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Using Python + Streamlit To Find Striking Distance Keyword Opportunities

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Using Python + Streamlit To Find Striking Distance Keyword Opportunities

Python is an excellent tool to automate repetitive tasks as well as gain additional insights into data.

In this article, you’ll learn how to build a tool to check which keywords are close to ranking in positions one to three and advises whether there is an opportunity to naturally work those keywords into the page.

It’s perfect for Python beginners and pros alike and is a great introduction to using Python for SEO.

If you’d just like to get stuck in there’s a handy Streamlit app available for the code. This is simple to use and requires no coding experience.

There’s also a Google Colaboratory Sheet if you’d like to poke around with the code. If you can crawl a website, you can use this script!

Here’s an example of what we’ll be making today:

Screenshot from Microsoft Excel, October 2021An Excel sheet documenting onpage keywords opportunites generated with Python

These keywords are found in the page title and H1, but not in the copy. Adding these keywords naturally to the existing copy would be an easy way to increase relevancy for these keywords.

By taking the hint from search engines and naturally including any missing keywords a site already ranks for, we increase the confidence of search engines to rank those keywords higher in the SERPs.

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This report can be created manually, but it’s pretty time-consuming.

So, we’re going to automate the process using a Python SEO script.

Preview Of The Output

This is a sample of what the final output will look like after running the report:

Excel sheet showing and example of keywords that can be optimised by using the striking distance reportScreenshot from Microsoft Excel, October 2021Excel sheet showing and example of keywords that can be optimised by using the striking distance report

The final output takes the top five opportunities by search volume for each page and neatly lays each one horizontally along with the estimated search volume.

It also shows the total search volume of all keywords a page has within striking distance, as well as the total number of keywords within reach.

The top five keywords by search volume are then checked to see if they are found in the title, H1, or copy, then flagged TRUE or FALSE.

This is great for finding quick wins! Just add the missing keyword naturally into the page copy, title, or H1.

Getting Started

The setup is fairly straightforward. We just need a crawl of the site (ideally with a custom extraction for the copy you’d like to check), and an exported file of all keywords a site ranks for.

This post will walk you through the setup, the code, and will link to a Google Colaboratory sheet if you just want to get stuck in without coding it yourself.

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To get started you will need:

We’ve named this the Striking Distance Report as it flags keywords that are easily within striking distance.

(We have defined striking distance as keywords that rank in positions four to 20, but have made this a configurable option in case you would like to define your own parameters.)

Striking Distance SEO Report: Getting Started

1. Crawl The Target Website

  • Set a custom extractor for the page copy (optional, but recommended).
  • Filter out pagination pages from the crawl.

2. Export All Keywords The Site Ranks For Using Your Favorite Provider

  • Filter keywords that trigger as a site link.
  • Remove keywords that trigger as an image.
  • Filter branded keywords.
  • Use both exports to create an actionable Striking Distance report from the keyword and crawl data with Python.

Crawling The Site

I’ve opted to use Screaming Frog to get the initial crawl. Any crawler will work, so long as the CSV export uses the same column names or they’re renamed to match.

The script expects to find the following columns in the crawl CSV export:

"Address", "Title 1", "H1-1", "Copy 1", "Indexability"

Crawl Settings

The first thing to do is to head over to the main configuration settings within Screaming Frog:

Configuration > Spider > Crawl

The main settings to use are:

Crawl Internal Links, Canonicals, and the Pagination (Rel Next/Prev) setting.

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(The script will work with everything else selected, but the crawl will take longer to complete!)

Recommended Screaming Frog Crawl SettingsScreenshot from Screaming Frog, October 2021Recommended Screaming Frog Crawl Settings

Next, it’s on to the Extraction tab.

Configuration > Spider > Extraction

Recommended Screaming Frog Extraction Crawl SettingsScreenshot from Screaming Frog, October 2021Recommended Screaming Frog Extraction Crawl Settings

At a bare minimum, we need to extract the page title, H1, and calculate whether the page is indexable as shown below.

Indexability is useful because it’s an easy way for the script to identify which URLs to drop in one go, leaving only keywords that are eligible to rank in the SERPs.

