's the suitability model. Where is it most suitable for me to open that new store?

[00:12:17] And we look at things from that perspective, right. And I'm going to take one step back for a moment and say, simply, this is that again. It is that ecosystem, right? I think a lot of folks and marketing agencies, ad agencies, even companies, they parse their departments so separately. There's that classic interplay.

[00:12:34] The sales team says the leads really stunk this month. Whereas marketing says, you know what? You had a 10% increase in phone calls, so that's amazing. And the reality is that the sales team that works anecdotally and maybe makes 10 phone calls a week, if they make 11, and then close one more sale. It's not a big deal to them.

[00:12:51] They probably won't notice. And that's where you have to get all these sort of departments and things talking, and really look at them appropriately. And that's very atypical. And that's that ecosystem approach, which I'm mentioning

[00:13:02] Eric: the example that you give about. The patterns that emerge when we're, uh, grouping things in physical space, brings me back to your background where I'm imagining correct me if I'm wrong, but I'm imagining that you were probably working with some robust GIS systems when you were studying and afterwards, and I, I wonder if that's the case, did that factor into how you, you made this jump from environmental ecosystem modeling to the business world.

[00:13:34] Ken: Clearly you've done your homework. That is exactly correct. So we use a variety of different GIS platforms, whereas there it's the dominant one, which would be esreys RGIS platform, also Q GIS has its benefits in certain areas. We also do a lot of our processing in our, we do a lot of Python scripting, and we even use Google CoLab to automate those things through GitHub.

[00:13:57] It's a full suite of variety, but yes, it goes back to that GIS sort of world. And a lot of what you initially mentioned, like these weather and these crazy predictive things, like how do they predict the weather a week out that's often. And even if you look on NOAH's site, you'll see a little credit on there that says esri ESRI, which is one of the main platforms that I work in.

[00:14:17] So yeah, it's exactly correct.

[00:14:19] Tell me a little bit more about what it took to make that leap for you personally, moving from the environmental ecosystem, modeling to the business

[00:14:28] world. Here's where it gets real. This is that personal aspect, right? And that there, there comes a time in everyone's career where they're moving along, they're doing the right thing.

[00:14:38] Enjoying some successes, but there isn't a full match of one skill set to the tasks at hand. And that was essentially what was occurring for me and my wife and I, we really loved New Hampshire. Let's make the jump I was on. What's called a term with the U S GS. And that was coming to a close meaning that the funding was running out a lot of it's grant related in the federal government.

[00:15:01] And so my grant was running out essentially. And we said, you know what, let's move back to New Hampshire. I am going to start a company. So I spent about a year figuring out of the potential needs in the business community. I went to every BNI and other networking that you could think that you could imagine. And out of that emerged an ability to use every aspect of what I had done in my schooling and ecosystem modeling, and then spent essentially.

[00:15:28] The next year, developing those systems so that we could deliver those products. And that's where we are now. What's interesting is that the diversity of companies that need these types of services really range in size from small to large, if you're the new startup. And I'll mention a startup that I work with, they are, they produce a what's the easiest way of saying it.

[00:15:50] They produce actually, it's interesting. It's a GPS oriented product. Um, it launched, I guess, in July of last year, doing exceptionally well and the goal was to ramp up the sales and understand the different channels. And when I say channels here, they are putting an enormous sum of money, you know, millions of dollars into advertising and marketing efforts.

[00:16:12] So the question is how are those tracking, which of those are being more productive, producing, better results, building our building, the overall quality of audience that lead to future sales and growth. And how can we track that against other aspects of the company and make decisions such as inventory like supply chains, big issues right now?

[00:16:31] Eric: Yeah. Okay. So this is the trillion dollar question. Every company on earth is trying to get to the bottom of this.

[00:16:38] Ken: Yes. There are a lot of aspects that we solve even hyper targeting, which is. The initial lead in of the, the, the podcast that was initially developed because of upcoming privacy laws and changes that every company is up against, right?

[00:16:53] In this cookieless world and this behind the scenes world where it's Google or it's Facebook, or some other company controls the ability to target your ads, how do you in those worlds still compete and get effective a result. And so this is one of the solutions to that where we put it in the hands of the company with their own data.

[00:17:13] And then can just implement that within those systems effectively.

