MARKETING
What Is AI Analytics?

Our 2023 Marketing Trends Report found that data-driven marketers will win in 2023. It makes sense, but data analysis can be challenging and time-consuming for many businesses.
Enter AI analytics, a time-saving process that brings marketers the answers they need to create data-driven campaigns. In this post, we’ll discuss:
What is AI analytics?
AI analytics is a type of data analysis that uses machine learning to process large amounts of data to identify patterns, trends, and relationships. It doesn’t require human input, and businesses can use the results to make data-driven decisions and remain competitive.
As with all machine learning, AI analytics gets more precise and accurate over time, especially when trained to learn industry preferences to contextualize results to individual business needs.
AI analytics is sometimes referred to as augmented analytics, which Gartner defines as “The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”
How to Use AI in Data Analytics
AI analytics differs from traditional analytics in that it is machine-led. Its scale is more significant, data processing is faster, and algorithms give accurate outputs.
AI analytics can do what humans do, but be mindful of viewing it as a total replacement. If you use AI in data analytics, consider leveraging it to supplement your team’s capabilities and expertise.
For example, an AI analytics tool can process the results of an A/B test and quickly say which version had the highest ROI and conversion rate. A marketer can take this information, identify exactly what impacted the performance of each version, and apply this information to future marketing practices.
Benefits of Using AI Analytics
The key differences between human-run data analysis and AI analytics are the three main benefits of using AI analytics: scale, speed, and accuracy:
1. Scale
AI analytics tools can leverage large amounts of data at a time. Its scale also brings a competitive advantage, as machines can seek publicly available data from other sources, run comparative tests, and help you learn more about competitor performance and how you measure up.
2. Speed
Machines don’t require the downtime that humans need, so data processing can happen instantaneously. It can simply be fed a data set and left alone to process, learn from, and bring insights.
3. Accuracy
Machine learning algorithms get better at understanding data while processing data, bringing comprehensive and accurate results.
You can also train algorithms on industry language and standards so results are contextually relevant to your business goals.
Some additional benefits include:
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Bias reduction: Algorithms don’t have the confirmation bias or general biases that teams might (unintentionally) have when analyzing data, so results are unbiased.
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New insights: Since the scale of data is much larger than human capabilities, AI analytics can shed light on trends and patterns that might otherwise go unnoticed by human researchers’ limited capabilities.
Business Applications of AI Analytics
Machine learning and AI work together to help businesses make data-driven decisions. Marketers can get deep insights into consumer behavior and marketing performance. Potential applications include:
-
Testing: Run your usual marketing tests and uncover the version(s) most likely to maximize key marketing metrics like ROI and conversions.
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Campaign segmentation: AI tools use data to discover consumer preferences so you can create segmented campaigns to maximize the potential for conversions and ROI.
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SEO: Machine learning algorithms can understand the search intent behind queries and help you learn more about the type of content to create and identify new keyword opportunities.
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eCommerce analytics: Get insight into page conversion rates and discover what might cause shoppers to drop out of the path to purchase.
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Identify problem areas: A big benefit of AI data analytics is uncovering new data points you might not find through your processing. You can discover hidden variables affecting performance and adapt your strategies to address them.
AI analytics is also beneficial to other areas of business, including:
-
Sales forecasting: Teams can use AI analytics to forecast revenue and sales based on historical data.
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Customer experience monitoring: Data helps service teams understand customer satisfaction levels and learn how to build customer loyalty and reduce churn.
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Internal performance: Business leaders can use AI analytics to understand internal team performance, from win rate to customer satisfaction scores, to understand what’s going right and identify opportunities for improvement.
Limitations of AI Analytics
The most significant limitation of AI analytics is that a computer is not a human. While machines can sort through significantly more data in a shorter time, a human knows a business and its processes better than a computer can.
Be mindful of treating AI tools as a replacement for human understanding. Teams can use insights (and will greatly benefit from the insights) alongside their contextual understanding of business needs before making decisions.
The limitation boils down to this: you can’t replicate human understanding and experience, so it’s essential to consider this when leveraging AI tools.
AI Analytics Gives Businesses A Competitive Advantage
Overall, using AI analytics gives businesses a competitive advantage. Machine learning algorithms produce data-driven insights from which marketers can make data-driven decisions.