If the script cannot find the indexability column, it’ll still work as normal but won’t differentiate between pages that can and cannot rank.

Setting A Custom Extractor For Page Copy

In order to check whether a keyword is found within the page copy, we need to set a custom extractor in Screaming Frog.

Configuration > Custom > Extraction

Name the extractor “Copy” as seen below.

Screaming Frog Custom Extraction Showing Default Options for Extracting the Page CopyScreenshot from Screaming Frog, October 2021Screaming Frog Custom Extraction Showing Default Options for Extracting the Page Copy

Important: The script expects the extractor to be named “Copy” as above, so please double check!

Lastly, make sure Extract Text is selected to export the copy as text, rather than HTML.

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There are many guides on using custom extractors online if you need help setting one up, so I won’t go over it again here.

Once the extraction has been set it’s time to crawl the site and export the HTML file in CSV format.

Exporting The CSV File

Exporting the CSV file is as easy as changing the drop-down menu displayed underneath Internal to HTML and pressing the Export button.

Internal > HTML > Export

Screaming Frog - Export Internal HTML SettingsScreenshot from Screaming Frog, October 2021Screaming Frog - Export Internal HTML Settings

After clicking Export, It’s important to make sure the type is set to CSV format.

The export screen should look like the below:

Screaming Frog Internal HTML CSV Export SettingsScreenshot from Screaming Frog, October 2021Screaming Frog Internal HTML CSV Export Settings

Tip 1: Filtering Out Pagination Pages

I recommend filtering out pagination pages from your crawl either by selecting Respect Next/Prev under the Advanced settings (or just deleting them from the CSV file, if you prefer).

Screaming Frog Settings to Respect Rel / PrevScreenshot from Screaming Frog, October 2021Screaming Frog Settings to Respect Rel / Prev

Tip 2: Saving The Crawl Settings

Once you have set the crawl up, it’s worth just saving the crawl settings (which will also remember the custom extraction).

This will save a lot of time if you want to use the script again in the future.

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File > Configuration > Save As

How to save a configuration file in screaming frogScreenshot from Screaming Frog, October 2021How to save a configuration file in screaming frog

Exporting Keywords

Once we have the crawl file, the next step is to load your favorite keyword research tool and export all of the keywords a site ranks for.

The goal here is to export all the keywords a site ranks for, filtering out branded keywords and any which triggered as a sitelink or image.

For this example, I’m using the Organic Keyword Report in Ahrefs, but it will work just as well with Semrush if that’s your preferred tool.

In Ahrefs, enter the domain you’d like to check in Site Explorer and choose Organic Keywords.

Ahrefs Site Explorer SettingsScreenshot from Ahrefs.com, October 2021Ahrefs Site Explorer Settings

Site Explorer > Organic Keywords

Ahrefs - How Setting to Export Organic Keywords a Site Ranks ForScreenshot from Ahrefs.com, October 2021Ahrefs - How Setting to Export Organic Keywords a Site Ranks For

This will bring up all keywords the site is ranking for.

Filtering Out Sitelinks And Image links

The next step is to filter out any keywords triggered as a sitelink or an image pack.

The reason we need to filter out sitelinks is that they have no influence on the parent URL ranking. This is because only the parent page technically ranks for the keyword, not the sitelink URLs displayed under it.

Filtering out sitelinks will ensure that we are optimizing the correct page.

Ahrefs Screenshot Demonstrating Pages Ranking for Sitelink KeywordsScreenshot from Ahrefs.com, October 2021Ahrefs Screenshot Demonstrating Pages Ranking for Sitelink Keywords

Here’s how to do it in Ahrefs.

Image showing how to exclude images and sitelinks from a keyword exportScreenshot from Ahrefs.com, October 2021Image showing how to exclude images and sitelinks from a keyword export

Lastly, I recommend filtering out any branded keywords. You can do this by filtering the CSV output directly, or by pre-filtering in the keyword tool of your choice before the export.

Finally, when exporting make sure to choose Full Export and the UTF-8 format as shown below.

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Image showing how to export keywords in UTF-8 format as a csv fileScreenshot from Ahrefs.com, October 2021Image showing how to export keywords in UTF-8 format as a csv file

By default, the script works with Ahrefs (v1/v2) and Semrush keyword exports. It can work with any keyword CSV file as long as the column names the script expects are present.

