Good morning. Thanks so much for joining us this
morning for Python Coding for Marketers. My name is Jim Brophy, I am the group director of digital
here at Northwoods and I will be your moderator for this morning. You might be asking yourself -
what exactly is Python, and how can I use it to help me in my marketing activities? Well, you've
come to the right place. We're going to spend the next 40 minutes or so laying it out for you in
brief. Python is a newer programming language that many marketers have found beneficial in helping to
improve efficiency and measurement. As marketers, we no longer need to rely on developers to
build tools to provide insights into our marketing tasks, and that's great news. Besides,
not all of us have access to developer staff. So today we're going to dive into Python basics,
the benefits of learning Python for marketing, show some examples of Python in action and how to
get started with Python. If any questions come up at any time during the presentation, please go
ahead and submit them using the chat feature and we'll answer as many as we can during the Q&A.
Also, we'll be sending out a link to a recording of today's program in the next day or two so you
can re-watch it or share it with your colleagues. For those of you who are not familiar with
Northwoods, or if this is your first time attending one of our webinars, I wanted to
take just a minute to introduce ourselves. We're a digital strategy, digital
marketing and advertising, website design, and software development agency
located just north of Milwaukee, Wisconsin. We've been helping companies grow their online
presence for more than 23 years, and have worked with more than 900 clients of all sizes across
a wide variety of both B2B and B2C industries. At Northwoods we keep up on trends and best
practices constantly and develop some of our own. And in doing so, we develop strategies based on
data and thought leadership that you can count on. You can check out our blog and
client stories at nwsdigital.com. One of our core values here at Northwoods is
lifelong learning, and we're proud to share our knowledge with others by offering dozens of free
education programs every year including webinars like today's. You can find a schedule of our
upcoming classes at learnatNorthwoods.com. But enough about us - let's go ahead
and dive into our presentation. Your trail guides this morning are certified
experts in the digital marketing space. Up first is Amanda Hoadley. Amanda is
a digital marketing strategist here at Northwoods, with specialties in SEO,
technical SEO, and Google Analytics. Amanda previously worked at Kohl's department
stores where she worked on technical SEO. Presenting with Amanda today is Katelyn Staaben.
Katelyn is a senior digital marketing strategist here at Northwoods, with specialties in digital
business analysis and website strategy persona development, and search engine and content
optimization. She holds certifications in Google Analytics, Google Ads, and conversion
writing. So you're in great hands this morning. So I’m going to get out of the way and turn
things now over to Amanda. Take it away Amanda. Thank you Jim for the wonderful introduction and
today's journey will consist of Python basics, benefit of learning Python for marketing, Python
in action, and getting started with Python. So to begin we will go over some Python
basics. So what exactly is Python? A simple definition is Python is a high-level programming
language that is designed to be highly readable and easy to implement. The Python interpreter is
free to download from Python.org, and there are versions for Linux, OSX, and Windows. And fun
fact - the name Python actually comes from the surreal British comedy group Monty Python, not
from the snake. So knowing Python as a marketer, you'll be able to find opportunities. You'll be
able to prioritize experiments, be able to devise a new strategy, build campaigns, and evaluate
your results to make data-driven decisions. And automate your campaigns. Python is the
world's fastest growing programming language, not just among software engineers, but also with
network engineers mathematicians, data analysts, scientists, accountants, and yes, us digital
marketers. So what makes Python special? Well it is able to solve complex problems
in less time with fewer lines of code. See the example here with C#, JavaScript,
and Python. When extracting the first three letters of Northwoods, this would be the line of
code for each. And you can see that Python uses fewer lines of code compared to the other two.
Additionally, Python is a cross-platform coding language which makes it easy for anyone, and
can run Python applications on Windows and Mac. There are many integrated development environments
such as PyCharm, Google Collab, Pythia, Visual Studio Code, and Visual Studio. Python improves
marketing campaigns by addressing mistakes that are inadvertently made by marketers, which include
inefficient keyword management, results tracking errors, inaccurate ROI calculations, and even
content marketing optimization. And what's great is that programmers don't need to know math. There
is a common anxiety that programmers should know a lot of math, however that is not true. Most
programming doesn't require math beyond basic arithmetic and being good at programming isn't
much different from being good at sudoku puzzles. Much like sudoku, writing program involves
breaking down a problem into individual steps. The Book Automate the Boring Stuff with Python
explains a great comparison of Python to sudoku. Just because sudoku involves numbers doesn't
mean you have to be good at math to figure out the solution. The same is true for programming.