Take a look at your current data analysis process to see where it fits in, and reap the benefits.
MARKETING
The Secret to Grow Your Business

In today’s digital world where over 50% of the world’s population (Hootsuite) is on social media, leveraging social media for e-commerce marketing is a great idea if you want to grow your business.
Consider this:
According to a 2021 Sprout Social’s the State of Social Media Investment survey, 34% of online consumers say they use social media to learn about products, services, and brands.
In the same survey, 33% said they use social media to discover new products, services, and brands.
Besides, according to Hootsuite’s Global State of Digital 2022 report mentioned above, users spend 2 hours and 27 minutes on average daily on social media:
What’s more?
In 2022, global sales via social media were estimated at $992 billion. Besides, social commerce sales are forecasted to reach approximately $2.9 trillion by 2026.
Seeing all these statistics, it’s clear that using social media for e-commerce is a great idea for promoting your business online.
Still not convinced?
Here are 4 reasons why you should use social media for e-commerce.
1. Helps You Drive Website Traffic
Using social commerce is a great idea if you want to drive traffic to your website.
As mentioned in the statistics above, consumers are using social media to learn about brands and discover new products and services.
E-commerce brands can leverage this huge social audience to drive more traffic to their websites.
The good news is that social media for e-commerce is affordable. You can even drive traffic to your e-commerce website for free.
Here are handy social media tactics to drive traffic to your e-commerce website:
- Research your e-commerce target audience.
- Choose the right social media platforms that are relevant to your e-commerce business.
- Post user-generated content.
- Post valuable content consistently at the right time.
- Collaborate with influencers.
- Target your e-commerce audience with social media ads and PPC ads.
- Utilize your social media and e-commerce data.
- Follow the 80/20 rule.
2. Helps Create Brand Awareness
Social media is one of the most powerful channels for generating buzz around your brand, products, and services while managing business expenses, effectively track finances, and curtailing them thanks to a large number of users it commands.
Right social media strategy help you to increase the brand value and traffic on your ecommerce website.
According to a 2022 State of Inbound Marketing Trends report by HubSpot, 39% of marketers say their primary goal in using social media is to increase brand awareness:
By creating a robust social media marketing strategy, you can boost the visibility of your e-commerce business, thereby increasing brand recognition.
Here are practical tips to build brand awareness for your e-commerce store using social media:
- Ensure you’re using social media networks that your target customers are using.
- Create an advertising budget and stick to it to handle business finance better.
- Demonstrate your brand’s personality and values.
- Deliver valuable content consistently and engage with your audiences.
- Take advantage of trends and breaking news.
- Always track and measure progress.
3. Improves Conversions
The US retail social commerce sales are projected to reach $79.64 billion by 2025:
There’s no doubt that social media marketing can help e-commerce brands improve their conversion rates.
Thus, creating a powerful social media strategy can help you improve conversions for your e-commerce business. In fact, with features like smart links, you can easily drive B2B sales on platforms like LinkedIn too.
To help boost their conversions, Walmart partnered with a US singer Jason Derulo in a live shopping event for which the singer shared a link on Twitter.
Here is how to use social media to boost e-commerce conversions:
- Share user-generated content to empower your customers.
- Improve conversions with influencer marketing.
- Use trending and relevant hashtags.
- Drive authentic engagement.
- Build deeper trust and loyalty with your audience.
- Make it easier for customers to shop for products directly on social media.
- Leverage social media analytics.
4. Provide Customer Service
Take a look at how lululemon responded to a subscriber’s question on Twitter.
The e-commerce brand provided the subscriber with a means to reach out to customer support. And they did so quickly too.
These days, the most popular social media platforms allow customers to purchase products directly without leaving the platform. These platforms work as the most digital marketing tools for the marketers and business owners.
This makes social media an important platform for customer service for your e-commerce business.
Here are useful tips to use social media for e-commerce customer support:
- Reply to all questions, comments, concerns, and feedback.
- Know what to address in public or private.
- Address crucial matters as soon as possible.
- Respond positively to both negative and positive feedback.
Conclusion
There are many incredible benefits of using social media for e-commerce marketing.