Processing

The following instructions pertain to running a Google Colaboratory sheet to execute the code.

There is now a simpler option for those that prefer it in the form of a Streamlit app. Simply follow the instructions provided to upload your crawl and keyword file.

Now that we have our exported files, all that’s left to be done is to upload them to the Google Colaboratory sheet for processing.

Select Runtime > Run all from the top navigation to run all cells in the sheet.

Image showing how to run the stirking distance Python script from Google CollaboratoryScreenshot from Colab.research.google.com, October 2021Image showing how to run the stirking distance Python script from Google Collaboratory

The script will prompt you to upload the keyword CSV from Ahrefs or Semrush first and the crawl file afterward.

Image showing how to upload the csv files to Google CollaboratoryScreenshot from Colab.research.google.com, October 2021Image showing how to upload the csv files to Google Collaboratory

That’s it! The script will automatically download an actionable CSV file you can use to optimize your site.

Image showing the Striking Distance final outputScreenshot from Microsoft Excel, October 2021Image showing the Striking Distance final output

Once you’re familiar with the whole process, using the script is really straightforward.

Code Breakdown And Explanation

If you’re learning Python for SEO and interested in what the code is doing to produce the report, stick around for the code walkthrough!

Install The Libraries

Let’s install pandas to get the ball rolling.

!pip install pandas

Import The Modules

Next, we need to import the required modules.

import pandas as pd
from pandas import DataFrame, Series
from typing import Union
from google.colab import files

Set The Variables

Now it’s time to set the variables.

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The script considers any keywords between positions four and 20 as within striking distance.

Changing the variables here will let you define your own range if desired. It’s worth experimenting with the settings to get the best possible output for your needs.

# set all variables here
min_volume = 10  # set the minimum search volume
min_position = 4  # set the minimum position  / default = 4
max_position = 20 # set the maximum position  / default = 20
drop_all_true = True  # If all checks (h1/title/copy) are true, remove the recommendation (Nothing to do)
pagination_filters = "filterby|page|p="  # filter patterns used to detect and drop paginated pages

Upload The Keyword Export CSV File

The next step is to read in the list of keywords from the CSV file.

It is set up to accept an Ahrefs report (V1 and V2) as well as a Semrush export.

This code reads in the CSV file into a Pandas DataFrame.

upload = files.upload()
upload = list(upload.keys())[0]
df_keywords = pd.read_csv(
    (upload),
    error_bad_lines=False,
    low_memory=False,
    encoding="utf8",
    dtype={
        "URL": "str",
        "Keyword": "str",
        "Volume": "str",
        "Position": int,
        "Current URL": "str",
        "Search Volume": int,
    },
)
print("Uploaded Keyword CSV File Successfully!")

If everything went to plan, you’ll see a preview of the DataFrame created from the keyword CSV export. 

Dataframe showing sucessful upload of the keyword export fileScreenshot from Colab.research.google.com, October 2021Dataframe showing sucessful upload of the keyword export file

Upload The Crawl Export CSV File

Once the keywords have been imported, it’s time to upload the crawl file.

This fairly simple piece of code reads in the crawl with some error handling option and creates a Pandas DataFrame named df_crawl.

upload = files.upload()
upload = list(upload.keys())[0]
df_crawl = pd.read_csv(
    (upload),
        error_bad_lines=False,
        low_memory=False,
        encoding="utf8",
        dtype="str",
    )
print("Uploaded Crawl Dataframe Successfully!")

Once the CSV file has finished uploading, you’ll see a preview of the DataFrame.

Image showing a dataframe of the crawl file being uploaded successfullyScreenshot from Colab.research.google.com, October 2021Image showing a dataframe of the crawl file being uploaded successfully

Clean And Standardize The Keyword Data

The next step is to rename the column names to ensure standardization between the most common types of file exports.

Essentially, we’re getting the keyword DataFrame into a good state and filtering using cutoffs defined by the variables.

