November 9, 2020

AI-Powered Content Marketing to Scale Your Business

Companies are running to publish videos, blogs, case studies, whitepapers, podcasts and e-books to keep their content marketing strategy active. As business operators start focusing on economic headwinds while businesses streamline their resources, it has become challenging to scale content marketing operations. Perhaps, AI can be of some help here.

What is AI-Powered Content Marketing?

Content marketing is the strategy of creating blogs, social media posts, whitepapers, case studies, videos, and e-books to attract, convert and retain target audiences. It's a pull-based strategy incorporating different ways of getting customers to come to your brand, products or services. 

In this entire process, attract, convert and retain are the central tenets. As marketers, we have graduated from optimizing these processes to automating them, and that is where AI comes in handy. 

Content marketing is a resource-heavy and time-consuming process. You generally begin with creating buyer personas, studying the problems they are facing and then producing content to solve those problems while still pushing them through your funnel. 

When you use AI in other processes, it cuts down resources and increases efficiency. While AI can undoubtedly help you achieve that with content marketing, it can also help you get a competitive edge on the back of unique insights that can be uncovered only based on AI-powered content marketing processes. 

Generally, there are two ways of integrating AI in your content marketing process:

  • Plug & Play Tools: There are several apps and platforms that are already doing the heavy lifting. They have AI technology to help you run more effective and efficient campaigns.
  • Internal AI Processes: This is a long-term goal. Since content marketing is strategic to your business, you might want to build an internal custom solution. While operating on the same principle, this would be a totally different, elongated and more comprehensive exercise. But, it will also help you build a competitive edge in the long run. 

How to Implement AI-Powered Content Marketing?

As most industry experts already know, content marketing is an iterative process. The performance of your first blog will be vastly different than the performance of your 100th blog. Some marketers have a misconception that they can use AI as a brute force to run through the journey to having published 100 blogs and get the desired results. 

While AI is powerful, it cannot automate the entire process. There are a lot of contextual inputs that you have to give it in order to reap the benefits. Before you get started with your AI experiments or integration, here are some resources you will need:

  1. Customer Data: Accumulate all the data you and your associates possess on your customers. Whether that includes buyer personas, purchases history, invoices or interactions, try to build a bank of data that gives you everything you need to know about your customers. Later on, if you need dummy data to test some of the AI-generated content marketing ideas, this data will come in handy. 
  2. Campaign Performance Data: Make sure you have clearly decipherable data on your past campaigns. It would help if you can break this down by timeline, budget and posts. Focus more on the paid campaigns, so you are able to create a benchmark of performance between your organic content marketing and digital advertising campaigns. 
  3. Content Performance Data: Any form of content you have published — add it to this repository, along with the performance metrics it achieved. It is not necessary for you to include reports and analysis you did on the past content. Just the raw data will help you train your AI algorithm or help you perform a comparative analysis between your old content and new AI-powered content. 
  4. Industry Averages: Get the data pertinent to your industry for average repeat buyers, cost per acquisition, lifetime value of customers and other engagement metrics. News dailies, consultants and industry associations generally publish such data. This would be one of the key benchmarks to evaluate the results of your AI-powered campaigns against. 

The Role of AI in Content Marketing

Now that you have the raw material ready, here are the potential areas where you can implement an AI solution. Depending on what function you choose and what features you are looking for, you might be able to find a plug-&-play solution for each one of these:

Audience Research:

This includes getting automated data or deeper insights into your audience. Typically, you can expect to garner insights pertinent to keyword intent, website visits, thematic ideas, search query trends and so on. Although you must already be getting the raw data on these from your Google Analytics dashboard and Ads account, an AI-powered platform can use the data to highlight insights that would otherwise get missed.


Once the content has been produced, you might want to tailor it to specific buyer personas or search queries to maximize the traffic generated by it. AI can especially help you achieve this with rule-based targeting. This is an area where the data you collected earlier can be of great help. 

Programmatic media buying was one of the earliest categories to get disrupted by AI. This is the reason why content distribution and matching have largely become automated for several large-scale brands. As far as content production is concerned, you still have a ton of space to create value. 

Content Production: 

Over 40% of the content marketing budget goes into content production. Most marketers hire agencies and experts who produce the content. But, the significance of each content asset is different, and some of them might not be worth such capital intensive measures. As a matter of fact, there is a form of content that can be easily produced with AI. It will be data-backed, save time and resources without compromising the results.

Download The Essential Guide to Developing a Content Strategy to learn how to  effectively drive high conversions.

The Types of Content You Can Produce with AI

It's fascinating to see that AI is now equipped enough to produce content. Oscar Schwartz, a renowned writer, detailed in his TED Talk the near future where poetry produced by human beings and computers would be indecipherable. That talk was given five years ago, so, in some senses, we have already arrived in that future. 

When you look at the most high-performing content pieces on social media or on a blog, you might wonder how is it possible to generate such content with AI? The reality is we are not there yet. AI cannot produce high-impact novels and movie scripts. In the context of content marketing, too, the best performing content has a strategic take that has been developed over years of tacit and explicit observing, with some mix of personality.  Neil Patel's content is segmented into its simplest form. Seth Godin's content is quirky. Rand Fishkin's content is informational and curiosity-inducing. It would be difficult for an AI engine to generate the kind of content produced by these prolific communicators without making it look like a jumbled set of facts stuffed with keywords. 