So, if you’re not using social media to promote your brand, products, and services online then you’re missing out on a lot of huge business opportunities. In fact, you’re giving your competition the edge.
The key lies in leveraging the right social media marketing strategies and promoting your e-commerce store using them. The right combination can give your brand a lift. So, go ahead and start leveraging these strategies.
MARKETING
Mnemonic Content Strategy Framework Can Spark Conversations

I’m a sucker for mnemonics.
In fact, I remember how to spell it by “Me Nomics Except M nOt N In Case Spelling.”
OK, that’s a lie. But I daresay ChatGPT could never come up with that.
Anyway, one of my favorite idea-remembering devices comes from my hero Philip Kotler. He reduces his perfect definition of marketing to CCDVTP – Create and Communicate Value to a Target at a Profit.”
I lean on that mnemonic device when anyone asks about the best definition of marketing’s function in a business.
However, what makes a great mnemonic like CCDVTP is that each word the letter represents has something deeper behind it. So it’s not just six words – it’s six operating concepts with definitions made easier to remember by just remembering how the six words go together.
A mnemonic device for content strategy
I’ve written about the standard framework for developing or strengthening your content strategy. It’s one of the core modules of a CMI University course. It can be a lot to take in because the framework’s concepts and definitions need to be explained in varying levels of detail.
So, recently, I created a mnemonic device to use in my explanation – the 5 Cs: Coordination and Collaboration produce Content before Containers and make Channels measurable.
5Cs of #ContentStrategy: Coordination and Collaboration produce Content before Containers and make Channels measurable via @Robert_Rose @CMIContent. Click To Tweet
It works as a core or high-level definition of a content marketing strategy. But, like Kotler’s CCDVTP, it also lets me drill into the framework’s five concepts or pressure points. Let me explain:
Coordination
The primary purpose of a content strategy is to develop and manage core responsibilities and processes. In addition, they allow marketing to build and continually assess resource allocation, skill sets, and charters the marketing team needs to make content a business strength.
Most businesses that lack this C struggle with content as a repeatable or measurable approach. As I’ve said, content is everyone’s job in many businesses and no one’s strategy. A key element of a content strategy is a focus on building coordination into how ideas become content and ultimately generate business value.
Most businesses that lack coordination struggle with making #content a repeatable and measurable approach, says @Robert_Rose. Click To Tweet
Collaboration
In many businesses, content is developed in silos, especially with sales and marketing. Sometimes, it may be divided by channel – web, email, and sales teams don’t work together. In other cases, it may be by function – PR, sales, marketing, brand, and demand generation have different approaches.
Content is a team sport. The practitioners’ job is not to be good at content but to enable the business to be good at content. Scalability only happens through an effective, collaborative approach to transforming ideas into content and content into experiences.
Content before containers
As marketers, you are trained to think container first and content second. You start with “I need a web page,” “I need an email,” or “I need a blog post.” Then, your next step is to create content specific to that container.
If you start with “I need a blog post” and then create the #content idea, you’re doing it wrong, says @Robert_Rose via @CMIContent. Click To Tweet
I can’t tell you how many big ideas I’ve seen trapped in the context of a blog post simply because that was how it was conceived. I’ve also seen the reverse – small ideas spun into an e-book or white paper because someone wanted that digital asset.
This pressure point requires reverse thinking about your business’ process to create content. The first step must be to create fully formed ideas (big and small) and then (and only then) figure out which containers and how many might be appropriate.
My test to see whether marketing teams put content before containers is to look at their request or intake form. Does it say, “What kind of content do you need?” and list options, such as email, white paper, e-book, and brochure? Or does it say, “Please explain the idea or story you’d like to develop more fully?”
Channels
I purposely put channels last because they express the kind of content you create. Channels dictate how you ultimately reach the customers and how the customers will access your content. Which or how many of your content channels do you treat as a media company would?
Is your corporate blog truly centered on the audience, or is it centered on your product or brand? Is it a repository where you put everything from news about your product and how to use it to what to expect in the future and how other customers use your product?
What about your social media, website, newsletters, and thought leadership center? What is their purpose and editorial strategy? How do you evolve your content products as your audience changes as a media company does? Without a clear strategy for every channel, the measurement of content becomes guesswork at best.