Like solving a sudoku puzzle, writing programs involves breaking down a problem into individual
detailed steps. Similarly when debugging programs, which is finding and fixing errors, you'll
patiently observe what the program is doing and find the cause of the bug. And like all skills,
the more you program, the better you'll become. Additionally, Python has what is called
libraries. Python libraries are a set of useful functions that eliminate the
need for writing codes from scratch. Python libraries play a vital role in data
science, developing machine learning and data visualization, and image and data manipulation
applications. The most useful libraries for data analysis and automation is pandas. And pandas
is a powerful, fast, flexible, and easy to use open source data analysis and manipulation tool
built on top of the Python programming language. So now that you know some basics, let's dive into
the benefits of learning Python as a marketer. So, as a marketer, you probably run across these
problems such as - ads are getting too expensive, you need omni-channel presence but
it's hard to manage and coordinate, you have tons of data coming from your online
advertising campaigns but you don't know how to get valuable insights from them without investing
funds in expensive automation tools, and sometimes it's hard to calculate your returns on investment
due to attribution problems, and other challenges. So the question is, can you solve at least
some of these problems with Python automation? And the answer is yes. Because of its simplicity
and understandability, it can benefit those even with minor coding experience. And what's
great is coding is not just for developers. These days programming is an important
factor in designing and executing digital marketing strategies. Critical digital marketing
activities such as web ads, videos promotions, and social media would not be possible without
software programs. Which is why it is essential for marketers to understand programming basics in
addition to knowing some html, marketing teams can benefit by becoming familiar with and leveraging
the power of Python to help improve efficiency. And Python is a tool that can drive automation
across all of your marketing activities. So do you get tons of data from multiple sources
for which you need to produce periodic reports? Well Python is an efficient
tool for data pre-processing, analytics, and data visualization. You'll need to
write code only once to create a report, and after that reporting can be done within minutes. As well
as content optimization, AB testing is a popular marketing tool for comparing several versions of
a website app or an ad to determine which performs best. So for example, if you have two groups
of customers - one that is exposed to group ad a and the other is exposed to group ad b.
You can then compare these two ad groups’ conversion rates to find the winner. Of course,
the difference should be statistically significant for you to conclude that one of the ads is
indeed better. And Python is a perfect tool for streamlining AB testing and defining statistical
significance of the resulting difference. As well as customer segmentation, delivering
personalized experience to customers is a must for marketers these days. But before you can
personalize your messaging and experience, you need to understand your customers behaviors,
preferences, and habits. Python gives you access to the most sophisticated clustering
techniques. A number of machine learning techniques that are easily implemented with
Python will help you classify your customers by features that really matter, increasing your
revenue, and improving your customers experience. As well as customer feedback analysis - customers
use multiple channels to leave feedbacks about the products that they use. And large companies
in particular struggle to manually analyze all the reviews left on different websites and social
media platforms, and this is a perfect opportunity for automation. And coding has been portrayed as
a solution to all problems, but let's be real. Python will not help you accomplish tasks such
as writing copy for a new ad campaign, unless if you are very well versed in how to conduct
artificial intelligence. It will not automatically create brand new pixel-perfect email templates for
announcing a new product or sale promotion, and it surely will not attend meetings for you. Otherwise
Python is here to help you if you are drowning in spreadsheets. And learning Python can be cheap
instead of using expensive tools, or allowing you to translate knowledge into other areas
such as speaking to developers at your company. And using Python as a marketer has many advantages
that help with repetitive data gathering and management tasks. Understanding how to use
Python can help turn those repetitive tasks into automated ones. In many of the examples I have
explained already, which include ROI calculations, customer segmentation, managing campaigns,
and gathering data, these tasks can be easily automated using Python, creating more time for
marketers to focus on other critical initiatives. So to dig a little deeper, we'll
speak more on campaign management. In the marketing world campaigns consist of
a variety of channels and tactics, including SEO optimization, Google ads, email, video, and
social media. Using Python, marketers can develop custom marketing campaigns to integrate and manage
each of those components. And when campaigns launch, marketers monitor, review, and analyze.
And here is an example from Towards Data Science of Python pulling information into a spreadsheet.
Here you can see impressions, budgets, region, and whether the ads are showing on
Facebook, Instagram, Google search, or Google display. Therefore, marketers who know
Python can develop programs that will monitor, review, and analyze various campaign
elements to help them make the right changes. And marketers gather data from multiple sources.
