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How to define your DAM governance structure

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How to define your DAM governance structure

When setting up a new digital asset management (DAM) system, governance is usually toward the bottom of the to-do list and, in some cases, forgotten altogether. You’re already juggling system configurations, legal compliance, user permissioning, taxonomy, metadata, training, etc. Do you need to worry about governance right away, too? 

Yes, you do. Governance touches all those things and more. Without it, your DAM may bring more chaos than order in the long run. Don’t leave it out or push it to the last minute. A DAM governance structure should be top of mind from the start of your DAM journey.

What is DAM governance?

As you’re already aware — and hopefully didn’t learn the hard way — a DAM doesn’t run itself. It’s not a set-it-and-forget-it system.

In the context of a DAM, governance is the practice of maintaining and evolving standards, policies and best practices. It encompasses the people, processes and technology involved with digital asset management at your company. 

Governance documentation defines the information, guidelines and policies that provide stability and keep your DAM running smoothly for the long term. This framework will prevent your DAM from turning into a junk drawer as your business and the system evolve, and stakeholders and end-users change commitments and flow in and out of your company. 

Documented governance policies support risk management and ensure ongoing alignment with your overall business goals. DAM governance also includes continually collaborating with stakeholders to manage, change and adapt your system to your organization’s needs. It establishes and maintains communication between all relevant stakeholders for sustained DAM success.

Where do I start?

Many of the questions you’re addressing during the set-up and launch of your DAM are the same questions you need to focus on when defining your governance policies. Multi-task! Save yourself from having to revisit those questions later by defining and documenting the answers from the start. 

Your governance documentation will be a living document that needs regular review and updating as your business and its priorities evolve. Like the DAM, it isn’t “set it and forget it.” You’ll thank yourself later if you remember it throughout your DAM journey rather than wait until a problem arises.

Your governance plan should address the following questions:

  • What goes in the DAM?
  • Who has access to the DAM? Which areas of content can they see with that access? And what are they allowed to do with the content they can see? 
  • What are the required naming conventions?
  • Who is applying metadata? What standards and requirements do they have to follow?
  • How will versioning be handled?
  • What are your licensing and regulatory requirements?
  • How are expired assets handled? What is the archiving process?
  • Who is responsible for providing training?
  • Who is responsible for enforcing and updating the DAM standards and requirements?
  • How will changes and updates be communicated to your users?
  • What is the reporting process when something goes wrong? Who is responsible for resolving which types of issues (technical, legal, content, etc.).
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Does this list seem overwhelming right now? Then start with a basic purpose statement and build from there as you go. Why does your DAM exist? Who is it for, and what goals is it expected to achieve?

Dig deeper: A 12-step guide for implementing a digital asset management system

Keeping the peace: Working with DAM stakeholders

Putting your DAM policies and process requirements on paper is the easy part. Generating buy-in and enforcing those policies and requirements is where the hard work comes in. Your governance documentation has no value if its contents aren’t being implemented and enforced. 

Your DAM is likely an enterprise-level system that must meet the needs of varying and, in some cases, competing divisions and departments within your company. Those departments need to have a voice if your DAM will be successful. 

Don’t forget that you also have stakeholders in business areas that aren’t directly handling the assets flowing in and out of your DAM but have a vested interest in the success and proper management of the system. Your IT and legal teams need a voice alongside your marketing and creative teams. Buy-in from all levels of the organizational chart is critical to your DAM’s success — from leadership to end-users. You must look at the DAM user experience from all angles to get the full picture and provide the best experience. The key to making all this work is communication.

Be thorough when defining the roles and responsibilities of all your stakeholders. Make your expectations for their commitment to the DAM’s success clear. You want active and engaged stakeholders, and if someone isn’t living up to the expectations of their role, you should feel empowered to seek a replacement. 

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Be sure you’re referring to roles and not specific personnel names or titles in your documentation. People will leave the company or take on new internal commitments, and org charts will change. When new members are onboarded into the DAM team, having well-defined roles for them will ease the transition. 