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df_keywords.rename(
    columns={
        "Current position": "Position",
        "Current URL": "URL",
        "Search Volume": "Volume",
    },
    inplace=True,
)

# keep only the following columns from the keyword dataframe
cols = "URL", "Keyword", "Volume", "Position"
df_keywords = df_keywords.reindex(columns=cols)

try:
    # clean the data. (v1 of the ahrefs keyword export combines strings and ints in the volume column)
    df_keywords["Volume"] = df_keywords["Volume"].str.replace("0-10", "0")
except AttributeError:
    pass

# clean the keyword data
df_keywords = df_keywords[df_keywords["URL"].notna()]  # remove any missing values
df_keywords = df_keywords[df_keywords["Volume"].notna()]  # remove any missing values
df_keywords = df_keywords.astype({"Volume": int})  # change data type to int
df_keywords = df_keywords.sort_values(by="Volume", ascending=False)  # sort by highest vol to keep the top opportunity

# make new dataframe to merge search volume back in later
df_keyword_vol = df_keywords[["Keyword", "Volume"]]

# drop rows if minimum search volume doesn't match specified criteria
df_keywords.loc[df_keywords["Volume"] < min_volume, "Volume_Too_Low"] = "drop"
df_keywords = df_keywords[~df_keywords["Volume_Too_Low"].isin(["drop"])]

# drop rows if minimum search position doesn't match specified criteria
df_keywords.loc[df_keywords["Position"] <= min_position, "Position_Too_High"] = "drop"
df_keywords = df_keywords[~df_keywords["Position_Too_High"].isin(["drop"])]
# drop rows if maximum search position doesn't match specified criteria
df_keywords.loc[df_keywords["Position"] >= max_position, "Position_Too_Low"] = "drop"
df_keywords = df_keywords[~df_keywords["Position_Too_Low"].isin(["drop"])]

Clean And Standardize The Crawl Data

Next, we need to clean and standardize the crawl data.

Essentially, we use reindex to only keep the “Address,” “Indexability,” “Page Title,” “H1-1,” and “Copy 1” columns, discarding the rest.

We use the handy “Indexability” column to only keep rows that are indexable. This will drop canonicalized URLs, redirects, and so on. I recommend enabling this option in the crawl.

Lastly, we standardize the column names so they’re a little nicer to work with.

# keep only the following columns from the crawl dataframe
cols = "Address", "Indexability", "Title 1", "H1-1", "Copy 1"
df_crawl = df_crawl.reindex(columns=cols)
# drop non-indexable rows
df_crawl = df_crawl[~df_crawl["Indexability"].isin(["Non-Indexable"])]
# standardise the column names
df_crawl.rename(columns={"Address": "URL", "Title 1": "Title", "H1-1": "H1", "Copy 1": "Copy"}, inplace=True)
df_crawl.head()

Group The Keywords

As we approach the final output, it’s necessary to group our keywords together to calculate the total opportunity for each page.

Here, we’re calculating how many keywords are within striking distance for each page, along with the combined search volume.

# groups the URLs (remove the dupes and combines stats)
# make a copy of the keywords dataframe for grouping - this ensures stats can be merged back in later from the OG df
df_keywords_group = df_keywords.copy()
df_keywords_group["KWs in Striking Dist."] = 1  # used to count the number of keywords in striking distance
df_keywords_group = (
    df_keywords_group.groupby("URL")
    .agg({"Volume": "sum", "KWs in Striking Dist.": "count"})
    .reset_index()
)
df_keywords_group.head()
DataFrame showing how many keywords were found within striking distanceScreenshot from Colab.research.google.com, October 2021DataFrame showing how many keywords were found within striking distance

Once complete, you’ll see a preview of the DataFrame.

Display Keywords In Adjacent Rows

We use the grouped data as the basis for the final output. We use Pandas.unstack to reshape the DataFrame to display the keywords in the style of a GrepWords export.