Analyzing and processing data from each source can become quite repetitive, but creating custom tools
to generate data from each makes data collection a breeze. So, enter Python. Examples of data a
marketer can collect using Python include: changes in url ranking, collection of email responses,
changes in competitor product or services, gathering survey data. So here's an example of
Python pulling information into a spreadsheet to view landing page performance. And here's the
code that you would type in to pull data from the table. And for a better visual, here's the code
typed out. In green is what you would change, such as the file name, the name of the sheet
you are wanting to pull data from, in the rows and columns you want to gather. And since we are
wanting to pull data for landing pages, sessions, percent of new sessions, and
new users, here is the result. The original table included bounce rate, gold
conversion rate, and other gold metrics we want to ignore. So here is Python reading the code and
only providing the information we want to analyze. In digital marketing, data is useless
if it's not meaningful or accurate. But using Python can help marketers gather
data and analyze it efficiently and accurately. And Python is easy to learn. It is understandable
and simple. It's easy to learn because of its clear syntax and legibility. It's also great
for those with little to no coding experience. For instance, look at the difference with
the following code to print “hello world” in Java in Python. But don't think because
Python is easy that it's a wimpy language. Python is actually incredibly powerful. There's
a reasons why companies like Google, Drobox, Spotify, and Netflix use it. Python provides many
automation opportunities for low-level tasks which we usually spend several hours undertaking. Python
gives us the ability to better understand data and make data-driven decisions. And these
decisions will enable us to provide better insights for our clients and more confidence
in the recommendations that we implement. So the answer to the question - how
long does it take to learn Python? It really depends on what your goals are.
And you can learn the basics in as little as a week or two. And having a solid grasp of the
basics, such as the variables, the functions, and the if-else statements can be enough to help you
solve problems at work or write simple scripts. And if you're a marketer who would like to
analyze Google Analytics data more rigorously, you can learn the fundamental syntax
of Python and the pandas techniques, you need a matter of weeks. But if you're
learning Python from scratch, you need at least a few months. But to be clear - you could
probably spend a lifetime learning Python, because coding is always changing. And just like
any other skill, you’ve got to keep doing it. And now that you understand the Python basics,
I will hand it over to Katelyn, who will cover more in-depth examples of Python in action
and how to get started with Python. All right, thank you very much Amanda. So as she just said,
we're going to jump right in to a bunch of really cool things you can do with Python. We're actually
going to show some really cool examples as well. But, before we get there, let's talk a little bit
about why you would choose Python over a pre-built tool. So you've given a bunch of different cool
things that Python can do. How do you know when it's the right time to build a tool in Python,
versus just using something that exists already? So here's a great example. A good way to think
about it is - does this tool that you want to do things, whether it does this thing that you want
to do, already exist? For example, I can build an awesome tool in Python that takes data, pulls data
from Google Analytics, pulls data from Google ads, pulls data from a spreadsheet that I’m adding, and
pushes it all out into one easy place to read and see all this data in one place. Or I could use
Google Data Studio that already exists and does that pretty well on its own. And it's free. So,
kind of looking at the gaps in the current tool set that you have, and what are some things that
you maybe want to do that you can't do right now, or can't do easily right now. That's where Python
can really jump in and be a really powerful tool. Another thing is - what would that pre-built tool
cost? Maybe you don't have a tool to do something right now, and all of the tools out there that
do it are really expensive. Now, sometimes they're expensive for a reason. Sometimes they
need to be expensive because they need to be incredibly powerful, and they need to do all
this analysis that you just can't do in Python. But sometimes, maybe you just need a portion
of that tool to do what you want to do. Maybe you just need a little piece that can do just
a little bit of data analysis to get you a good chunk of the way there on your own. If Python
is able to accomplish that, which often it is if it's data analysis, it might be a great tool to
jump into and build yourself. Save a little money. The other thing, just to make sure, is to double
check if Python is capable of doing what you need it to do. So like Amanda said, Python's not
going to be, unless you're really good at AI and figuring out, you know, this kind of natural
language processing, Python usually isn't going to be writing ad copy or building
out email templates or things like that. But it can build really great powerful tools.