[00:17:17] Eric: So your timing here is amazing being in the market at, in this era, as we move into the post cookie world and every advertising agency and CMO in the country is freaking out about what do we do when we don't have cookies anymore? How are we going to track things?

[00:17:35] When did you start the company?

[00:17:38] Ken: Yeah. Great question. So I think we incorporated in 2018 and we've been growing, you know, insanely ever since.

[00:17:45] Eric: So, when we were talking about the disappearance of cookies, you talked about the critical importance for companies to collect their own data. What data is the most important for companies to be tracking?

[00:18:02] Ken: I mean, it's sort of a loaded question. And the, and by saying that I would say that all data is important to some degree. However, there are certain nuggets of information, and I would say, You know, if you've ever heard of the concept value of information theory, which is a whole other sort of realm of scientific study, which has certain bits of information.

[00:18:22] If I'm paraphrasing this correctly are more valuable than others. And that's when you say what data is important. Some companies that answer will be there, their sales data and their sales data related to building their audience appropriate understanding, right? There's the aspirational and the actual audience.

[00:18:39] A lot of times, companies think they're getting one and are actually dealing with the other and you need to align those. Um, but in terms of actual types of data, it really runs the gamut. It could be your historical sales data. It could be just client emotional response to things. It could be information such as just, if we have an address of where, like in a zip code, most of your clients are coming from, we can append that to so many other sources of data.

[00:19:05] And I think I'll mention a key concept, which is atypical data, is that everyone's used to talking about census and income and education or age or gender all extremely important, but sometimes the best insight can be gained from hidden variables, data and information that are combined and data that is maybe not typically looked at, there are companies out there, I think like Orkin or whatever the case is.

[00:19:30] And this was a company in Massachusetts, just north of Boston, essentially. They were shutting down and they said, Ken we're, this was back in whenever the pandemic started, it feels like a lifetime ago. And they said, okay, exactly. And so the, the question or the task before me was, we're going to, we want to spend less money on advertising because we're under this new pandemic.

[00:19:56] We don't know what's going to happen, but we still need to generate business. And so I said, okay, great. What I need is I need your historical data. We will remove all identifying aspects of that. And we put it into a GIS, and we did some spatial analysis on that. And it turned out what we pulled in, for instance were environmental variables and some of those environmental variables were so good at predicting where his clients were, that we then used that to identify all other areas in the region of the state that they wanted to do business that matched those prior success areas. And that was based on what

[00:20:35] Eric: What are some of those factors? Proximity to a pond nearby or something.

[00:20:40] Is it when you say environmental factors is that literally the physical environment,

[00:20:44] Ken: Literally, it is the case and it is much more in-depth and granular than a pond. So we would pull in, for instance, data related to soil moisture content. We would link that up to land use. Such as is that in proximity to farming?

[00:20:59] Is it in proximity to an open space land use area? Is it associated with a certain size of yard? And then once you link that up with information such as their typical job cost for the people that are where they were validating the fact that yes, they need to earn a certain minimum amount of money before they're spending money on this service.

[00:21:19] We probably don't want apartment buildings or we would go after the management companies in that case. But at any rate, we created such a strong suitability model that they actually generated the most leads ever in the middle of the pandemic. And typically during that month of the year, which they have seasonality, they would spend a budget of $20,000 a month during that timeframe in advertising and so we were able to reduce that to about $6,000, but they actually increased and had jobs to work on to where they cut off the advertising After one month. Didn't have to turn it on for three more months.

[00:21:55] So they actually saved. Enough to buy a new vehicle to service more clients from that effort. And that didn't include the increase in business that was just the ad savings. And that is when you have married the geospatial aspect of targeting or removed your dependence on Google or Facebook, which is still important.

[00:22:13] They do an amazing job, but even as Google says on its own website, You can actually target your company the best. So we give you the capability to do that. So in this current world and environment, the more that you can leverage your own data to custom tailor how you're approaching your client the better.

[00:22:31] And in this case, it paid huge dividends.

[00:22:34] Eric: That's just absolutely eons beyond the kind of insights that they would get. Let's say if that company had worked with a traditional marketing and advertising agency, sure. You're going to have a strategist. Who's probably really smart and doing some audience insight, things like focus groups and analyzing whatever, uh, data they have their insights from Google ads.

