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How to Use AI Writing Software in Your Content Process [Sponsored]

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How to Use AI Writing Software in Your Content Process [Sponsored]

Provided by Writer

Stop! Hopefully, you read yesterday’s post, 6 Tips for Writing Content That Drives an Immediate Response. (If you haven’t had a chance, I recommend taking a look before you continue reading.)

Great! Now we can get to the good part. That article you read was drafted by AI writing technology – specifically, CoWrite by Writer. My objective was to put AI writing to the test, then share a first-hand account of the experience. Below, you’ll find a complete breakdown of every step of my process, from drafting the initial copy to editing and preparing it for final publication.

I needed to choose a topic for the article to get the ball rolling. At this point, my primary concern was finding the proper focus, so the piece would resonate with the CMI audience and meet the criteria provided to me by CMI’s editorial team. The quality of the resulting draft would be my top concern later in the process, but I just kept it in the back of my mind for now.

After some back-and-forth with the editor, we settled on an idea we both felt would be relevant for the CMI audience: “# Tips for Writing Content That Drives an Immediate Response.

Assuming CoWrite would be able to work with this title and topic, I knew the number of tips included in the article would inform how my first draft would be written. So, I kicked things off by inputting the necessary details into our outline generator.

Step 1. Outlining the article

The setup

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My first step was to confirm the appropriate inputs for accurate outline generation. This can vary, depending on which AI writing tools you’re working with; but for CoWrite, specifically, I needed to provide:

  • The proposed title (required): 6 tips for writing content that drives an immediate response
  • SEO keywords (optional): content, immediate response, writing content
  • CTA (optional): left blank

I submitted the inputs and anxiously waited to see if AI could accommodate my needs or whether I’d have to go back to CMI to workshop a different idea.

The AI output

Below is exactly what I received back:

  • The Importance of Driving an Immediate Response
  • Tip #1: Write a Compelling Headline
  • Tip #2: Write to a Specific Person
  • Tip #3: Make it Easy to Read
  • Tip #4: Use Active Voice
  • Tip #5: Use Power Words
  • Tip #6: Create a Sense of Urgency
  • Conclusion

I recognized it might need some fine-tuning, but at least it confirmed that AI was capable of delivering a draft on the topic I chose. I shared the outline with CMI before moving forward.

My revisions

I used this original outline to create the first draft. Yet later in my process, I decided the AI-generated copy for Tip #5 (Use Power Words) wasn’t the right fit for an audience of experienced marketers. So I returned to the outline generator, reentered the prompts, and requested an expanded set of tips.

One of the additional tips (Add Visual Interest) seemed much more contextually relevant. So, I was able to replace the problematic tip and continue working on the article without having to start all over from scratch.

Curious how effective AI content generation is these days? Take a look behind the scenes of an article written with CoWrite from @Get_Writer. #sponsored Click To Tweet

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Step 2: Adding key discussion points  

The setup

The next step was to identify the tips I’d use to support the discussion in each section of the article. No additional inputs were needed here, as I could carry over the tips generated by AI for the initial outline.

At this point, I did take note of the time (2:15 pm), so I could gauge how long it might take to complete the process from here.

The AI output

CoWrite provided multiple tips I could select and apply to each section or modify as needed. In the image below, you can see the options supplied for Tip #3 and how the interface enables writers to reorder key points or add their own.

My revisions

At this point, I could have taken the opportunity to work in some specific stats, quotes, or talking points of my own. However, I wanted to see what the drafted article would look like with minimal intervention. Knowing I could always revisit this step and generate a new draft, I moved on without adding further input.

In retrospect, it might have been helpful to have CoWrite add specific stats and examples at this stage. Since I knew both would strengthen the final article, it would have saved valuable time and effort to rely on AI rather than having to add those details manually at the end.

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Step 3. Creating a draft

The setup

After reviewing the key points, I was ready to create a first draft. Again, there were no new inputs needed at this stage – as part of its workflow for writing an article draft, CoWrite simply leveraged the information supplied in the outline.

The AI output

The AI writing tool automatically generated a draft, along with a quality score and a series of improvement suggestions. As you can see in the screenshot below, the objective feedback I received was as follows:

  • Overall score: 85
    • The score reflects the number of suggestions compared to the overall length of the article.
  • Suggestions: 38
    • This counts up the number of suggested changes related to punctuation, writing style, clarity, and more. Note that I used Writer’s default style guide here, though the AI can also be configured to work with other style guides.
  • Grade level: 9.0
    • This score is based on the Flesch-Kincaid readability formula.

My revisions (objective)

Objectively, I accepted the quality score as proof that AI produced a good foundation. Yet I also felt it necessary to read through the article myself so that I could form a subjective opinion on its quality.

I worked through all the suggestions – most of which were related to style or clarity (per Writer’s style guide). While I did get a laugh when it recommended changing “immediate” to “instant” (“use simple words” is listed right under Tip #3, after all), I couldn’t bring myself to make that change.