DataFrame showing a grepwords type-view of keywords laid out horizontallyScreenshot from Colab.research.google.com, October 2021DataFrame showing a grepwords type-view of keywords laid out horizontally
# create a new df, combine the merged data with the original data. display in adjacent rows ala grepwords
df_merged_all_kws = df_keywords_group.merge(
    df_keywords.groupby("URL")["Keyword"]
    .apply(lambda x: x.reset_index(drop=True))
    .unstack()
    .reset_index()
)

# sort by biggest opportunity
df_merged_all_kws = df_merged_all_kws.sort_values(
    by="KWs in Striking Dist.", ascending=False
)

# reindex the columns to keep just the top five keywords
cols = "URL", "Volume", "KWs in Striking Dist.", 0, 1, 2, 3, 4
df_merged_all_kws = df_merged_all_kws.reindex(columns=cols)

# create union and rename the columns
df_striking: Union[Series, DataFrame, None] = df_merged_all_kws.rename(
    columns={
        "Volume": "Striking Dist. Vol",
        0: "KW1",
        1: "KW2",
        2: "KW3",
        3: "KW4",
        4: "KW5",
    }
)

# merges striking distance df with crawl df to merge in the title, h1 and category description
df_striking = pd.merge(df_striking, df_crawl, on="URL", how="inner")

Set The Final Column Order And Insert Placeholder Columns

Lastly, we set the final column order and merge in the original keyword data.

There are a lot of columns to sort and create!

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# set the final column order and merge the keyword data in

cols = [
    "URL",
    "Title",
    "H1",
    "Copy",
    "Striking Dist. Vol",
    "KWs in Striking Dist.",
    "KW1",
    "KW1 Vol",
    "KW1 in Title",
    "KW1 in H1",
    "KW1 in Copy",
    "KW2",
    "KW2 Vol",
    "KW2 in Title",
    "KW2 in H1",
    "KW2 in Copy",
    "KW3",
    "KW3 Vol",
    "KW3 in Title",
    "KW3 in H1",
    "KW3 in Copy",
    "KW4",
    "KW4 Vol",
    "KW4 in Title",
    "KW4 in H1",
    "KW4 in Copy",
    "KW5",
    "KW5 Vol",
    "KW5 in Title",
    "KW5 in H1",
    "KW5 in Copy",
]

# re-index the columns to place them in a logical order + inserts new blank columns for kw checks.
df_striking = df_striking.reindex(columns=cols)

Merge In The Keyword Data For Each Column

This code merges the keyword volume data back into the DataFrame. It’s more or less the equivalent of an Excel VLOOKUP function.

# merge in keyword data for each keyword column (KW1 - KW5)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW1", right_on="Keyword", how="left")
df_striking['KW1 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW2", right_on="Keyword", how="left")
df_striking['KW2 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW3", right_on="Keyword", how="left")
df_striking['KW3 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW4", right_on="Keyword", how="left")
df_striking['KW4 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)
df_striking = pd.merge(df_striking, df_keyword_vol, left_on="KW5", right_on="Keyword", how="left")
df_striking['KW5 Vol'] = df_striking['Volume']
df_striking.drop(['Keyword', 'Volume'], axis=1, inplace=True)

Clean The Data Some More

The data requires additional cleaning to populate empty values, (NaNs), as empty strings. This improves the readability of the final output by creating blank cells, instead of cells populated with NaN string values.

Next, we convert the columns to lowercase so that they match when checking whether a target keyword is featured in a specific column.

# replace nan values with empty strings
df_striking = df_striking.fillna("")
# drop the title, h1 and category description to lower case so kws can be matched to them
df_striking["Title"] = df_striking["Title"].str.lower()
df_striking["H1"] = df_striking["H1"].str.lower()
df_striking["Copy"] = df_striking["Copy"].str.lower()

Check Whether The Keyword Appears In The Title/H1/Copy and Return True Or False

This code checks if the target keyword is found in the page title/H1 or copy.

It’ll flag true or false depending on whether a keyword was found within the on-page elements.