So, keep in mind, you know, if you need a tool, or if you need an entire factory to do all this
building for you. If you need the whole factory, if you need all these different things, it might
be good to see if there's a tool already built to be able to accomplish it. But in many cases,
Python is an awesome tool to build and to create to meet those needs. As Amanda was explaining,
there's a lot of reasons why you might want to use Python. And often those pre-made tools may
not even be available to do what you want to do. You can build something that's custom to
exactly what you need. You don't have to pay for this huge suite of tools when you just need
one little bit of data analysis that would dramatically improve the way that you're doing
your marketing and your analysis. And again it's custom - you can build something that specifically
meets exactly what you need it to do, which can be really great and really powerful. Because how
often can we say we have a custom built marketing tool that we've made ourselves? It's pretty cool.
So, let's give some examples of that. So, we have a couple tools here at Northwoods that we've
developed using Python in our marketing team that we can use to help do various different tasks and
help us to do a lot of different cool things that we couldn't do before. The first one that we're
going to look at, and we're going to see these in action in just a couple of minutes, is a web page
word counter. So this is a tool that will go out, it will give it some web pages, it'll scrape those
web pages, take down all the words on those pages, and then count them up so we can do some analysis
with that. We'll show that in a little bit. So in this case, this is a tool that adds to our suite
of analysis tools. This is something that we don't have any tools in our marketing arsenal right now
that can do that. So this is kind of filling a gap and helping us to be even more strategic
in the recommendations that we're making. The other tool is more of a utility. So one
thing that Python's really great at is combining different systems together that maybe don't
naturally work together on their own. So in this case, like a lot of people I think, we build lots
of PowerPoints here at Northwoods, especially our digital team. And there's a few different things
we do with PowerPoints that are very repetitive. We're always entering certain data from Google
Analytics for example into PowerPoint. So this tool takes a few of those things that we're always
entering from Google Analytics into PowerPoint and does it automatically, so that we don't have
to go and manually take that data from Google Analytics. We can enter some data into Python and
it will do all of the heavy lifting for us. So one tool that adds some additional kind of strategy
analysis, and one tool that makes things just a little bit more convenient. We're going to see
them both. Let's start with the web scraping tool. So the goal of this tool was to build something
that provides word counts for web pages. So what I mean by that is, we wanted something that we
could give a single web page, an entire site full of web pages, and it would tell us how many
times we said a certain word. Would basically go through, count up all the words, and say - this
site, the most commonly used word on this site was “blank.” So we can get an idea of what words that
the website is using to describe their services, what features are the most important to them,
what are they talking about the most. And we can use that for some of our
own analysis. Python is great at this because of a couple things. One is that I
think as we've kind of said a bunch already it is built for data analysis. There is so much
you can do with data analysis in Python, it's really meant to kind of take all that data
and do all of that kind of heavy lifting for us. The other is that there's a great Python library
for it. Manufactured on the pandas library, a little bit earlier, but another library that can
be really helpful is one called beautiful soup, which I think is just a very fun name. And it's
basically meant to do this kind of work. It's meant to do web scraping and you can kind of take
that web scrape and do some analysis with it. All these Python libraries are as easy as kind of
installing them onto your kind of Python system, I won't get into the details of how that works,
and then just inserting them into the code, and then they just kind of work. They're really
nice - kind of like plug-ins on wordpress, where that functionality is already built
for you, and you can just kind of do your own analysis using that functionality. It
gets you very close to getting started, and it's really great. So. One thing to keep in
mind with this is that a lot of these tools kind of work in conjunction with other things we might
already have. So for example, with this tool, you'll see in a second that we need to give it
the web pages to scrape. Obviously it can't kind of read our minds - what web pages we want. And we
can do it with just a single web page. That's what we're going to do today, just to make it easy.
But a lot of times we want to scrape an entire website. We want to say - here are all of the
pages on this website. Let's see, let's compare all of them together. Let's see what this company
is saying on their website as a whole rather than just on a single page or in a section of the site.
And we will compare that to a different website, and you know we could go and manually get all of
the URLs from a website, but that doesn't really seem like a great use of our time. So in this
case we're using a tool called Screaming Frog. Another great web scraping tool that doesn't quite
do this functionality, gives us some other really great SEO data instead, but we use that tool to
get all the web pages. So just keep in mind that, you know, you don't have to build everything in
Python, you can kind of use some of the data you have from an existing tool, and then just use that
data to kind of build your own tool from there. So how this tool is going to work, and I’m going
to show it in a second, but just to kind of get an idea of what's going on, is that we're going
to give it a series of web pages. One page, multiple pages, whatever we'd like. We're going to
basically save all those in an excel file ahead of time and we're just going to say – go, on the
Python file. And what's going to pop up is it's going to give a little message that says - okay
I’m going and I’m saving this website copy now. And as it's doing that, it's going to those web
pages we've given it in excel, and it is scraping all of the html from those pages, so not just the
copy, but all of the other kind of code that makes that page work. From there it's going to keep
going, and it's going to start cleaning up that. So it's going to take out all the html, it's going
to take all the punctuation, it's going to get us so that we just have the words and that is it.
