www.siliconexpert.com Open in urlscan Pro
2a02:e980:148::2d  Public Scan

Form analysis
3 forms found in the DOM

/

<form role="search" action="/" __bizdiag="115" __biza="WJ__">
  <label class="">
    <span class="screen-reader-text">Search For:</span>
    <input type="search" class="header--main--search--input--input" placeholder="" value="" name="s">
  </label>
</form>

/

<form role="search" action="/" __bizdiag="115" __biza="WJ__">
  <label class="">
    <span class="screen-reader-text">Search For:</span>
    <input type="search" class="header--main--search--input--input" placeholder="" value="" name="s">
  </label>
</form>

/

<form role="search" action="/" __bizdiag="115" __biza="WJ__">
  <label class="">
    <span class="screen-reader-text">Search For:</span>
    <input type="search" class="header--main--search--input--input" placeholder="Search" value="" name="s">
    <i class="fas fa-search header--mobile--search--trigger--icon"></i>
  </label>
</form>

Text Content

Free Trial
Search For:
 * Platform
    * Column
      * P⁵ PlatformSiliconExpert P⁵ Platform brings out the best features on our
        application.
      * Part SearchSearch over 1 Billion Electronic and Mechanical Components,
        and stay up-to-date.
      * BOM ManagerEasily upload your bill of materials and instantaneously run
        risk assessments.
      * AlertsGet notified when PCNs, datasheets and lifecycle updates occur.
    * Column
      * ACL/AMLReduce Risks and Ensure Compliance with Approved Components Lists
        (ACL) and Approved Manufacturers Lists (AML)
      * ComplianceServing up to date information and reports on regulatory
        compliance.
      * SiliconExpert ConnectSiliconExpert has partnered with leading companies
        to embed our native software into design engineering tools.
      * APIAccess comprehensive component data directly within your own
        engineering environment.
   
   Build Your Solution
 * Services
   Professional ServicesTransimBusiness Intelligence SolutionsSupply Chain
   SolutionsOur Solutions
 * Benefits
    * Column
      * Design Right The First TimeSiliconExpert's normalized, comprehensive
        component database will allow you to select the right components with
        confidence.
      * Detect and Avoid Counterfeit PartsRecognize counterfeit parts early in
        the design phase and avoid them completely.
      * Meet Compliance RequirementsThe challenge posed by environmental
        regulations is that standards evolve continuously.
    * Column
      * Manage Obsolescence RiskStay ahead of obsolescence or end of life
        concerns to adjust parts as needed.
      * Forecast Supply Chain RisksEnsure supply chain continuity and identify
        risks before they arise.
   
   Explore the Platform
 * Customer Success
   Knowledge CenterIntelligent Engine PodcastBlogEventsSupply Chain ImpactsFAQs
 * About
   Who is SiliconExpertTaxonomyWho We ServeContactCareers
 * Partners
   Our PartnersBecome a Partner

Live Webinar | Supply Chain Survival Guide: How to Prepare for an Unpredictable
Supply Chain | June 29 | 11:00 AM EST

Register Now!
 *   Contact
 *   Careers
 *   Login
 *   API Services
 *   Supply Chain Impacts

Free Trial
Search For:
 * Platform
    * Column
      * P⁵ PlatformSiliconExpert P⁵ Platform brings out the best features on our
        application.
      * Part SearchSearch over 1 Billion Electronic and Mechanical Components,
        and stay up-to-date.
      * BOM ManagerEasily upload your bill of materials and instantaneously run
        risk assessments.
      * AlertsGet notified when PCNs, datasheets and lifecycle updates occur.
    * Column
      * ACL/AMLReduce Risks and Ensure Compliance with Approved Components Lists
        (ACL) and Approved Manufacturers Lists (AML)
      * ComplianceServing up to date information and reports on regulatory
        compliance.
      * SiliconExpert ConnectSiliconExpert has partnered with leading companies
        to embed our native software into design engineering tools.
      * APIAccess comprehensive component data directly within your own
        engineering environment.
   
   Build Your Solution
 * Services
   Professional ServicesTransimBusiness Intelligence SolutionsSupply Chain
   SolutionsOur Solutions
 * Benefits
    * Column
      * Design Right The First TimeSiliconExpert's normalized, comprehensive
        component database will allow you to select the right components with
        confidence.
      * Detect and Avoid Counterfeit PartsRecognize counterfeit parts early in
        the design phase and avoid them completely.
      * Meet Compliance RequirementsThe challenge posed by environmental
        regulations is that standards evolve continuously.
    * Column
      * Manage Obsolescence RiskStay ahead of obsolescence or end of life
        concerns to adjust parts as needed.
      * Forecast Supply Chain RisksEnsure supply chain continuity and identify
        risks before they arise.
   
   Explore the Platform
 * Customer Success
   Knowledge CenterIntelligent Engine PodcastBlogEventsSupply Chain ImpactsFAQs
 * About
   Who is SiliconExpertTaxonomyWho We ServeContactCareers
 * Partners
   Our PartnersBecome a Partner

Search For:
P5 Platform
Services
Benefits
Customer Success
About
Partners
 * Login
 * Free Trial
 * Careers

P5 Platform
P⁵ Platform
Part Search
BOM Manager
Alerts
ACL/AML
Compliance
SiliconExpert Connect
API
Build My Solution
Services
Professional Services
Business Intelligence Solutions

Our Solutions
Benefits
Design Right the First Time
Detect and Avoid Counterfeit Parts
Meet Compliance Requirements
Manage Obsolescence Risk
Forecast Supply Chain Risk
Explore The Platform
Customer Success
Knowledge Center
Intelligent Engine Podcast
Blog
Events
FAQs
About
Who is SiliconExpert
Who We Serve
Contact
Careers
Partners
Our Partners
Become a Partner

EPISODE #12 | MAY 26, 2022


COOKIES ARE DEAD! LONG LIVE GEOSPATIAL AND PREDICTIVE ANALYTICS!

