Nik Ranger We don’t always have access to first-party data (), buuuut luckily, we can use third-party data to develop our understanding and help derive directional business intelligence and insights (). During the session, we are going to spend 15 minutes going through one method of gleaning insights from a large-scale ranking dataset. Using SEMrush’s organic ranking report* (our sizeable third-party dataset for today), we’re going to: Segment keywords into different subsets using a Jupyter Notebook (including brand/non-brand, SERP intent, intents, question words, etc.), Load the data into Power Bi*, Visualize some data to support identifying insights. If you love content, ranking analyses, or are passionate about efficiencies in reporting, you’re going to enjoy the fifth session of 5 Hours of SEO with Alexis Sanders! *Note: regardless of your toolset (for ranking and BI visualization), this methodology can help you identify insights. This thirty-minute session was hosted by Nik Ranger.

1. @alexisksanders|
1
@alexisksanders|
Scaling ranking data
analysis:
organizing, visualizing, and having some fun with
SEMrush data

2. @alexisksanders| @alexisksanders|
http://bit.ly
/dealing-with-ranking-
data

3. @alexisksanders|
What are we going to do today?
1.Get some SEMrush ranking data
2.Segment keywords using Jupyter
Notebook
3.Load the data into Power Bi
4.Visualize data

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Why?
• 1st-party data > 3rd-party data > no data
• insights > data
• efficiency > work time
• fun > mundane parts of analysis

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Step 1: Get SEMrush data
y’all know how to do
this, let’s use
chipotle!

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Step 2: Open Jupyter Notebook (can also use Google
colabs here)
https://www.anaconda.com/distributi
on/

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Step 3: Import pandas
that’s all we need to
start

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Step 4: pandas.read_csv(data)
everything is easiest if it’s in the same
folder (buuuuuuuut who doesn’t love living outside the box, on the edge)

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Step 5: Setting up f(x) to segment brand
vs. non-
it’s all a
big if-
else
statemen
t (since python doesn’t have
switch statements)

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What sorts  SERP intents (proxy)
shopping ads
adwords
(top &
bottom)
local pack
featured
snippets
people also
ask
knowledge
panel
FAQ
job search site links
video
featured
video
top stories
twitter
reviews
flights
image pack
AMP
video
carousel
featured
image
hotels pack
buy go learn get a
job
navigation
al
other
this can be
organized however
you want

11. @alexisksanders|
Step 6: Setting up a f(x) to segment SERP
intent
we’re
going to
take
what we
used for
brand
and
modify

12. @alexisksanders| @alexisksanders|
… more f(x) creation …

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What sorts  digging into informational
menu
nutrition
recipe
price
deals
rewards
special
days
hours
take-out
delivery
catering
near me
feedback
corporat
e
gift card
can be
whatever
you want

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What sorts  products
bowl burrito taco
quesadill
a
salad

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What sorts  meats
sofritas chicken steak carnitas veggiebarbacoa

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It looks like it’s softritas…

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Step 7: dataframe.to_csv(categorized-data)
export,
will go to
the
folder
noteboo
k is in

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Step 8: Review data in Excel (then make logic
adjustments)
clean the
data,
modify
logic (e.g.,
[chip])

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Step 9: Load data in Power Bi (or other BI visualization
software)
[Get
data] >
load .csv
data

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Example 1: What are the top subdomains?

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And… we make the chart look nice in the style
tab

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Example 2: What are the top SERP intents?

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Very high likelihood of hitting a local pack in the
SERP
not
necessaril
y
surprising
for a local
business,
but hey –
the more
you know

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Very high likelihood of hitting a local pack in the
SERP
and…
non-
brand is
similar
(buuuut
with less
ads)

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Example 3: What are the top intents?

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This one got a bit meta…
it’s the
intent’s I
made up
overlaid with
the SERP
intents
(based on
SERP
features that
appear)

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Example 4: What the top products by meat?

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Chicken bowl for the win…
who knows …
maybe
people want
to know what
sofritas it,
but don’t
necessarily
want to eat
it…

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Example 5: What are top nb keywords & their
intent?

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Example 6: What are top features?

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Example 7: What are top questions?

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Tofu… it’s tofu…
for calories
(cough…and
macros…cough),
Chipotle
actually has a
nutrition
calculator, you
can add your
usual order
(note: I didn’t write you should…)

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Example 8: What are top nb kws by traffic and
cost?

34. @alexisksanders| @alexisksanders|
http://bit.ly
/dealing-with-ranking-
data

35. @alexisksanders| @alexisksanders|
Thank you!