When you examine your strategic approach to content, I hope the 5Cs mnemonic device helps you have those necessary conversations around coordination, collaboration, content before containers, and channels with the stakeholders in your business.
It’s your story. Tell it well.
HANDPICKED RELATED CONTENT:
Cover image by Joseph Kalinowski/Content Marketing Institute
MARKETING
The Moz Links API: An Introduction

What exactly IS an API? They’re those things that you copy and paste long strange codes into Screaming Frog for links data on a Site Crawl, right?
I’m here to tell you there’s so much more to them than that – if you’re willing to take just a few little steps. But first, some basics.
What’s an API?
API stands for “application programming interface”, and it’s just the way of… using a thing. Everything has an API. The web is a giant API that takes URLs as input and returns pages.
But special data services like the Moz Links API have their own set of rules. These rules vary from service to service and can be a major stumbling block for people taking the next step.
When Screaming Frog gives you the extra links columns in a crawl, it’s using the Moz Links API, but you can have this capability anywhere. For example, all that tedious manual stuff you do in spreadsheet environments can be automated from data-pull to formatting and emailing a report.
If you take this next step, you can be more efficient than your competitors, designing and delivering your own SEO services instead of relying upon, paying for, and being limited by the next proprietary product integration.
GET vs. POST
Most APIs you’ll encounter use the same data transport mechanism as the web. That means there’s a URL involved just like a website. Don’t get scared! It’s easier than you think. In many ways, using an API is just like using a website.
As with loading web pages, the request may be in one of two places: the URL itself, or in the body of the request. The URL is called the “endpoint” and the often invisibly submitted extra part of the request is called the “payload” or “data”. When the data is in the URL, it’s called a “query string” and indicates the “GET” method is used. You see this all the time when you search:
https://www.google.com/search?q=moz+links+api <-- GET method
When the data of the request is hidden, it’s called a “POST” request. You see this when you submit a form on the web and the submitted data does not show on the URL. When you hit the back button after such a POST, browsers usually warn you against double-submits. The reason the POST method is often used is that you can fit a lot more in the request using the POST method than the GET method. URLs would get very long otherwise. The Moz Links API uses the POST method.
Making requests
A web browser is what traditionally makes requests of websites for web pages. The browser is a type of software known as a client. Clients are what make requests of services. More than just browsers can make requests. The ability to make client web requests is often built into programming languages like Python, or can be broken out as a standalone tool. The most popular tools for making requests outside a browser are curl and wget.
We are discussing Python here. Python has a built-in library called URLLIB, but it’s designed to handle so many different types of requests that it’s a bit of a pain to use. There are other libraries that are more specialized for making requests of APIs. The most popular for Python is called requests. It’s so popular that it’s used for almost every Python API tutorial you’ll find on the web. So I will use it too. This is what “hitting” the Moz Links API looks like:
response = requests.post(endpoint, data=json_string, auth=auth_tuple)
Given that everything was set up correctly (more on that soon), this will produce the following output:
{'next_token': 'JYkQVg4s9ak8iRBWDiz1qTyguYswnj035nqrQ1oIbW96IGJsb2dZgGzDeAM7Rw==', 'results': [{'anchor_text': 'moz', 'external_pages': 7162, 'external_root_domains': 2026}]}
This is JSON data. It’s contained within the response object that was returned from the API. It’s not on the drive or in a file. It’s in memory. So long as it’s in memory, you can do stuff with it (often just saving it to a file).
If you wanted to grab a piece of data within such a response, you could refer to it like this:
response['results'][0]['external_pages']
This says: “Give me the first item in the results list, and then give me the external_pages value from that item.” The result would be 7162.
NOTE: If you’re actually following along executing code, the above line won’t work alone. There’s a certain amount of setup we’ll do shortly, including installing the requests library and setting up a few variables. But this is the basic idea.
JSON
JSON stands for JavaScript Object Notation. It’s a way of representing data in a way that’s easy for humans to read and write. It’s also easy for computers to read and write. It’s a very common data format for APIs that has somewhat taken over the world since the older ways were too difficult for most people to use. Some people might call this part of the “restful” API movement, but the much more difficult XML format is also considered “restful” and everyone seems to have their own interpretation. Consequently, I find it best to just focus on JSON and how it gets in and out of Python.