From there it's going to take those words and it's going to list them all out. So words are going
to be repeated, but we're going to have a list of just the words on the page. We're not going to
see any of this happening, so it's all happening behind the scenes in Python, but just so you now
kind of know like what it's doing as it's going. Once it has that list of words, it's going to
start cleaning up that list of words. So it's going to take out some of those filler words
- the so's, the these, is, it, that. We don't really care how many times they say the words
“the” on their website. We care how many times does they say the word “feature” or “schedule” or
“demo” or stuff like that. From there it's going to take that big list and it's going to start
counting. It's going to say - oh I see the word, in this case we're using our content management
system, Titan CMS, we’re using the home page for that site. So it's going to say – oh, there's the
word Titan. There's the word Titan again. We've got two now, so on and so forth, until it has
a count of all of the words that are the same. And then Python's - we're going to finally see
something again in Python - it's going to say your csv is complete, go open your file, and we'll
go and have our answer. So let's see in action. So one second, I’m going to actually bring up Python
here. This is the one we want. All right, so this is just a folder of Python files, doesn't mean
too much but we're going to see something first. So first thing we want to look at is this
spreadsheet here. So this is where we're putting in all of those URLs. So in this case you can see
we've got one Titan cms.com, but we could have as many as we want. I’ve done this with 500 URLs I
think is the most I’ve tried, and it worked, it just took longer, so we're keeping it simple for
today just to speed things up a little bit. The other thing we're looking at is nothing! This is
that final word count file. This is where Python is going to take all of the words that it's doing
and it's going to give us an output here. So this is more so just showing the magic trick of it. Now
you don't see it, but in a little bit you will. So just keep in mind - final word count, empty for
now, but it's going to not be empty in a minute. So from there we've got those nice and closed to
make sure this works and we're going to open up Python. So you've got Python here. And, you know,
all sorts of code in here, but we're just going to let it run. So let's run this module. Let's
see here it is going? There we go, all right. So we see Python is starting to go after it starts
gets warmed up a little bit. Here it's going to say saving. Sometimes it likes to run kind of
slow when we're kind of live streaming this, so bear with it. Normally it runs pretty quick. There
we go save a website copy. Look - simple as that. So as you can see, all of this is stuff that we
just included ourselves, it's mainly just to know if something breaks. I like to recommend kind of
putting in little messages that pop up so that if something doesn't pop up you know where something
went wrong. But in this case we're good! We got “saving website copy,” it counted up the words, it
cleaned up the same list of words, and now our csv is ready. So let's close it down, let's go back to
our folder, and that final word count suddenly was updated a minute ago so that's a good sign.
So let's open up that final word count and hopefully our magic trick works.
There we go! So this was empty before, now there's some words in here. So we can see
the words “enterprise” “content “management.” Makes sense - it's an enterprise level content
management system. Makes sense we say those words. And our tool’s, you know, obviously kind
of always learning, always cleaning it up. Right now we've got a couple of weird code
things that pop in there, but in general it works really well. We can see all this stuff here
and we suddenly have a really good idea of what words that website is using. So kind of
hopping back to our presentation here, how we kind of use that going forward then
is we can build charts. We can say all right, on the Titan CMS website let's organize all of
that data Python just gave us and we can see that the words we say the most often on the Titan CMS
website - this is the website as a whole now, but - are Titan and CMS. Pretty straightforward.