That is one side of the story. The other side is becoming more efficient. Not all the content produced for content marketing has to be that highly-developed, crafted, researched and personalized. There are forms of content that can be massively automated, and since you don't have a limit on the numbers, you can produce an equivalent amount of value with such content, as you would with the human-composed ones. 

Here is a framework for you to evaluate what type of content can be automated with AI. If your content meets one or more of these qualities, you should consider automating it:

  1. High frequency of publishing, mostly based on data or fact aggregation. Example: Listicles, news updates, product launches, etc. 
  2. Content's success can be broken down into smaller pieces and can be repeated without any changes. 
  3. Low variance in tonality. A combination of words may change, but the tone of informing remains the same.
  4. The intent of the content is to inform the audience. 

As you might have started visualizing, there is a family of content that might adhere to more than one of these requirements. Here is a list of types of content you can automate using AI:

  1. News Updates: These are generally updated relevant to your product category, industry, or the leadership of your company. A simple Google Alert can trigger updates on themes you believe are relevant to your target audience. This is easily achievable because several news agencies like the Associated Press have been doing this for a few years now. 
  2. Repositioned Blogs: After you have created a blog, or any piece of content for that matter, you might want to reposition it to suit another target audience. Or, you may want an automatic data update and description based on it. There are off-the-shelf AI solutions that can help you get this repositioning easily.
  3. Product Descriptions: There are tools like, which help you generate product descriptions on a large scale based on the raw features of the product. 
  4. Data Visualizations & Reports: Tableau and Power BI already have an extensive library for creating intrinsic data visualizations and reports. Once you have the right data repository setup, along with a platform to connect both, you can practically send out reports and visualizations on a daily basis. Earlier, this was possible only with the Visual Basic Application in Excel. With AI, these reports have become more intricate and nuanced, providing human-like insights and the right context. 
  5. Chatbot Responses: Facebook already provides the functionality to create automated responses on your company's social media handle. You can scale similar conversational chatbots to your website and other digital properties with conversational chatbots that use Natural Language Processing. For example, Frase’s chatbot product uses AI in to find the answers visitors are looking for on your website.

How Does AI Help Businesses Scale?

Conceptualizing the application of AI in your content marketing efforts is not difficult or abstract anymore. We have covered the specific use-cases. Now is the time to set your expectations straight. What would be the impact of these AI solutions in scaling businesses:

  1. Consistency: Once the customers have interacted with your brand on one medium and are satisfied with their experience, they don't want it to change when they move to other touchpoints. Consistency in your content marketing campaigns can help you get compounded results. With AI-powered content marketing plans, you will take a rule-based approach that will change only when you change the inputs and parameters of the program.
  2. Cost-Effectiveness: AI-based programs don't have maintenance costs. Once integrated, they get to work in real-time. Plus, all the inefficiencies like the additional research, feedback on inconsistencies, and automated scheduling are eliminated from the process. This lowers your marketing overheads. 
  3. Human Capital Availability: Now that your copywriters, brand managers, SEO experts, and digital marketing managers don't have to focus on just pushing content out — they can focus on more strategically important initiatives that have a greater potential of generating value for the business. 
  4. Zero Incremental Costs for Scaling: Assume that one writer is paid $4000 a month to write ten blog posts, alternate day emails, social media copy, and landing page content. No matter how talented this individual is, they can only produce a fixed amount of deliverables in a month. Let's assume that number is 30–40. Every time you have to produce more deliverables, you will have to hire freelancers and agencies. An AI engine powered by Natural Language Generation technology can scale the output with a few clicks and practically zero incremental costs.
  5. Data-Driven Decision Making: Quite often, since the marketing teams are operated more on subjective-decision making, it becomes difficult to attribute the success of a campaign to one specific element. Hence the opinion of the most senior members in the team holds the maximum weight, even if it is biased. AI pushes you to make more data-driven decisions, so only the most meritocratic ideas get scaled. 

AI Content Marketing Tools

Here are the tools that can help you test, optimize and scale your content marketing initiatives with minimal efforts and maximized success rates:

  1. HubSpot: The company is already known for its CRM platform. With AI-enhanced capabilities in its Marketing Hub, you can get thematic recommendations for topic clusters optimized for brand relevance and traffic generation.
  2. BrightEdge: Get more detailed insights on which keywords have the maximum intent and can generate maximum organic traffic for you.
  3. Personyze: By aggregating data from several sourcing channels, the platform gives each of your website visitors a unique and tailored experience.
  4. Adobe Marketo Engage: It scans the content assets already established on your website, and then, by harnessing content consumption and user-profile data, it optimally recommends content for maximum engagement to your users on your website.
  5. Wordsmith by Automated Insights: If you are in the business of consistently pushing out content, this is the tool you must try. Based on the data you provide, Wordsmith generates content that is ready to be systemically sent to your entire clientele.

Wrapping up

AI can be the greatest partner to the marketers by writing reports, analyzing the content or making it cheaper to promote your content.

AI has all the power to speed up the content creation process or can offer personalized content to the audience. Hence AI-Powered content can be a definite game-changer for your business.

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Hardik Shah

Hardik Shah works as a Tech Consultant at Simform – a dedicated team of mobile app developers in Chicago. He leads large scale mobility programs that cover platforms, solutions, governance, standardization, and best practices. Connect with him to discuss the best practices of software methodologies.


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