[00:22:59] Maybe it's something more sophisticated, but what you guys are doing. Is that's a game changer for that business. Yeah. I mean that, that's the entirety of their marketing strategy. I would imagine if you really know that granular of a level who, where is going to be likely to need our services, that man that's hyper targeting beyond the wildest dreams of even a cookie laden scenario, you know, and regards to the concerns about privacy and data collection. I wonder if the model that you all use is actually less concerning than some of the more traditional tactics or traditional in the digital age anyway, that that marketing agencies use because you're using more general and more publicly available data that, that isn't necessarily about an individual. It's about, again, those patterns in those areas and being able to focus on physical areas rather than maybe a psychographic approach where you're targeting someone who has interests in gardening and is a male between 25 and 30 years old. You know, these kinds of things, it feels much more as, as you said about recognizing patterns that you can do something about.

[00:24:27] Ken: I mean, I think those are great points. All of that information that you just mentioned extremely valuable to a company to building an audience profile. And I don't think that will ever fall by the wayside and for there will be a large proponent grouping of individuals that don't opt out of any of this, and you can still target them individually based on whatever the laws end up settling in at, although I'm sure they'll constantly be in flux, but you're right. It's that if you're just in a crowd or let's say you go to a baseball game and someone shows you an ad, right. And that ad is more geared towards you because of an understanding of people that typically visit baseball games.

[00:25:05] That's not privacy invasive. That's never looking at you individually. That's really not doing anything that you would be offended at. But if for instance, you're sitting there at the same baseball game and you get a text message that says, Hey, Bob, you should buy this while you're at the game. That's where it gets a little bit creepy and people are pushing back against.

[00:25:22] And I think there's that happy medium between the two. And that's what you're saying.

[00:25:27] Eric: Yeah. The example that you just gave us about the pest control company is a great one because it's something that we can all very easily wrap our heads around. When I imagine you working with a more enterprise level client, I am picturing massive amounts of data that's probably all over the place. Poorly formatted, just a nightmare of disparate sources that you have to parse and figure out what's relevant. Are you using machine learning or AI to get through that or any other part of your discovery process?

[00:26:06] Ken: We are actually, we use machine learning and different realms and tools within that universe to actually look at our data appropriately.

[00:26:15] And the example would be if you were to do a word cloud analysis, which even pops up in like Salesforce and maybe HubSpot and some of these systems, and essentially what they're doing is they're applying, they're applying value to words so that you can then analyze them. And in that case, it's important and machine learning, and it's often sort of the way we use it as well.

[00:26:35] Is that. Just want to really parse and define to use, you know, a word that you just used a moment ago, but the data and do it effectively, but you can't typically sift through, by hand a million records, right? Like at the enterprise level. And this has happened multiple times where even in a given month, there'd be millions of records to go through.

[00:26:54] So you essentially have to subset that data appropriately. Or if you are working through the full data set, you really need to understand the different forms that, that, that data is taking. Whether it's. Even the fact of a header, what data is classified as often differs between different data sets and data types that are collected.

[00:27:13] And getting those all to agree is something that we spend quite a bit of time doing. And then once that occurs, you can get it all to work in concert, like very frequently. We'll take Salesforce data, HubSpot data, other atypical data. We will take environmental data. We will take census data. We will mix that with the government permitting date.

[00:27:31] That's public. We will, whatever it really, I think you get the idea. We take a very consultative approach. That's specific to that client. Not that it's not systematized because it is, but there's a little artistry in terms of what you pull in and doing a little due diligence on every single situation.

[00:27:48] Eric: No question, not a pure science equation here.

[00:27:51] A little artistry and human intuition has gotta be required. Yeah. Tying into that, the, this idea of massive volumes of data at some of these larger companies. Can you talk a little bit about what it's like working with a, with an enterprise level client in your business?

[00:28:10] Ken: It's actually really fun. This is where I think I've had the most exposure to the most clients quickly in that even at that high level there is often a lack of leveraging data appropriately, or for instance, they just skip over that, meaning that everything is so sales driven and they're so pervasive out there that they often fail to recognize the true value of the data that they're sitting on. And I'm always surprised at that, meaning that even billion dollar companies that I've been exposed to.

