1. The document discusses segmenting, organizing, and visualizing SEMrush ranking data for Chipotle using Jupyter Notebook and Power BI. 2. Key steps include getting SEMrush data, using Python code to segment keywords by brand and intent, exporting the categorized data, and loading it into Power BI for visualization. 3. Examples of visualizations shown include top subdomains, SERP intents, products by meat, and non-brand keywords by traffic and cost.

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

2. @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

3. @alexisksanders|
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/

6. @alexisksanders|
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

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

11. @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])

18. @alexisksanders|
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
necessari
ly
surprisin
g for a
local
business,
but hey
– the
more
you

23. @alexisksanders|
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

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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?

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

34. @alexisksanders| @alexisksanders|
Thank you!