Iterative Marketing Podcast Ep. 45: With Measurement, The Sky’s The Limit

Iterative Marketing Podcast Ep. 45: With Measurement, The Sky’s The Limit


Hello Iterative Marketers!
Welcome to the Iterative Marketing Podcast where each week we give
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of your fellow Iterative Marketers. Now let’s dive into the show. Hello and welcome to the
Iterative Marketing podcast. I am your host Steve Robinson and with me as always is the industrious
and innovative, Elizabeth Earin. How are you doing today? I am well.
How are you, Steve? I am doing pretty well, pretty well. I have been cranking the classical
music in our house these days. Oh! Is this out of the
ordinary for you? A little bit. I think it’s a combined effort on
my part to help calm the children, we have a 5-year-old
and a 2-year-old and it gets kind of chaotic and I have also found a newfound
appreciation for classical music, so now I need to relearn or learn, I learned a little bit a long time ago
about the different composers, but it has been so long I forgotten who
I like and what kind of music, etc. So I am starting to in all my
free time reeducate myself Now, you have very
inquisitive children. So are they asking you about
music and composers and are you able to answer those or they are just more
just enjoying it right now? They haven’t been asking
me questions yet but I have been force feeding
them little bits of information, more like because they
are 5 and 2, what does this song make you feel like
and did you hear this instrument, but that may be just setting myself
up for something in the future where they start asking me questions
I don’t know the answer to. We will see. And then you actually
have a third child, so does she not get to
listen to the classical music? She gets to listen to classical
music and she seems to like it but she is also 6 months old
and not really, still trying to figure
what things hurt when she hits herself
in the head with them, so classical is probably a little out
of her reach at this time. Yeah, I was unsure if you were
setting up some odd experiment with your children, what happens when these two get to
listen to classical and this one doesn’t. Well, classical music is not what
we are talking about today. What are we talking about today? So today we are talking
about measurements, specifically effective measurements. We came up with before the
show basically four components to what we feel are
important to keep an eye on or keep us as parts
of your measurement if you are measuring
your marketing success. And we have talked
about this before, experimentation and measurement, just on their own just because you are doing it doesn’t necessarily mean you
are doing it successfully or you are realizing
the benefits of it. So, when we talk about
these four components, again these are the things
that really help to ensure that you are getting
the most out of it. So it starts with
measuring your investment and what that comes down to
is really trying to figure out – before you can get to figuring
out what your ROI is, you have to understand what you
have invested to get there and we are not just talking necessarily
about the monetary investment, we will get more into that
a little bit later in the episode. We talked about ROI a lot in the
past in our reporting episodes and some other episodes, we will link in the show notes, but ROI has that investment component but it also has the
return component, right? So, what’s the return
or the outcome? So the next thing
we will talk about, the second component we will
talk about is measuring outcomes, what it means to be successful either on an ongoing basis
or in your experiments. The third thing we are going
to take a look at is attribution and I don’t know if you
would agree with me but I sort of feel like attribution
is marketing’s Holy Grail. It’s what we all want
to be able to do but it just seems to be
just out of grasp for us, but specifically really what investments
take credit for which outcome, so what did we do
and what did that result in. So, we will dive into what to look at
when we are taking a look at attribution. And by dive in I mean we
will touch on it fairly briefly because it’s a huge rabbit hole and we don’t have time today
to get into the details of it. The fourth component is really time because our report doesn’t
do a whole lot of good if it’s just one day at a point, we will talk about how time impacts
your measurement and your reporting when you start
to set it in motion. Before we dive into
these four areas, I think it makes sense for us to really
talk about why we chose this topic because we do have a
method to our madness, and we sit down
and we outline a podcast and in this particular case one thing
that we hear over and over from clients is what is my ROI
or is my marketing working? And it’s not necessarily
always a yes or no question or there is not a straight answer and measurement really
leads into that and so that’s really one of the reasons
that we wanted to dive into this. And at the same time we
have worked with clients or we have run across
plenty of organizations where they are doing
the measurements, they are doing the reporting but they are measuring
for measurements sake or worse than that they are
measuring vanity metrics because it’s the metrics that they can
make the graphs go up into the right and makes them look good or that gets them the positive
feedback from the C-suite. Or because that’s what they have
access to in Google Analytics, and so they have copied
and pasted that graph. It’s hard because as marketers most
of us aren’t analysts by trade and so really getting to
understand measurements and make them more actionable, it doesn’t come naturally. It’s something you really
have to work towards. I know I have struggled with this and I am lucky that I get to
work with someone like you who is kind of this you have