[00:28:40] There the data that they're collecting often as in different departments and sits in different locations and that's not often talking. So the true insight comes when you get those all coming together in a little more appropriate fashion. If you're like most companies in the U S you're so busy running the company that you can't stop and think about how you deal with the change that's coming down the pike tomorrow, and many companies will simply be like, what do I do.

[00:29:05] They don't have that billion dollar reach to actually put a development team and staff of scientists in place. And I think what's interesting about that is that even when they do find the solution, they're not going to disperse that out to the general populace and say, here's a tool that you can use, young business to grow they're keeping that to themselves.

[00:29:25] But the reality of it is. Uh, my view personally, and part of why I found it KnoWhere is that I wanted to put the power of some of that analysis into the hands of the rest of that population of, of businesses.

[00:29:37] Eric: Yeah. Are you working yet with any advertising agencies? That's a great question. I say yet, because I have a feeling you will be if you're not already.

[00:29:48] Ken: Probably not surprising to you, is that one of the numerically, most frequent clients that I deal with are other advertising agencies and they are hiring me, because they, again, it takes a good amount of money and infrastructure to develop and schooling, to develop the skillset, to really make this data sing and make decisions. Really what data should be there.

[00:30:11] Eric: Is there a particular advertising or marketing agency relationship that you can talk a little bit about and how that develops and what you've been able to provide for them?

[00:30:23] Ken: Yes, Zozimus agency out of Boston was one of my initial partners and they immediately saw the value of what I was doing and actually working with them has been very synergistic. So we've been able to use some of my advanced geo statistical methodology to great impact for some of their clients that were really during the pandemic in an industry that was experiencing some downturns, turning that around and understanding how we were able to do so without raising the budget.

[00:30:54] And Nick was Nick Lowe of Zozimus was very forward-thinking as was David Wilson at Zozimus. So of seeing the potential impact of this and willing to put it into play with some of their clients. And it's been really a joy working with them.

[00:31:08] Eric: Yeah, and I bet their clients are thrilled because this is certainly not anywhere near the level of detailed insights that clients are used to getting from typical ad agencies.

[00:31:22] You get things like focus, group results and Google analytics. We're in a completely different ball game here.

[00:31:30] Ken: There is a lot of garbage data and the same goes, which I'll use in a minute is garbage in garbage out. Right? So you want as high quality data as possible. Part of our process is always identifying the appropriateness and quality of the data.

[00:31:44] That's pretty much step number one, after discovery and during discovery is what data is available. What channels are there? What is the quality of that data? And a huge one is Salesforce. You mentioned companies spend an enormous amount of money getting Salesforce, HubSpot, you name it up and running. And then even though Salesforce does have ability to service and you can work with them on customization, etc.

[00:32:08] A lot of times, people that are then at the company, putting data in such as the sales team are really messing with that data so that it's no longer standardized or in a way that can be used effectively. So one data practices have to be established and adhere to at least to some degree. And then secondarily, it's really important that everyone agrees what things mean and then just stick to that plan. We often guide companies in that as well, right? Like you're not even collecting the right data. What we're really talking about. Ultimately is lead quality, right? I would much rather a company spend less money and reach and the lead quality increases. And we can track this just by creating a lead quality index through HubSpot or sales force data.

[00:32:50] A simple way of looking at it is let's say you generate a hundred leads into HubSpot or Salesforce based on your digital advertising. And your click through rate was 10%, which is a high click through rate in many instance, Most companies would be very pleased with that and with the ad agency. But the reality of it is, is if only two of those leads are closing. There are several things to look at. One, is it the sales team, right? There could be some things that fall apart, that aspect, which we do often analyze, which is again, atypical, right? It's that business ecosystem. You don't want to look only at the digital data, but how that interacts with the rest of the company.

[00:33:29] But let's say it is an issue with the actual quality of leads directed digitally. There may be something that can be done through appropriate analysis that we would undertake to identifying how to generate a higher quality lead. And I'll give you the specific example in dollar terms, is that. I was working with a client.

[00:33:48] We put in our version of hyper targeting, which by the way, hyper targeting was coined by MySpace. Like in 2006, it's in the public domain.

[00:33:57] Eric: I love that we're throwing that around, like it's some state of the art term.