df_striking["KW1 in Title"] = df_striking.apply(lambda row: row["KW1"] in row["Title"], axis=1)
df_striking["KW1 in H1"] = df_striking.apply(lambda row: row["KW1"] in row["H1"], axis=1)
df_striking["KW1 in Copy"] = df_striking.apply(lambda row: row["KW1"] in row["Copy"], axis=1)
df_striking["KW2 in Title"] = df_striking.apply(lambda row: row["KW2"] in row["Title"], axis=1)
df_striking["KW2 in H1"] = df_striking.apply(lambda row: row["KW2"] in row["H1"], axis=1)
df_striking["KW2 in Copy"] = df_striking.apply(lambda row: row["KW2"] in row["Copy"], axis=1)
df_striking["KW3 in Title"] = df_striking.apply(lambda row: row["KW3"] in row["Title"], axis=1)
df_striking["KW3 in H1"] = df_striking.apply(lambda row: row["KW3"] in row["H1"], axis=1)
df_striking["KW3 in Copy"] = df_striking.apply(lambda row: row["KW3"] in row["Copy"], axis=1)
df_striking["KW4 in Title"] = df_striking.apply(lambda row: row["KW4"] in row["Title"], axis=1)
df_striking["KW4 in H1"] = df_striking.apply(lambda row: row["KW4"] in row["H1"], axis=1)
df_striking["KW4 in Copy"] = df_striking.apply(lambda row: row["KW4"] in row["Copy"], axis=1)
df_striking["KW5 in Title"] = df_striking.apply(lambda row: row["KW5"] in row["Title"], axis=1)
df_striking["KW5 in H1"] = df_striking.apply(lambda row: row["KW5"] in row["H1"], axis=1)
df_striking["KW5 in Copy"] = df_striking.apply(lambda row: row["KW5"] in row["Copy"], axis=1)

Delete True/False Values If There Is No Keyword

This will delete true/false values when there is no keyword adjacent.

# delete true / false values if there is no keyword
df_striking.loc[df_striking["KW1"] == "", ["KW1 in Title", "KW1 in H1", "KW1 in Copy"]] = ""
df_striking.loc[df_striking["KW2"] == "", ["KW2 in Title", "KW2 in H1", "KW2 in Copy"]] = ""
df_striking.loc[df_striking["KW3"] == "", ["KW3 in Title", "KW3 in H1", "KW3 in Copy"]] = ""
df_striking.loc[df_striking["KW4"] == "", ["KW4 in Title", "KW4 in H1", "KW4 in Copy"]] = ""
df_striking.loc[df_striking["KW5"] == "", ["KW5 in Title", "KW5 in H1", "KW5 in Copy"]] = ""
df_striking.head()

Drop Rows If All Values == True

This configurable option is really useful for reducing the amount of QA time required for the final output by dropping the keyword opportunity from the final output if it is found in all three columns.

def true_dropper(col1, col2, col3):
    drop = df_striking.drop(
        df_striking[
            (df_striking[col1] == True)
            & (df_striking[col2] == True)
            & (df_striking[col3] == True)
        ].index
    )
    return drop

if drop_all_true == True:
    df_striking = true_dropper("KW1 in Title", "KW1 in H1", "KW1 in Copy")
    df_striking = true_dropper("KW2 in Title", "KW2 in H1", "KW2 in Copy")
    df_striking = true_dropper("KW3 in Title", "KW3 in H1", "KW3 in Copy")
    df_striking = true_dropper("KW4 in Title", "KW4 in H1", "KW4 in Copy")
    df_striking = true_dropper("KW5 in Title", "KW5 in H1", "KW5 in Copy")

Download The CSV File

The last step is to download the CSV file and start the optimization process.

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df_striking.to_csv('Keywords in Striking Distance.csv', index=False)
files.download("Keywords in Striking Distance.csv")

Conclusion

If you are looking for quick wins for any website, the striking distance report is a really easy way to find them.

Don’t let the number of steps fool you. It’s not as complex as it seems. It’s as simple as uploading a crawl and keyword export to the supplied Google Colab sheet or using the Streamlit app.

The results are definitely worth it!

More Resources:


Featured Image: aurielaki/Shutterstock

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SEO

Top 6 Free Survey Maker Tools For Marketers

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Top 6 Free Survey Maker Tools For Marketers

The number of online surveys has risen dramatically in the past decade, according to the Pew Research Center.

From short social media polls to lengthy feedback forms, it’s never been easier to survey your target audience and find out what exactly they’re thinking.

When it comes to free survey makers, you have plenty of options to choose from.

That’s the good news.

The bad news is you have to wade through your options to figure out the best survey tool for you.

In this article, I’ve done that dirty work for you.