Likewise, be conscious of always giving everyone an equal voice. When you have a mix of strong personalities on your team of DAM stakeholders, it can be difficult not to give in to the loudest voice in the room or defer to the stakeholder representing the largest group of end-users.

You may consider instituting a voting policy for major decisions involving the DAM as part of your governance plan to give everyone an equal opportunity to help determine the path forward. Everyone needs to feel heard, or engagement will suffer.

Engaging regularly with your stakeholders from day one of your DAM journey will set the project off on the right foot. Begin holding meetings before your DAM is open to any end-users. Regularly review and address user feedback, assess if changes are needed to your processes and policies and evaluate the potential need for technical upgrades. Getting governance to stick in an already active system is exponentially more difficult. Not impossible, but challenging. 

If you wait to address governance with your users and stakeholders until after the system has launched, most major decisions have been made. Getting everyone involved from the beginning fosters a feeling of ownership for the DAM and encourages ongoing investment in its success. 

As your DAM moves through planning and launch into maintenance, your meeting cadence may become less frequent, but there is never an end. Meetings should continue so that you keep your stakeholders and your users involved. Their value doesn’t decrease once the DAM has rolled out and the governance documentation is written. 

As the DAM evolves and grows, decisions will still need to be made, and they should always remain involved in those decisions. While the existing governance policies will guide future decisions, remember it is a living document. Always have clearly defined channels for stakeholders and end-users to offer feedback and suggestions for changes and improvements to workflows and processes. 

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Don’t hide your governance documentation away in a secret location. Make sure it’s easily accessible and open for users to review at any time. Always be open to questions and feedback about the documentation. 

Dig deeper: Here’s why you need a DAM workflow — and how to map it out

I don’t need governance: I have a DAM manager

Don’t make the mistake of assuming that having a dedicated DAM manager role is your governance. Yes, they likely have a degree in library science or DAM management and are certainly well-versed in all the DAM best practices. They talk with users and consider their needs and opinions. So aren’t they ultimately responsible for all the decisions? They know the “right” way to manage a DAM — that’s why you hired them. 

Well, sorry, but no. Having a single system manager unilaterally making all the decisions with no governance policies guiding them isn’t ideal. It’s certainly not the best way to get buy-in and have your users feel a sense of ownership for the system they’re using. And while the DAM manager may know all the best practices, they aren’t using the DAM every day as an end-user from all the different facets of your user base. 

Yes, best practices are best practices for a reason, but they don’t always work for every scenario and situation. You can’t force a best practice if it is not the best solution for your particular users and their business needs. 

The DAM manager will use the governance policies to guide you forward and maintain standards and order, but they’ll also recognize that sometimes you’ll need to be flexible when it comes to best practices. If sometimes being best-practice-adjacent makes the end-users’ lives easier and doesn’t introduce risk or disorder, you have to be willing to give an inch or two. 

Happy DAM users are active DAM users who remain engaged in its long-term success. The success of the DAM depends as much on stakeholder and end-user involvement as it does the DAM Manager’s leadership.


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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.

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The Secret to Grow Your Business

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Social Media for E-Commerce: 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:

1685505933 232 The Secret to Grow Your Business

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.

1685505934 837 The Secret to Grow Your Business

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.

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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:

1685505934 243 The Secret to Grow Your Business

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.
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3. Improves Conversions

The US retail social commerce sales are projected to reach $79.64 billion by 2025:

1685505934 910 The Secret to Grow Your Business

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.

1685505934 210 The Secret to Grow Your Business

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.

1685505935 345 The Secret to Grow Your Business

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.

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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.

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Mnemonic Content Strategy Framework Can Spark Conversations

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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

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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.

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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?”

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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.

Subscribe to workday or weekly CMI emails to get Rose-Colored Glasses in your inbox each week. 

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Cover image by Joseph Kalinowski/Content Marketing Institute



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The Moz Links API: An Introduction

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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).

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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.

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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.

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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:

  1. anchor_text

  2. final_redirect

  3. global_top_pages

  4. global_top_root_domains

  5. index_metadata

  6. link_intersect

  7. link_status

  8. linking_root_domains

  9. links

  10. top_pages

  11. url_metrics

  12. 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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