Python dictionaries
I lied to you. I said that the data structure you were looking at above was JSON. Technically it’s really a Python dictionary or dict datatype object. It’s a special kind of object in Python that’s designed to hold key/value pairs. The keys are strings and the values can be any type of object. The keys are like the column names in a spreadsheet. The values are like the cells in the spreadsheet. In this way, you can think of a Python dict as a JSON object. For example here’s creating a dict in Python:
my_dict = { "name": "Mike", "age": 52, "city": "New York" }
And here is the equivalent in JavaScript:
var my_json = { "name": "Mike", "age": 52, "city": "New York" }
Pretty much the same thing, right? Look closely. Key-names and string values get double-quotes. Numbers don’t. These rules apply consistently between JSON and Python dicts. So as you might imagine, it’s easy for JSON data to flow in and out of Python. This is a great gift that has made modern API-work highly accessible to the beginner through a tool that has revolutionized the field of data science and is making inroads into marketing, Jupyter Notebooks.
Flattening data
But beware! As data flows between systems, it’s not uncommon for the data to subtly change. For example, the JSON data above might be converted to a string. Strings might look exactly like JSON, but they’re not. They’re just a bunch of characters. Sometimes you’ll hear it called “serializing”, or “flattening”. It’s a subtle point, but worth understanding as it will help with one of the largest stumbling blocks with the Moz Links (and most JSON) APIs.
Objects have APIs
Actual JSON or dict objects have their own little APIs for accessing the data inside of them. The ability to use these JSON and dict APIs goes away when the data is flattened into a string, but it will travel between systems more easily, and when it arrives at the other end, it will be “deserialized” and the API will come back on the other system.
Data flowing between systems
This is the concept of portable, interoperable data. Back when it was called Electronic Data Interchange (or EDI), it was a very big deal. Then along came the web and then XML and then JSON and now it’s just a normal part of doing business.
If you’re in Python and you want to convert a dict to a flattened JSON string, you do the following:
import json my_dict = { "name": "Mike", "age": 52, "city": "New York" } json_string = json.dumps(my_dict)
…which would produce the following output:
'{"name": "Mike", "age": 52, "city": "New York"}'
This looks almost the same as the original dict, but if you look closely you can see that single-quotes are used around the entire thing. Another obvious difference is that you can line-wrap real structured data for readability without any ill effect. You can’t do it so easily with strings. That’s why it’s presented all on one line in the above snippet.
Such stringifying processes are done when passing data between different systems because they are not always compatible. Normal text strings on the other hand are compatible with almost everything and can be passed on web-requests with ease. Such flattened strings of JSON data are frequently referred to as the request.
Anatomy of a request
Again, here’s the example request we made above:
response = requests.post(endpoint, data=json_string, auth=auth_tuple)
Now that you understand what the variable name json_string is telling you about its contents, you shouldn’t be surprised to see this is how we populate that variable:
data_dict = { "target": "moz.com/blog", "scope": "page", "limit": 1 } json_string = json.dumps(data_dict)
…and the contents of json_string looks like this:
'{"target": "moz.com/blog", "scope": "page", "limit": 1}'
This is one of my key discoveries in learning the Moz Links API. This is in common with countless other APIs out there but trips me up every time because it’s so much more convenient to work with structured dicts than flattened strings. However, most APIs expect the data to be a string for portability between systems, so we have to convert it at the last moment before the actual API-call occurs.
Pythonic loads and dumps
Now you may be wondering in that above example, what a dump is doing in the middle of the code. The json.dumps() function is called a “dumper” because it takes a Python object and dumps it into a string. The json.loads() function is called a “loader” because it takes a string and loads it into a Python object.
The reason for what appear to be singular and plural options are actually binary and string options. If your data is binary, you use json.load() and json.dump(). If your data is a string, you use json.loads() and json.dumps(). The s stands for string. Leaving the s off means binary.
Don’t let anybody tell you Python is perfect. It’s just that its rough edges are not excessively objectionable.