got this crazy mind for it and I think I have kind of absorbed
some of that information and I feel very
fortunate for it, but it is definitely something that
I know I have to work towards. Yeah, I think it’s something we
all have to work towards. It’s hard to get to your left brain
and your right brain working in sync and this is definitely I think a
challenge for all of us marketers and myself included. At any rate, it definitely
warranted a podcast episode and I am excited that we
get to talk about it today. So, without further ado let’s talk
about what to measure, how to measure it and then
how we put that data to work. Excellent. So, the first thing
is investment, right? This is – you can’t measure – you can’t accurately report
return on investment unless you understand the investment, so where are you putting the effort
in to get the outcomes you want? As I alluded to earlier, this isn’t
just necessarily the hard cost. It’s not just the money that
we are putting out there. There are other resources
that we are using that we are making a
choice of where that goes. And so taking a look at
not only the monetary side of it but the resources is important in
understanding what that total investment is. So, by measuring this we actually
have to go through the motions or the efforts of making sure that
we understand where the Dollars go. In other words, how are we
segmenting our programs out and then understanding where
the Dollars actually go and by Dollars that’s not
just advertising Dollars, that’s also cost of
production too and then take that
one step further, do the same thing with time and figure out where has our time
and our effort been going as well because both internal time
and agency time isn’t always broken out
or divvied up based on which marketing programs
or efforts we put that time into. And it’s funny because I worked for
and with a number of organizations that really don’t pay
attention to tracking time until you are talking about all right this is something do
I need to hire a new position for and suddenly there is this frantic
search for logging time and what you are spending to try and determine if it
makes sense to add a new part time or full time employee but again this is another cost
that is adding to the investment that you are making and something that
we want to factor in and make sure that we
are getting that ROI. So let’s break this down for
some tangible examples. So, for example,
say a company blog, if you have a company blog, you have some hard costs
probably associated with that as far as how much media does it
cost to promote each blog post. But those are just the hard costs and obviously there is more that goes
into writing a blog than just posting it, there is sourcing, writing,
editing, publishing and so I am promoting it and again not those hard costs
but the time it takes to do all of that. So what is that costing you? For another example, I mean you can look at something
like a pay per click program and in this instance, the hard costs
are pretty straightforward, it’s going to be what you
paid for each click but then the soft costs
also come into that, are you paying an outside
consultant in order to manage this, if not, then what sort of time
and effort goes internally into managing that
pay per click and if so what time goes
into managing that consultant and making sure that you
are reviewing those reports and staying on top of the
pay per click program. So, next we look at
measuring outcomes and what we are
looking for here is sort of this question of
did we move the needle and as clichéd
does that sound that is what all we want to know
about our marketing investment. The trick is understanding which
needle are we supposed to be moving, because there are million to one
different metrics we can measure. Google Analytics has all kinds
of numbers buried in it, and this is where identifying
the K part of the KPI what is the key performance indicator not just any other
performance indicator. And I am sure you
are familiar with this but again this is the metric
that is indicating success, so when we make
a change to this number, then we are
making a change in the overall effectiveness of
our marketing efforts and again this is the one
we really want to focus on. And not every program
has one number, right, but there is – usually you will
have one to four numbers and some of them are what
are called leading indicators and some of them are what
are called trailing indicators. There is a reason that we look
at these two different ones, as you identified them,
trialing and leading, and let’s start with trailing. Trailing indicators are telling
us if we had overall success and a lot of times
this is the number that your finance team or your
C-suite wants to focus on because this ties directly
to your bottom-line and that’s good because it’s showing us we can show
that direct impact that we are having. The problem here is that
depending on our sales cycle a lot of time these
indicators are delayed. If you got a six month or
a year long sales cycle it’s going to be six months
or a year or longer before you start to report
on this and show the impact that your efforts have had and that gets complicated because the C-suite doesn’t always
want to wait six months or a year to find out if your marketing program
has been successful or not. It also makes it hard to optimize because if you are trying to
take a series of outcomes and use that information
in order to make changes, then you have to wait
6 to 12 months in order to get feedback
on your changes in order to make the
next set of changes, so it doesn’t create a
tight enough feedback loop in order to perform optimizations. Not only that but because these
are typically tied to revenues or sales, they are not coming out
of your marketing systems, they are going to be coming out of
your CRM or your e-commerce system and if you have