But we can also see that we're saying the words “content.” We're saying “data” because data is a
really powerful feature. We can see “scheduling a demo” we can see “features” “support” and
we can compare that to another competitor’s website and say – well, what words are they using
that we're not using? And what can we learn about their website their services, from that
information? Ao a cool way to see, you know, a different form of data gathering. And data, you
know, data that we wouldn't be able to get without a tool like this that kind of fills that gap
in our arsenal of tools. So just a cool example there of something in action. Let's see another
one. This one like I said is more of the utility. This is - we build a lot of PowerPoints, we
combine data from Google Analytics into that PowerPoint, how can we make that easier so that
we can focus our time on doing fun stuff like data analysis instead of filling out PowerPoints? So in
here our goal is to update a PowerPoint template with data from Google Analytics. So why Python
is great at this is that it's awesome at linking systems together. Especially big systems like
this where they've got pre-built API integrations, they've got Python libraries that work well with
it. A lot of those things are already built, by either actually Google or Microsoft, or by
other people who have done this before us. So we can use those integrations to our advantage to get
us a good chunk of the way there. The one thing to keep in mind with something where you're kind of
linking these other kind of programs is that there is some assembly required, more so than if you're
building just your own tool. You kind of need to set up some of the things with certain Python
libraries, for example there's a pptx Python library, which its description is that it's meant
to create for creating and updating PowerPoint files. Pretty much what we need it to do, which is
perfect. So we need to kind of get that set up and we need to make sure that PowerPoint is installed
on the machine that you're using because it can't kind of pull it out of nowhere. Google Analytics
also has their Hello Analytics reporting API, which helps us to grab all that data from Google
Analytics. That does take some setup though. You have to enable the API install and set up a Python
library that's associated with it and add the API user to the account you're grabbing data from.
I’m not going to go into any of that today, we're just going to jump to the fun stuff but just know
that there are tons of documentation on Google's various support resources to get that going for
you if this is something you're interested in. And you also make sure you need to know
that you have the view ID for the Google Analytics view that you are pulling data from.
So if you're not familiar with where that is, if you're in your Google Analytics account
there's a little tiny ID here underneath the view that you can pull that from. It's also in
your view settings as the view ID there, so that's going to be really important. Pretty
much anytime you're pulling data from Google Analytics with Python, you are going to want
to have that view ID handy to make it happen. So how this one works is a little bit different.
What's actually going to start off here, and you'll see this in a second, is that it's
going to start asking us a bunch of questions. Actually built into this one is sort of a little
input feature where it can ask us for data, we can give it view IDs and date ranges and stuff,
so that we don’t have to do anything in the code. We have to answer a question in the program and
it's going to put that where it needs to go into the code for us. So it makes it a little bit more
convenient, and makes it a lot easier if you have other members on your team here developing this
to use maybe aren't as comfortable playing around in the code in Python. All they have to do is
answer questions so they don't have to worry about messing with code if it makes them uncomfortable.
From there it's gonna again pop up a little message saying – thanks, your presentation's
being created, we're doing what we need to do. And it's going to form that connection with
the Google Analytics API. It's going to start downloading the data that we are telling it to do
in Python. Just as a little fail-safe it's going to print all of that data into Python, so you're
gonna see all of a sudden it starts scrolling and it's gonna have a bunch of data in there and
we'll be able to see it kind of happening in real time this time. So, less waiting more action I
guess if you wanna call it that. It's then gonna, after it's kind of gathered all the data, it's
to ask another question. What do you want to call your PowerPoint? Because now that it's got all
that data from Google Analytics it needs to have a place to put it. In this case it's going to create
a brand new file. It's not going to be putting the information into an established excel file,
it's going to create a brand new PowerPoint, name it whatever we want it to make with
all of that great data we're pulling in it. So after we give it a name, it's going to open up
PowerPoint, it's going to start putting in all of that data into the fields that we're telling
it to, it's going to save the PowerPoint file as the name we told it, and it's going to say -
your presentation is ready, you know, go check it out. So let's see this one here as I get it up
so again, we've got a nice kind of file of a bunch of folders. In this case we have a template set
up so we've got some empty slides here that have some red areas that we'll have to go and add in
our own analysis or add in some information later. But as you can see right now it's just basically
an empty file with a bunch of empty kind of graphs and empty charts that we can put the data into
with our tool. So I’m going to close this one out but as you can see there's no other bunch of,
you know, excel spreadsheets stuff. But there are no other presentations in this folder, so what's
gonna happen is Python's gonna create a brand new presentation and save it in there with the
name we told it to. So let's hop into the tool. Here we go, let's run it. And just like the last
one it's gonna take a little bit to get started and then it'll start asking us our questions in
blue. There we go, all right. So, hello what is the Google Analytics view ID you would like to
access? So I have the one I want on the outside of my screen. That is what we want, all right. What
is the start date of the most recent date range? So in this case we have it set up to do kind
of a couple different date ranges, and do some analysis and stuff with those date ranges. So I’m
just going to type in a couple that are relatively easy to type so I don't mistype here. So we're
gonna go, we're gonna say our previous date in this range is gonna be the first January 1st of
2020 and we're going to look at it compared to January 1st of 2021. Right, so we're just
gonna do the exact same thing but a year previously. So let's do 2019 to 2020. All right,
so now is where the fun happens. I'll click go. Thanks your presentation is being created.