Below I outline the top six free survey makers, with a simple bulleted list of their pros and cons, so you can quickly select the best one for your needs.

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But first up, the caveats.

What You’re Missing With Free Survey Makers

When something’s free, there’s usually a catch. The same goes for free survey makers.

Free survey tools, or the free plan offered by a paid survey tool, often come with the following limitations:

  • Limited export options. You may not be able to export your survey data for review in Excel or Google Sheets. There may be a PDF-only export option or no export ability at all.
  • Limited analytics. Free survey tools often skimp on the analytics. You may be left to your own pivot tables and Excel expertise if you want to create anything fancy from your survey data.
  • Limited survey functionality. This runs the gamut, from a limit on how many respondents or questions you can have per survey, to only allowing so many question types (e.g., multiple-choice, long-form, etc.).
  • Limited extra perks. By perks, I mean those other features that make software from good to great. With survey makers, that might mean easy-to-access support, the ability to embed surveys in email or webpages, multiple user accounts, or integration with other email marketing or CRM software.
  • No branding. Free survey makers give you their tools for free. In return, you provide them with free brand awareness. Don’t expect to be able to swap out their logo for your own. You’ll probably be stuck with their branding, along with a prominent link to their site throughout the survey or on the thank you page (or both).

If any of the above is a dealbreaker for you, you should plan to drop a little dough on a paid survey tool. That’s why I’ve also included the starting price for all six of the tools featured below.

In case you end up having to upgrade later, it’s easier to do so from a tool you’re already familiar with.

Top 6 Free Survey Tools

Without further ado, I present the best free survey makers you’ll find today. These are listed in no particular order.

1. Google Forms

Screenshot by author, June 2022

Do you live and die by your Google Drive?

Great news: Google also offers free survey software via Google Forms.

Alright, I know I just said these were presented in no particular order, but I’ll openly admit Google Forms is my personal favorite. Just look at all of the features they include in their free plan!

All you need is a free Google account to get started.

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Here’s what’s included in the free plan:

  • Unlimited surveys.
  • Unlimited questions.
  • Unlimited responses.
  • Export to Google Sheets.
  • Survey logic (ability to skip or trigger questions).
  • Ability to embed images and YouTube videos.
  • Ability to embed the survey on your website and share to social media.
  • Survey analytics, updated in real-time.
  • Integration with Google Docs, Sheets, Slides.
  • Unlimited collaborators.
  • Customizable survey templates.
  • Free branding.

What’s missing from the free plan:

  • Enhanced security and collaboration options.
  • Integration with your existing Google Workplace account.

Price: Completely free. Google Workplace pricing starts at $6 per user per month.

Best for: Anyone and everyone, for business or casual use.

2. SurveyMonkey

surveymonkeyScreenshot by author, June 2022

SurveyMonkey is the online survey tool. Established in 1999, it’s still the most well-known online survey software.

Despite the limitations of its free plans, SurveyMonkey continues to be popular thanks to its intuitive interface and brand recognition. Notable clients include Allbirds, Tweezerman, and Adobe.

One nice perk is that you can test out any of the paid features with your free plan. (You just won’t be able to actually use it in your live survey until you pay up.)

Here’s what’s included in the free plan:

  • Unlimited surveys.
  • 10 questions.
  • 15 question types.
  • 100 responses per survey.
  • Over 250 customizable survey templates.
  • Ability to embed the survey on your website.
  • Mobile app.
  • One user.

What’s missing from the free plan:

  • Unlimited questions, question types, and responses.
  • Data exports – this is a biggie!
  • Custom branding.
  • Survey logic (ability to skip or trigger questions).
  • Team collaboration.
  • Advanced security (single sign-on, HIPAA compliance).
  • A/B testing.

Price: Freemium. Paid plans start at $16 per month for individuals, $25 for teams.

Best for: Those who want a tried-and-true survey maker with all the features you could ask for.

3. Typeform

typeformScreenshot by author, June 2022

Many online survey tools are designed for the general public.

Readers of Search Engine Journal will be happy to hear that there’s a survey tool created just for us. Typeform was built specifically with marketers, UX researchers, and business owners like us in mind.