To complete the initial editing phase, I accepted the remaining suggestions. I also took note of a few things that stood out:

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  • All of the section headlines were written in title case. They needed to be changed to sentence case.
  • Passive voice was commonly used throughout the article.
  • In the bulleted sections, the style guide didn’t like the use of capital letters following a colon (unless the next word was a proper noun).

Based on my time stamps, it took me about 20 minutes to address the suggestions sufficiently to move the article into the next phase.

My revisions (subjective)

After working through the low-hanging fruit – grammatical and stylistic errors – I read the article thoroughly to determine how much rewriting might need to be done.

Here, I focused on percentages – was I 50% of the way there? 70%? 90%?! Yet, I also kept in mind the stated intention of this exercise: to keep the article as close as possible to the AI-generated draft while still meeting everyone’s standards (CMIs, yours, and mine).

My conclusion was that the AI-generated draft got me about 75% of the way to achieving that goal without requiring any fundamental intervention on my part. But I did have a few thoughts about what would help bring the article into better alignment with the editorial guidelines I received from CMI:

  • In contrast to many of the CMI articles I reviewed for my reference, the AI-generated draft seemed to lack a clear voice or personality. In retrospect, this isn’t surprising. But, to really make the article my own, I would need to invest much more time manually refining the copy.
  • While, on the whole, the draft might have lacked a strong “author” personality, there were still passages where CoWrite varied its writing style and approach to make the content more engaging.
  • Some sections contained repetitive phrasing or sentences that didn’t really add anything useful to the conversation. Most of the time, I simply removed those passages, though I used Writer’s ReWrite feature (currently in beta) to simplify or enrich some redundant phrases.
  • The most challenging requirement was the need to include specific examples and links to relevant source materials. While CoWrite did provide an example of active vs. passive voice, it just wasn’t the right fit for this article. As noted earlier, I would have saved myself some work if I had better leveraged the “key points” step.
  • The tips varied widely in the amount of content supplied and how it was presented. For example, the explanation provided for the third bullet under Tip #3 (“Make it easy to read”) was (ironically) too “short and simple” to be helpful, so I had to expand it to provide better value.

Step 4. Editing and revising the article

After writing and editing both articles (the AI-written one and the one you are currently reading), I sent them to CMI for feedback. As you might expect, both required some minor revisions and restructuring on my part before the editorial team moved it into their process for final editing and production.

But there was still one larger piece of feedback to reconcile: The AI-written article needed more sophistication and advanced recommendations to really benefit the CMI audience.

That feedback prompted me to swap out Tip #5 (as referenced earlier) and do some rewriting to strengthen certain points. It also explains my earlier note acknowledging I could have done more during the outline and key points stages to produce a stronger draft.

It’s worth noting that the latest wave of AI content generation technology provides the ability to train AI based on your content. Using that functionality, I could have provided customized input (sample pieces of content) and received an output that was better aligned with the CMI audience’s needs. I would have explored this option if I had not been up against a deadline.

The latest wave of AI content generation technology provides the ability to train AI based on your content, says @ryanejohnston #sponsored. Click To Tweet

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As someone who has not written an article for a third-party publication in quite some time, CoWrite saved me a lot of time and frustration. The initial process of going from title and topic to actual first draft was incredibly quick and efficient, and I spent zero time staring at a blank piece of paper, wondering what to write.

As expected, the heavier lift came during the editing process after I had the first draft. I tracked it as taking from 2:15 pm to 4:37 pm to manage (with some Slack and snack breaks mixed in). A coworker gave it a second round of edits, which brings my estimate up to about 2.5 hours of editing before sharing that draft with CMI.

Addressing the feedback I received from CMI tacked on an additional 45 minutes of editing and rewriting before I submitted the updated draft. Going from title to submitted draft in under 4 hours is a big win, considering how rusty I am at writing.

There are a few tips that I’d provide anyone looking to get started with AI-generated content:

  • Start with a strong topic that you feel confident writing about – with or without AI
  • Consider all the elements that need to go into a great article and incorporate them into your process (stats, quotes, etc.)
  • Think like an editor when working with AI writing, and you’ll get great results.

Now it’s time for the real question: My dear reader, what did you think of the article you read prior to this one? What were your initial thoughts, and what are your thoughts now, having read all the details on how it came together? Do share them in the comments!

About Writer

Writer is the leading AI writing platform for teams. Writer empowers GTM leaders to build a consistent brand across every customer touchpoint. Automated language generation and writing suggestions make it possible for teams to accelerate content, align with their brand, and empower more writers across all types of content and communications.

Writer recently launched CoWrite, which helps you produce high-quality, on-brand first drafts in a fraction of the time, using AI that is custom-trained on your best content. You can learn more about CoWrite on our product page: CoWrite.



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MARKETING

MOps leaders as psychologists: The modern mind-readers

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MOps leaders as psychologists: The modern mind-readers

This four-part series presents a framework that describes the roles and responsibilities of marketing operations leaders. This part discusses MOps leaders as psychologists, in addition to their roles as modernizers (see part 1) and orchestrators (see part 2).