Look at all that fun data that's being put in there - it's going crazy. And then you can see
here at the bottom, here it says - what would you like to name the final presentation? We’ll be
really original and say “final presentation” just for fun. Grea,t your presentation is saving.
And there we go. Your presentation is ready. So let's hop back into that folder and suddenly
there's this final presentation PowerPoint. So we'll pop in here, we'll see how red stuff is
there, but suddenly we have data! The other cool thing is Python can do some of the calculation
for you. So we did not pull a percent change from Google Analytics, we pulled these various numbers,
but we had Python do some of this. Calculating a percent change and things like that for us. So
another cool thing you can do is some of that data crunching and number analysis stuff in Python so
you're not kind of forced to do it off there. But you always have a little bit of cleanup, you know,
some kind of weird stuff gets pulled sometimes, you know, just depending on the way
you have different things set up. But we can see everything is all filled out
and we're good to go. We've got a great start to kind of keep us going. So a couple different
fun tools. So hopefully that gave you an idea of, you know, how we use Python in our practice. But
also maybe to kind of spur some ideas of things that you could use Python for. What are some
repetitive things that maybe you're constantly copying from one thing to another that you would
like to clean up? Maybe there's something that you've always wanted to know about a website,
or something you've always wanted to know about different ways people are interacting with you
that you could learn with Python. Just kind of think of those cool ways that maybe you don't have
data right now or something that would be really nice to automate and see if there's a way to do it
with Python. Because there probably is at least a way to get you a good chunk of the way there.
So kind of knowing all of this, how do you get started? How do you jump into this? Because I know
as a marketer with not a ton of coding background, up until a couple of years or two ago with Python,
that it can seem really overwhelming to jump in and, you know, deal with all this code. You
know, it could be kind of scary as a marketer, if you don't have a background in that to jump
in. But just know that there's a ton of good places to start. Amanda is listening off some
great things to kind of learn about Python to get you started. There's just a few of the various
things that could be helpful to know to get you going. But just know that kind of getting an idea
of the basics, learning about what variables are, learning those if-then statements, the flow
control stuff, getting kind of a - just taking a month and learning the basics can set you
up to build some really really cool tools. A really great thing to do is follow a guide.
Both Amanda and I love Automate the Boring Stuff, which is a really cool Python handbook to help you
get going. Really great tool. There's also a ton of resources on LinkedIn learning. That's formerly
lynda.com if anyone's used that. But tons of great resources about Python there to get you going. And
just because Python is so heavily used in so many different industries, there's just a ton of online
how-to’s, both in the basics as well as in some of these really specific things that you're trying
to do. So use your resources online as well. A big thing to keep in mind though is you don't
need to learn everything to get started. All you need to do is have an understanding of the basics
and then pick a project. Pick something that you want to learn how to do with Python and figure
out what you need to do to make that happen. Really having a step-by-step process to follow
and a step-by-step thing you're trying to do is a great way to learn, you know, those little
individual pieces even better. So map out the process of what you want to happen, kind of
like how I showed with my little diagram, and then just take it one step at a time,
testing as you're going along the way. So for example, with our process here
this first one I had to figure out how, if you put a bunch of URLs into a spreadsheet,
how do we get Python to crawl those pages and take all the uh the content down. It's all I needed
to do to get started. So let's figure out how to do that one piece. And then we'll figure out
the next step, and the next step from there. But taking it one step at a time helps you to really
understand how to do this first piece, and once you have that skill you can apply it to all sorts
of different tools you're making in the future. Also don't forget to use your resources. Just
a simple web search for “Python web scraping” has tons of different how-to's of the way
different people have done that for their various projects. So use the resources out there as you're
getting started, it could be incredibly helpful. Just know that with any new thing you're learning
uh you're gonna make some mistakes. Just learn from learn those. Learn, uh, help you have a
better understanding of the process going forward. So I hope with our presentation today you are
left with the feeling that marketers can code too. And in reality we shouldn't even be saying that.
Marketers should code too! Coding and Python and html, all these different coding languages are
such a huge part of our jobs now, with so much of our focus being on digital marketing. So having
a skill like Python in your arsenal is just going to make you get even more data and make it even
easier for you to do your job going forward. Python is an awesome tool to get started with.