[00:34:01] Ken: Yeah, no, it's Hey, it's been around forever, but the current version is so not equivalent, at least as I view it, even though it's the same term, it's a rocket ship versus the horse and cart.

[00:34:11] Eric: Not what my friend, Tom imagined.

[00:34:14] Ken: That's right, but the concept is there broadly at any rate, looking at the, this particular client that was in the healthcare industry is that we put the hyper targeting in place at their location. I believe it was about 30 locations and reduced the amount of time that it took to generate the sale, which each sale is worth about a hundred thousand dollars in this case. So not inconsequential and over the course of, I think 35 months generated an equivalent of $1.3 million in sales related to that advertising versus the old way of doing it. It only generated $600,000 of sales, but took around 30 months versus 60 months.

[00:34:57] It was a lot longer time period. And that was even at more facilities. So we really changed the game for them. I will bring up a really funny point as they had a hard time believing the numbers.

[00:35:09] Eric: There is a measure of success when it is too good to be true, but it's true.

[00:35:15] Ken: It's funny how sometimes people are so entrenched in traditional ways of viewing things that shifting into that new paradigm is difficult process.

[00:35:25] Eric: It can be a very painful process and a lot of people have to be dragged, kicking and screaming into the new tomorrow.

[00:35:31] Ken: Making an extra amount of money. Makes it a lot easier.

[00:35:34] Eric: It sure does.

[00:35:37] Ken, this has been an absolutely amazing discussion. I'm so fascinated with the work that you're doing, and I'm super excited to see what is next for KnoWhere.

[00:35:49] Thank you so much for being our guest today.

[00:35:52] Ken: Thank you as well. It's been a great conversation.

[00:35:55] Eric: And a special thanks to you, our audience for tuning into this episode. Visit, WeKnoWhere, that's K N O w H E R e.com to check out some case studies and see some really powerful images that help make that bridge from the data to the visual.

[00:36:15] And tune in for new episodes that'll delve into more of the electronics industry and share our podcast with your colleagues and friends. You can also sign up to be on our email list to receive updates and the opportunity to provide your input on future topics. Go to SiliconExpert.com/podcast to sign up. Until next time, keep the data flowing.

Latest Episodes

December 22, 2022
Play Pause

Is Your Co-Worker Lying? It May Be More Obvious Than You Think.

Whether it’s a terrorist that’s planning a bombing or a co-worker that has created a bad situation in the workplace, both are likely lying. See how to pinpoint the lie versus the truth and reveal the shortcomings in a company that has a challenging culture. Former FBI agent, Colton Seale has had an interesting career interviewing people. He can give an inside look at the new methods he’s helped create in the questioning process of interrogation (or information-gathering-session). The truth is out there!

November 1, 2022
Play Pause

From Transforming IBM Call Centers to Creating US Navy Digital Twins

Transformational advancement is a necessity. If you’re not changing and morphing, you’re going backwards. Hear about how IBM call centers went from individual sites to an interconnected global network. What it took to convert aviation from paper manuals to digital. How two destroyer collisions launched the US Navy into using digital twins. And more.

October 1, 2022
Play Pause

Innovating Tough Tech, Crossing the Chasm, and Avoiding Analysis Paralysis

How does a start up go from innovative genius design to profitability and scalable manufacturing efficiency. This is the make or break question for most companies that want to get past the initial phases of exciting development and buckle down to the nitty gritty of having a sellable product.

September 1, 2022
Play Pause

Building Circuit Boards and Helping Cancer Patients

Kevin Devine and Liam Holt are partners, but more importantly, friends that have each other’s back. Both have had their share of success, but not without the challenges that come from running an electronics industry business. This is their story. They’ve managed to not only build a great company but also discover a way to help those in need that are fighting cancer.

July 30, 2022
Play Pause

What’s Your Colleague’s Reasoning? Engineering and Procurement Need to Sync

In the past, the phrase ‘Not my Job’ may have been the norm. But today, with shortages and disruptions happening more and more frequently, an Engineer and a Procurement Manager needs to be acutely aware of each other’s needs and justifications. The new phrase is: ‘Walk a Mile in My Shoes.’ At the end of the day, your production and manufacturing need to be uninterrupted and efficient in order to keep everyone happy.