Exposure to marketing during my early educational journey was limited. With a heavy math/science background, I chose the “easy” path and majored in engineering. I struggled in advanced engineering classes but thrived in electives — communications, business, organizational behavior — which was a sign for my future in marketing.

Because of my engineering background, I was fortunate to get an opportunity to join GE Healthcare through its entry-level leadership development program. There I was exposed to magnetic resonance imaging (MRI). 

MRIs had become go-to diagnostic devices and subsequently were used in neuroscience. I was fascinated by their eventual application in fMRI: Functional MRI. These extensions helped us understand the most consequential medical mystery: how (and why) people do what they do.

fMRI uses the same underlying technology as conventional MRI, but the scanner and a medical contrast agent are used to detect increased blood flow in response to a stimulus in what is commonly referenced as “hot spots.”

fMRI reveals which of the brain’s processes “light up” when a person experiences different sensations, e.g., exposure to different images in common studies. As a result, we now know what parts of the brain are involved in making decisions.

Successful marketing ‘lights up’ customers’ brains

Traditional marketing campaigns and measurement left gaps in understanding how and why people choose to buy. We were dependent on aggregated data. 

With digital channels, we gain first-hand insights into an individual’s response to a stimulus, i.e., content. Here’s where the comparison picks up: 

  • We can observe nearly anything and everything that customers or prospects do digitally.
  • Most customers know that we can track (almost) everything that they do.
  • Because of that knowledge, customers expect contextual, value-based content, forcing marketing to provide more value in exchange for the permission to track.

Our goal as marketers is to make our customers and prospects “light up” with pleasure or satisfaction at each interaction. And, we now have the technology to track it. We are effectively reading minds — just as if it were an fMRI scan.

Here’s an overview of three of the primary psychology “tactics” that every marketer should know: 

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  • Priming is the attempt to trigger a subconscious reaction to stimuli that influences our conscious decisions. The most common application is in branding and first click-through impressions. If a customer continues their journey, then the use of aspirational product or service images in content are common priming approaches.
  • Social proof is perhaps the most common example, given the impact of word-of-mouth influence. It is commonly seen in product reviews and ratings. Content marketing often relies on case studies and customer testimonials to hear from “people like us.”
  • Anchoring refers to marketing’s role in pricing and discounting. Most decisions people make are relative to the initial set of information they have received.

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MOps leaders manage the mind-reading stack 

MOps leaders are modernizers that now manage the mind-reading martech stack. We then lead the orchestration efforts to analyze the response (the “scan” data) and “prescribe” the next steps of the campaign.

Two catalysts spawned the emergence for martech applications:

  • New channels that delivered stimulus (content) and collected responses: search, social media, retail commerce channels, etc.
  • Tools that organize and manage all of that response data, from foundational CRM platforms to marketing analytics and data enrichment.

These developments led to the new psychological skills that have become essential to the role of MOps leaders. 

Processing and interpreting intent data is an example. ZoomInfo illustrates how B2B marketers are accessing this capability. The company now provides buying signals to marketers based on their customers’ behaviors, in addition to the basic contact information that was the origin of its business. 

Intent data is already in widespread use. Six in 10 companies responding to a recent survey said they had or planned in the next year to implement intent measurement data solutions. 

The top challenges for effective intent data utilization fit squarely in the role/responsibilities of MOps leaders include:

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These trends support the conclusion of the first three parts of this series — that MOps leaders should aspire to be: 

  • Psychologists who elicit responses (i.e., “light up” the brains) of customers and prospects and interpret those signals for the business. 
  • Modernizers who adopt the technology that enables the activation of those signals.
  • Orchestrators who are cross-functional project managers and business partners with IT, legal and compliance.

Next time, I’ll complete the framework with a discussion of how the role of MOps leaders includes being a scientist, constantly testing and evaluating marketing efforts with teams of analytics specialists and data scientists. 

Editor’s note: This is the 3rd in a 4-part series. In case you missed them, part 1 (Modernizers) is here and part 2 (Orchestrators) is here.


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


About The Author

Milt is currently Director of Customer Experience at MSI Data, an industry-leading cloud software company that focuses on the value and productivity that customers can drive from adopting MSI’s service management solutions.

With nearly 30 years of leadership experience, Milt has focused on aligning service, marketing, sales, and IT processes around the customer journey. Milt started his career with GE, and led cross-functional initiatives in field service, software deployment, marketing, and digital transformation.
Following his time at GE, Milt led marketing operations at Connecture and HSA Bank, and he has always enjoyed being labeled one of the early digital marketing technologists. He has a BS in Electrical Engineering from UW Madison, and an MBA from Kellogg School of Management.

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In addition to his corporate leadership roles, Milt has been focused on contributing back to the marketing and regional community where he lives. He serves on multiple boards and is also an adjunct instructor for UW-Madison’s Digital Marketing Bootcamp. He also supports strategic clients through his advisory group, Mission MarTech LLC.

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