It's really easy to learn. It's really easy to get going. It's an incredibly versatile
tool as we showed. There's a couple, there's just the very versatile ways even we use it to
get us additional data, but also to simplify our processes. It's a great tool for that. And
in addition for marketers, there's just tons of applications we can use it in, from a marketing
perspective. Really think of Python as a gateway. Because it's such an easy tool to learn a very
easy coding language to learn it can help open the door to learning additional coding languages
in the future. Having a better understanding of html and CSS, that are making your web pages look
the way they do. Or the JavaScript that's behind kind of some of the functions and things happening
on your site as well. Having understanding of Python will just make it even easier to understand
these things which are also incredibly important to us as marketers. In addition, it's really a
bridge to your IT team. It helps you to kind of speak the same language as your IT team and have
an understanding of at least a little bit of what they do on their side. And what we've seen is
that really having a strong connection between marketing and IT is just a great way to make sure
that everyone's goals are being achieved and that, you know, you can kind of work with them
easily to do the things that you need to do for your marketing. The big thing here is that you
just don't need to be an expert. All you need to do is build one tool. Find that tool, the great
starting point that will help you to learn and to build something that will be really helpful for
you. Then all you have to do is build another and another and another as they come up and pretty
soon, not only do you have a great toolbox full of awesome things that can help you to do your job
better, or do things more easily things like that, you've also got a great skill set that can help
you in the long run as a marketer as well. So with that I’m going to open it up to any questions. All
right, thank you Katelyn and Amanda. Let me get into focus here. Hello um yeah we got a couple of
questions. You kind of touched on one of them here towards the end, but let me kind of paraphrase
this question. If you were starting from scratch, in other words you're getting into Python for
the very first time, as you're talking about what's the first training resource you would use
to understand what is possible so they want to dive in? You talked about, you know, googling a
couple of Python phrases and it's going to come up with a lot of results, but if you were starting
again from scratch, what would you go to first? Yeah, I know for me, Amanda I’m not sure if we
use exactly the same tools when we first getting started, but for me I just use Automate the Boring
Stuff. They really walk you through kind of the things you need to know first, and then building
upon that. It's a book as well as I think there's some online resources and things like that. So I
would focus on just those kind of getting to know the basics of Python and once you feel like you've
got a handle on how to do some really basic things then kind of thinking through what it is
you want to do next and how you can do that. I definitely agree with Katelyn. That's what I
started was with that book Automate the Boring Stuff with Python. And just understanding
the variables and the functions, what that all means is really, I think it's step one to
understand the vocabulary when it comes to Python, so then when you do dive into, you know, the
how-to videos you understand what they're saying. You understand how to go about it
more. So definitely start off with more of the definitions I would say and then diving into the
how-to's and actually connecting it yourself. So. Yeah, yeah that's good advice. I think that, you
know, if you don't know anything about Python, it might seem kind of dry, but some of the examples
you showed today, I mean those are pretty powerful and really help, you know, make your job much
more efficient. So it's worthwhile kind of jumping in and trying to figure it out because
you'll discover a lot of fun things to do. We've got another question here - so this pertains
to versions. So how often is Python updated? Like a code base. And are there big differences in
capabilities between versions? So I guess the question really would be do I need to be checking
on an updated version of the code base, and do I need to update that right away based on new and
evolving capabilities of Python? What would you guys suggest and where do you check to make sure
that you're up to date? I will say that Python is changing all the time. As with any coding language
it's always changing. But I don't know Katelyn, do you have a resource of how to check that?
Yeah I typically just keep an eye on the Python. Python.org is their website. I will say
that they don't do - they do little updates, but nothing truly major. I’ve never had something
I’ve built break because of anything with changing in the language. I would say usually that uh
just kind of keeping an eye on that. Maybe subscribing to a couple blogs or things like
that that keep an eye on that just so, you know, if things are changing. But for the most part I
would say it's pretty adaptable, and even when they make changes I’ve never seen an impact on the
things that I’ve already created. If that helps. Yeah great thank you for that. And I think
that's the end of our questions here that have been submitted, so let's go ahead
and wrap up. As I put in the chat today we have developed a knowledge management hub
for you on YouTube which you can subscribe to. You can find our channel at this
particular link and you can find all of our webinars on demand at
nwsdigital.com/webinar-recordings. So thanks again everyone for attending. We hope
you learned a lot about Python this morning. It was a great intro into Python. We hope to see
you at one of our upcoming webinars, and until then have a great day, stay safe, and have a
great rest of your week. Thank you very much.