the right data points you can push that data
into Google Analytics but again require some set up and you have to know what you
are tracking and looking for and there are some
prerequisites there. So at the end of the day trailing indicators would be great if we could all run our
worlds up on trailing indicators, and if you work for an
e-commerce company you might be able to
run off of the money because it’s pretty straightforward, you don’t have that
long sales cycle but the rest of us working for
B2B or a B2C organization with a long considered purchase
cycle like a car or healthcare, then you can’t run
off of trailing and you really have to
run off of other indicators in order to do your optimization, in order to determine
success of an experiment. So again these are going
to be those indicators that tell you if you are
on the right path by measuring something
that is happening now, that you can to Steve’s point,
you can optimize on, that you can report on today, not six months or a year
or two years from now. So, and these are
called leading indicators. If you have ever heard of the phrase,
a canary in a coal mine, I don’t know if you actually know
where that comes from but we did actually use canaries
in coal mines at one point in time, or at least small birds, right, and the canary was the leading
indicator in that scenario. The coal miner would take that
canary into the coal mine with them and if there was toxic gas in the coal
mine then the canary wouldn’t make it and so you would watch the canary and if the canary died that meant
get the heck out of the coal mine because you are coming next. So, in that case the leading
indicator is the health of the canary and the trailing indicator is the
health of the coal miners, right? Yes. So as your very dark example has shown
us to be an effective leading indicator the metric really has to be
correlated to the trailing indicator. And as marketers the
way that we get to that is that we look at our
trailing indicators and we look and see which other
metrics correlate to those indicators. So, if web traffic going up is
directly correlated to sales, then we are able to use web traffic
as a leading indicator for sales. You do the same thing for leads
or leads of a certain quality or the number of demos is – One of our clients uses demos
as a very key leading indicator because if they demo
their particular software they know that they have a
consistent rate of conversion between that demo
and the number of sales. It’s different for every business but you should be able to find that
correlation between those two metrics. So I just want to make
sure I understand, so we are looking
at our past data to see where see when that leading
indicator changes so we can identify how it impacts
the trailing indicator, is that correct? That’s correct.
Okay. And it’s not always perfect, you are going to have blips, you are going to have instances
where the leading indicator changes but it’s influenced
by something else, and so to come back to
our dark canary example, if that canary
dies of old age everybody still gets out
of the coal mine, that was influenced
by an outside situation and not because of what
you are trying to measure which was the gas in that case. So, putting this into
a real life example and not one where we
kill poor innocent birds, if we were working on a blog
for an accounting firm, leading indicators would be
dwell time on the blog post, shares, repeat visits, those are things that we
could track in the moment that could also be tied
to those trailing indicators which could be new clients
or retained clients. If you flip that and look
at like a pay per click site or pay per click for an
e-commerce site, here your leading indicators would
be simple things like clicks, but also sales because you are going
to get that sales data right away. So, in that case your trailing
indicator is not only your sales but also things like lifetime
value of a customer which you are not
going to know initially. You are going to know that they
put something in their cart and they checked out but you are not going to know
if that’s a customer for life or if that was a one
time situation, right? I think you brought up a
really great point, Steve, in that both your trailing and your
leading indicators can involve sales if that works with
your business model. Either way I think it’s a
good time for a break, so let’s go help some people. Before we continue I’d
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are important to you. And we are back. So, before the break we talked about measuring
your costs or your investments, both hard and soft, and then we also talked about
measuring your outcomes, but I think it’s important
that we take a moment to talk about how we connect the
costs or investments to the outcomes and to do that we will talk about
a term called attribution. And as you mentioned earlier
it’s important to note, this is very, very high level, we definitely can’t get into
everything about attribution but I think that this may be something
we want to explore in the future. Yeah. We talk about attribution, there are number of what
are called attribution models. They are models because
they are not perfect, they are the best we
can do in order to try and come up with
a way to look at which investments result
in which outcomes. The simplest form of
attribution models are where we take an entire sale and
attribute it to one given investment and that’s really kind
of very simplistic and it’s not the way that
marketing actually works just because somebody clicked
on a pay per click ad, doesn’t mean that that’s the only
reason why they bought from us, just because we know
that they saw a billboard does not mean that that’s the only
reason that they bought from us, however, to get more
complicated than that is sometimes beyond the capabilities