Assignment vs. equality
For those of you completely new to Python or programming in general, what we’re doing when we hit the API is called an assignment. The result of requests.post() is being assigned to the variable named response.
response = requests.post(endpoint, data=json_string, auth=auth_tuple)
We are using the = sign to assign the value of the right side of the equation to the variable on the left side of the equation. The variable response is now a reference to the object that was returned from the API. Assignment is different from equality. The == sign is used for equality.
# This is assignment: a = 1 # a is now equal to 1 # This is equality: a == 1 # True, but relies that the above line has been executed
The POST method
response = requests.post(endpoint, data=json_string, auth=auth_tuple)
The requests library has a function called post() that takes 3 arguments. The first argument is the URL of the endpoint. The second argument is the data to send to the endpoint. The third argument is the authentication information to send to the endpoint.
Keyword parameters and their arguments
You may notice that some of the arguments to the post() function have names. Names are set equal to values using the = sign. Here’s how Python functions get defined. The first argument is positional both because it comes first and also because there’s no keyword. Keyworded arguments come after position-dependent arguments. Trust me, it all makes sense after a while. We all start to think like Guido van Rossum.
def arbitrary_function(argument1, name=argument2): # do stuff
The name in the above example is called a “keyword” and the values that come in on those locations are called “arguments”. Now arguments are assigned to variable names right in the function definition, so you can refer to either argument1 or argument2 anywhere inside this function. If you’d like to learn more about the rules of Python functions, you can read about them here.
Setting up the request
Okay, so let’s let you do everything necessary for that success assured moment. We’ve been showing the basic request:
response = requests.post(endpoint, data=json_string, auth=auth_tuple)
…but we haven’t shown everything that goes into it. Let’s do that now. If you’re following along and don’t have the requests library installed, you can do so with the following command from the same terminal environment from which you run Python:
pip install requests
Often times Jupyter will have the requests library installed already, but in case it doesn’t, you can install it with the following command from inside a Notebook cell:
!pip install requests
And now we can put it all together. There’s only a few things here that are new. The most important is how we’re taking 2 different variables and combining them into a single variable called AUTH_TUPLE. You will have to get your own ACCESSID and SECRETKEY from the Moz.com website.
The API expects these two values to be passed as a Python data structure called a tuple. A tuple is a list of values that don’t change. I find it interesting that requests.post() expects flattened strings for the data parameter, but expects a tuple for the auth parameter. I suppose it makes sense, but these are the subtle things to understand when working with APIs.
Here’s the full code:
import json import pprint import requests # Set Constants ACCESSID = "mozscape-1234567890" # Replace with your access ID SECRETKEY = "1234567890abcdef1234567890abcdef" # Replace with your secret key AUTH_TUPLE = (ACCESSID, SECRETKEY) # Set Variables endpoint = "https://lsapi.seomoz.com/v2/anchor_text" data_dict = {"target": "moz.com/blog", "scope": "page", "limit": 1} json_string = json.dumps(data_dict) # Make the Request response = requests.post(endpoint, data=json_string, auth=AUTH_TUPLE) # Print the Response pprint(response.json())
…which outputs:
{'next_token': 'JYkQVg4s9ak8iRBWDiz1qTyguYswnj035nqrQ1oIbW96IGJsb2dZgGzDeAM7Rw==', 'results': [{'anchor_text': 'moz', 'external_pages': 7162, 'external_root_domains': 2026}]}
Using all upper case for the AUTH_TUPLE variable is a convention many use in Python to indicate that the variable is a constant. It’s not a requirement, but it’s a good idea to follow conventions when you can.
You may notice that I didn’t use all uppercase for the endpoint variable. That’s because the anchor_text endpoint is not a constant. There are a number of different endpoints that can take its place depending on what sort of lookup we wanted to do. The choices are:
-
anchor_text
-
final_redirect
-
global_top_pages
-
global_top_root_domains
-
index_metadata
-
link_intersect
-
link_status
-
linking_root_domains
-
links
-
top_pages
-
url_metrics
-
usage_data
And that leads into the Jupyter Notebook that I prepared on this topic located here on Github. With this Notebook you can extend the example I gave here to any of the 12 available endpoints to create a variety of useful deliverables, which will be the subject of articles to follow.
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