00:36:48 minutes


LISTEN NOW





COOKIES ARE DEAD! LONG LIVE GEOSPATIAL AND PREDICTIVE ANALYTICS! …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.
Subscribe on: Itunes | Spotify | Blubrry
 * Share:
 * 
 * 
 * 
 * 

 * 
 * 
 * 
 * 


SIGN UP TO BE NOTIFIED WHEN NEW EPISODES ARE POSTED!

Sign Up

 * 



[FULL TRANSCRIPT AVAILABLE BELOW]

SiliconExpert Podcast Episode 12 with Ken Sheehan of KnoWhere, LLC -
Transcription

[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.
Show More


LATEST EPISODES



May 26, 2022


COOKIES ARE DEAD! LONG LIVE GEOSPATIAL AND PREDICTIVE ANALYTICS! …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.

COOKIES ARE DEAD! LONG LIVE GEOSPATIAL AND PREDICTIVE ANALYTICS!

The phrase “Know Your Customer” is entering a whole new level of intensity. No,
it’s not going to compromise privacy. But it is going to drastically reduce or
strategically redirect advertising and lead gen marketing budgets. In this
episode, Ken Sheehan reveals how KnoWhere, LLC provides actionable geospatial
and predictive business intelligence by leveraging a unique mix of typical and
atypical methodologies and data.

April 29, 2022


THE 'CHIPS ACT' - WHY CONGRESS NEEDS TO PASS IT …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.

THE 'CHIPS ACT' - WHY CONGRESS NEEDS TO PASS IT

Passage of the “CHIPS for America ACT” could mean a lot of things to the
semiconductor industry in the United States, all of them good! Patrick, who is
an Army reservist, Iraq War veteran, former Congressional and administration
staffer who also led the DC office for the Semiconductor Industry Association,
explains in detail the reasons for the Act, why it’s happening and how it will
influence the United State’s place on the global semiconductor industry stage.

March 30, 2022


THE GOVERNMENT VIEW OF THE SEMICONDUCTOR INDUSTRY …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.

THE GOVERNMENT VIEW OF THE SEMICONDUCTOR INDUSTRY

Patrick’s experiences are far reaching and unusual for a tech lobbyist. He’s an
Army reservist and Iraq War veteran, former Congressional and administration
staffer who also led the DC office for the Semiconductor Industry Association;
he even helped to tackle counterfeit component imports with our US Customs &
Border Protection. He has a refreshingly candid view and deep understanding of
the relationship between Capitol Hill, the semiconductor industry and the
rapidly changing global stage that will affect us all; industry and consumer
alike.

February 25, 2022


VALUE ENGINEERING CAN SAVE YOU MILLIONS AND VOLKER WILL TELL YOU HOW …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.

VALUE ENGINEERING CAN SAVE YOU MILLIONS AND VOLKER WILL TELL YOU HOW

From inventing the 10-megabit ethernet switch to saving millions of dollars at
Honeywell, Volker Ebert has a lot to say about Value Engineering and Component
Engineering. Volker’s mission is to make sure everyone knows how to keep new
product introductions and manufacturing production plants from experiencing
excess costs or unnecessary interruptions.

February 1, 2022


PULLING A GLASS OF DRINKING WATER OUT OF THIN AIR …


PODCAST

Follow
Skip back 15 seconds

Skip forward 15 seconds

Play Pause
Your browser does not support the audio tag.

PULLING A GLASS OF DRINKING WATER OUT OF THIN AIR

Imagine being able to have a drink of pure, filtered, crystal clear water
without spending a dime on five gallon refill jugs or having to toss a plastic
bottle in the recycle bin afterwards. Cody Soodeen’s start-up, Kara Water
(www.karawater.com), has developed the technology to do this. He tells about all
the mistakes a young technology company should watch out for when designing and
manufacturing a new product!

245 Main Street Cambridge, MA 02142 408.330.7575 Sales@SiliconExpert.com
 * 
 * 
 * 
 * 
 * 

ABOUT

 * Company
 * Taxonomy
 * Our Partners
 * Contact
 * Careers

245 Main Street Cambridge, MA 02142 408.330.7575 Sales@SiliconExpert.com
 * 
 * 
 * 
 * 
 * 

PLATFORM

 * Part Search
 * BOM Manager
 * Alerts
 * ACL and AML
 * Compliance Module
 * SiliconExpert Connect
 * API Services
 * Free Trial

PROFESSIONAL SERVICES

 * Professional Services
 * Business Intelligence Solutions
 * Supply Chain Solutions
 * Our Solutions

CUSTOMER SUCCESS

 * Knowledge Center
 * Blog
 * Events
 * FAQs
 * Customer Portal
 * Supply Chain Impacts

© 2022 SiliconExpert. 245 Main Street Cambridge, MA 02142 | 408.330.7575 | All
Rights Reserved. | Privacy Policy | Terms of Use


[class^="wpforms-"]
[class^="wpforms-"]