of lot of marketing organizations. So in the simplest format, if we are attributing that
revenue to the first touch then that’s called first
touch attribution and if we are
attributing that revenue to the last piece of
marketing they touched then that would be
last touch attribution. So like Steve said,
very, very simple. But that’s also how most of
our analytics system work. Like Google Analytics
out of the box will do– I believe it does a form
of last touch attribution, so does AdWords, to get more complex is when
you get into something called multi-touch attribution, and that’s where we start to say, well, they touched these six things, so we are going to
divvy the revenue up based on the six things that
we think that they touched along their way in their journey and that gets really complicated because there are several different
types of multi touch attribution models. Again, rabbit hole that we don’t
want to dive too far into today, so just know that you can get more
complicated than first and last touch and that first and last touch
are really inaccurate because they don’t that whole
journey into consideration. So no matter which attribution
model you choose, what we are looking for is finding a way
to measure when a touch point occurs and which of those investments
are responsible for that touch point. So when we start talking
about digital efforts, this we have got means available to
us that makes this a little bit easier than I think is in
traditional advertising and we will touch on that
in just a second here, but when we are specifically
talking about digital efforts, it’s tagging your URLs, and we will link to some really
great resources in the show notes because I know I said
tagging your URLs and there is a lot more behind that
than the three words that I just said. Yeah. But really it’s just keeping a
ledger of where you invested and then making sure that
that ties back to a click so that then you can come back and
say we paid this much for these clicks and that resulted into this much
revenue at some point down the line, yes, it’s more
complicated than that, that’s why we are linking
to more resources. You can do the same thing to
some extent for traditional although it gets
a little bit fuzzier, I think marketers generally take
one or two tags for this, right? Some organizations look
at the probability that a prospect saw or experienced a
certain piece of marketing or advertising and try and formulate
that into the equation. Another tactic is to try and use
surveys of new and existing customers and see what they can recall. And so neither
one is perfect, but it does give us an
opportunity to measure. So let’s break this down and talk about
couple of examples we referenced earlier, like a company blog. There are few questions
you need to ask internally to decide how you want to do
your attribution for your blog and every organization
is going to be different, so you are going to have to come
up with your answer for these because well, again every method
is going to be slightly inaccurate. So, the first question
you need to ask is what constitutes when a sale
was influenced by a blog post, does that mean that that somebody
just saw it along the way in their journey or does that mean that this was
the thing that made them buy, right? And the best way
you can guess that is this is the thing that they clicked to
submit a lead or make a purchase off of. Another thing to
keep in mind is did they just have to interact
with this one blog post or did they have to interact
with multiple blog posts on their journey to completing
whatever that action is that we have identified
as our conversion. And then did that blog post have
to be the source of that person being introduced to
our brand or company because if we spend
a bunch of money on some other awareness building or
traffic generation tactic somewhere else and they just happen
to also hit our blog, does that really matter, right? So some organizations only
count it as influencing a sale if that’s where that person became
aware of the brand or organization. Looking at our other example
pay per click, one of the questions
we want to know is what constitutes a sale that was
influenced by a pay per click ad? And so here again we have
to ask that question, was pay per click the actual
origination of that interaction, would that person have
found us otherwise if it weren’t for the
pay per click post or had they found us previously before they interacted with
the pay per click post. Did they have to click on a paid ad
somewhere along their journey and did it matter which ad they clicked
on or just any ad would have worked? And then finally again was
this the last ad that they clicked on or the last thing that they did
before they made that purchase or were there other things that
influenced that purchase along the way? So, how you answer
these questions is going to help to determine which of
the attribution models you want to use. I am going to throw a little bit
of a curve ball out there and I know we didn’t want to get
too far into attribution modeling but how much does technology
play a role on this too in your ability to measure? It plays a huge role because if you can’t tell if somebody
interacted with that particular thing that you put a bunch of
money and effort into, then you have no way of ever
attributing any sales to it, so here is where having solid
systems for tracking engagement and interaction with
your digital assets is key as well as having solid data, the best data you can on some of
your more traditional investments and who may have seen them and the probability that they saw
them or interacted with them or again coming back,
survey data and in either case, the better you have
as far as sensing that in technology through taking Google Analytics
and pushing it to its extreme using a marketing automation
system, whatever, the better you are going to be
able to accurately do that attribution. And finally the fourth