Here’s what’s included in the free plan:

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  • Unlimited surveys.
  • 10 questions per survey.
  • 10 responses per month.
  • Basic question types.
  • Basic reporting and analytics
  • Ability to embed the survey on your website.
  • Integrations with MailChimp, HubSpot, Trello, Google Sheets, Zapier, and more.

What’s missing from the free plan:

  • Unlimited questions and responses.
  • Custom thank you screen.
  • Custom branding.
  • Survey logic (ability to skip or trigger questions).
  • Team collaboration.
  • Ability to accept payment.
  • Ability for survey respondents to upload files.
  • Integration with Facebook pixel and Google Tag Manager.

Price: Freemium. Paid plans start at $29 per month.

Best for: Enterprise users, UX researchers, and marketers hoping to track customer behavior.

4. Zoho Survey

zoho surveyScreenshot by author, June 2022

Zoho Survey is part of the same Zoho suite of apps that caters to sales, HR, IT, finance, and virtually any kind of business user you can think of.

Given their tenure creating SaaS software for business, their survey tool is just as robust as you might expect. Customers include big names like Netflix, Amazon, Facebook, and Change.org.

Here’s what’s included in the free plan:

  • Unlimited surveys.
  • 10 questions per survey.
  • 100 responses per survey.
  • Ability to embed surveys in email or website, or share to social media.
  • Export to PDF.
  • 250 survey templates.
  • Password protection and HTTPS encryption.
  • One user.

What’s missing from the free plan:

  • Unlimited questions and responses.
  • Ability to export to XLS or CSV.
  • Survey logic (ability to skip or trigger questions).
  • Custom branding.
  • Team collaboration.
  • Real-time responses.
  • Multilingual surveys.
  • Integration with Google Sheets, Tableau, Shopify, Zendesk, Eventbrite, and others.

Price: Freemium. Paid plans start at $25 per month.

Best for: Zoho users, or anyone who needs an extra level of security for their surveys.

5. Alchemer

alchemer survey makerScreenshot by author, June 2022

Alchemer is an advanced survey maker developed for the enterprise client.

Paid features include custom coding so you can customize every single element of your survey, from the survey URL to the form logic.

They stand out among free survey makers for being one of the few (besides Google Forms) to offer unlimited questions and Excel exports in their free plan. Clients include Disney, Salesforce, Verizon, and The Home Depot.

Here’s what’s included in the free plan:

  • Three surveys at a time.
  • Unlimited questions.
  • 100 responses.
  • 10 question types.
  • Export to Excel.
  • Customizable templates.

What’s missing from the free plan:

  • Unlimited surveys.
  • Unlimited responses.
  • Unlimited question types.
  • Survey logic (ability to skip or trigger questions).
  • Custom branding.
  • Ability to embed surveys in websites.
  • Export to PDF, PowerPoint, or Word.
  • Ability for survey respondents to upload files.
  • Survey analytics and reporting.
  • Ability to accept payment.

Price: Freemium. Paid plans start at $49 per month.

Best for: Enterprise users needing to create long surveys with advanced logic and question types.

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6. Jotform

jotform survey makerScreenshot by author, June 2022

With over 10,000 templates, Jotform takes the cake as the survey maker with the most form templates on our list.

Jotform also stands out for letting you accept payments with the free plan (although you’re limited to 10).

This popular survey maker includes clients as wide-ranging as AMC and Nickelodeon to Redfin and the American Medical Association.

Here’s what’s included in the free plan:

  • Five surveys.
  • 100 questions per survey.
  • 100 responses per survey.
  • Ability to embed surveys in email or website.
  • Export to PDF or Excel.
  • 10,000 survey templates.

What’s missing from the free plan:

  • Unlimited surveys.
  • Unlimited questions and responses.
  • Survey logic (ability to skip or trigger questions).
  • Custom branding.
  • HIPAA compliance.

Price: Freemium. Paid plans start at $29 per month.

Best for: Users who want a template for every kind of survey possible.

Which Survey Tool Will You Use?

There truly is a survey maker for everybody.

The above options are all solid choices. Which one works for you may depend on your organization’s needs and your personal preferences.

Take advantage of the free trials and see which one you like best.

More Resources:

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Featured Image: Prostock-studio/Shutterstock



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