component, time. We mentioned time but how
does that factor into all of this? Well, I think if you look at it measurement of one data point doesn’t
create anything actionable, right? If I know my bank balance today that may or may not
be useful whole lot if I don’t know what
it was last week or I don’t have a goal for
what it’s supposed to be three weeks from now, right? We really need to put
our metrics in motion in order to be able to get useful
information out of them. And there are two things
that make that possible, to make that information
useful and actionable and that’s trends and experiments. So, when we are talking
specifically about trends, we are looking at how can past
performance of a particular metric predict the future success or failure
of what it is that we are doing. So if you have a KPI that seems to
be improving in it’s performance week by week by week you can anticipate that it’s going to
continue to improve in it’s performance and therefore you can anticipate
the better results that might come and then likewise if you have a KPI
that’s diminishing week by week by week, well now you know, okay, this is
starting to not work anymore, we need to take some
corrective action because otherwise the future
means some bad numbers, right? So it’s a way of taking the past
and predicting the future against the warnings at the end of every financial
commercial you have ever seen. Of course, of course and when we combine these
trends with experiments, that things start to
get really interesting and we can see some
of that change and what we are talking about
when we talk about experiments is if I were to change
one particular input how does this change my output, how do I change what’s
going on with the trend that I have already
identified and recognized. The best example of this, where we keep coming
back to the AB test, the nice thing about AB test is you are usually controlling the
number of inputs that you are changing, the number of things
that you are changing and then you are watching
the KPIs on the other side to see how they have changed. But there is another component of
time that we need to consider and that is making sure that we have
enough time to let data accumulate so that it can be actionable. Traditionally marketers would
do this by running all of their marketing
in a campaign format, so we would launch a
campaign on a given date, we would run, run, run for
a given period of time and then we would
end that campaign and then start the next campaign, at the end of that campaign we would
run a post mortem or some analysis of how did that campaign do, right? And look at the data backwards
to come to a conclusion. But in Iterative Marketing,
we don’t believe in time boxing and we have talked
about this a lot before, we will link back to an episode where we go into more detail
if this is a new concept but really what we
are looking at is that when we remove the campaign’s
start and end dates, we don’t have a built in
point for analysis anymore and so one of the things that we want
to make sure we are doing is that we are taking a look over
a specified period of time. And that time has to be broad enough
in order to get the data we need but also not to too narrow, so you can think about it, do I want to look at this data at the
5-foot view or the 50,000-foot view or where in between there, if we start analysing data on a
daily basis it doesn’t work for us and it doesn’t work for us
for two reasons. One, you can count that your
prospects are going to react differently depending on the days of the week, so there in itself you are going to have
different data on Saturday and Sunday than you are going to have
on Tuesdays for example. Same thing would be
true for holidays. You are going to have different data
on Thanksgiving Thursday versus any Thursday in September. The other reason why you can’t look
at the data on a daily basis is because unless you are just
cranking up tons of volume you are going to have graphs
that are going up and down, and up and down and
up and down like crazy and you are not going to be able
to find the signal within the noise, there is a certain randomness
to the way that the people act that it’s going to cause your analysis
to be unusable at a certain level, so you need to spread out that time
and look at broader chunks of time in order to be able
to do effective analysis. And there is one more piece to this and that is when we are looking
at experimental analysis, we have to make sure that when we are reporting on
the results of our experiment that we are confident that
we can reproduce them and that comes down
to statistical confidence which means that we have been able to
accumulate data for a long enough period that we can either
determine the result with that high desired
statistical significance or we can determine that we are never
going to be able to tell the difference, which is also a conclusion that
can happen from your experiments. There is one more wrinkle, and that is that in order to be able
to effectively understand a change we have to have a period of
non-change on both sides of it, right? So, if I am changing
something every single day then my KPIs are going
go be all over the place, my metrics will be
all over the place and I am not going
to really be able to tell well, what was this
before the change and what was this
after the change. So, you have to limit the number of
changes you have in a given period. So for example, if I were to
make a big change today, I have to let enough
data accumulate to make me comfortable knowing that
that big change had the desired effect, and this means that enough time has
passed where no other changes were made that they could potentially be
influencing this one that I am testing that I am specifically looking at and this is hard because once you
kind of get a taste of experimentation you want to keep it going and you have to remember to
pull back on the reins a little bit and make sure
that you are not– one change isn’t influencing
another change. And you also want to – same thing is true of optimization. If you are going in
and you are tweaking things because you could see that
the data is telling you that you are missing opportunities, then you want to go in and do
that as often as you possibly can but the reality is that you need
to let enough data accumulate in order to make sure
that you are confident in making that tweak
to that optimization. So we have given you
couple of different things of what not to do
in terms of time, but I think it might be helpful, Steve
if we go into what we would recommend or what we have
seen has worked and specifically that comes down to what
we have seen in terms of a timeframe that makes sense for experimentation. And for us and for our clients we have come to settle on a
monthly cadence or monthly cycle. So, this cycle is sort of
the back bone or spine to which we do our optimization
and our experiments, so that we can make sure that
we have a recurring time break regardless of the programs
that may run indefinitely. So, this doesn’t necessarily mean
that we only look at this monthly, we actually go in weekly
and do check ins on the data to make sure that nothing
has gone off the rails and to also determine if any of the
experiments have collected enough data that we can make a call on it because again we only
have a finite period of time that we can run experiments. So, if an experiment has concluded
we want to turn that one off, implement the changes
and get the next one in place so we can continue to build on
those insights that we are learning. We also do weekly optimizations of
data points that accumulate lot of data. So, examples of this are things like
bids and keywords and AdWords and some of the bidding and
targeting and media optimizations that we might do with
programmatic media where you have a
high volume of data and we can go in to
make changes weekly because we have enough data before
the change and after the change to make the optimization. And then at mid month we are going to
take a look at our trends for the month and identify anything that
requires immediate action that must be identified right then and then when we get
to the end of the month, that’s when our
heavy analysis occurs, and at that point we have given
our programs that are running enough time to accumulate that
data through those weekly check ins and the midcycle check in we have been able to iron out
some of those fluctuations that occurred due to external noise or those weekly and monthly
cycles that we talked about. Conversely if we try to do the
analysis more often than monthly, it would be a significant – Say we did it twice a month, we tried to do heavy
analysis twice a month, that would be double the
effort on that analysis but the return isn’t
going to be there. We are not getting twice the
return on that analysis, right? So, we found that monthly is
enough to keep us nimble and keep our clients nimble so that we are responding
and reacting to changes, we are reporting and keeping
everybody in the loop but at the same time
it’s not overkill where we are constantly trying to
do deep analysis into trends and what’s working
at the same time. I think another benefit of
a monthly reporting is it sets up a regular
cadence for meeting so that we make sure we
are taking action on the data because as everyone knows
you have the best intentions and all of a sudden
six weeks have passed and you haven’t had a chance
to go back and look at it, so by making sure that we
have monthly meetings we know that we have got a scheduled
time that we are going to check in, we are going to sit down and we are
going to have a conversation about this. So, let’s wrap up the points
that we talked about today. The first key to accurate measurement
is to understand your investment, your cost, where is your money
and where is your time going. Next, we look at measuring the
outcomes which are tied to the KPIs through both trailing
and leading indicators. Third, we discussed attribution, understanding how you connect to those
investments to the desired outcomes. And finally we looked
at the role of time and making sure that we have enough
time to not only run our experiments but identify the trends from
those experiments. We touched briefly on
four deeper topics today, we are going to link to plenty of
resources in the show notes for deep dives and you can bet we are going to
revisit at least one or two of these in a future podcast episode. If you haven’t already subscribed you
can do so at iterativemarketing.net. I want to thank everyone for making
time for us again this week. If you haven’t submitted a review on
iTunes we would really appreciate it, it helps others find our podcast
and get the same value out of it that you hopefully did today. Until next week onward and upward. If you haven’t already, be sure to
subscribe to the podcast on YouTube on your favorite podcast directory. If you want notes and links to
resources discussed on the show sign up to get them emailed to you
each week at iterativemarketing.net. There you’ll also find
the Iterative Marketing blog and our community LinkedIn group where you can share
ideas and ask questions of your fellow Iterative Marketers. You can also follow us on Twitter. Our user name is @iter8ive or email us at
[email protected] The Iterative Marketing Podcast is
a production of Brilliant Metrics a consultancy helping
brands and agencies rid the world of marketing waste. Our producer is Heather Ohlman with transcription assistance
from Emily Bechtel. Our music is by SeaStock Audio,
Music Production and Sound Design. You can check them out
at seastockaudio.com. We will see you next week. Until then onward and upward!

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