‘AI 5 Ways’: How AI can help Content Marketing Drive Business Outcomes’

Content Marketing. By now, we all know it works. It’s the lifeblood of B2B and B2C marketing, albeit in different forms. Its proven to deliver engagement if done right. And it lends itself well to all other digital marketing techniques – from websites to email marketing to social media. So, what’s the catch?

Well, it involves writing. And writing takes time. It’s not everyone’s cup of tea. Good writers cost money. And search engines are getting smarter at weeding out the meaningless keyword filled drivel that parades as content. So, what’s a marketer to do?!

In a conversation with us, Parry MalmCEO of AI-powered marketing content firm Phrasee, agrees that the lines are blurring between B2B and B2C content, but unfortunately, neither of them is getting it right even today. The endless quest for clicks and diminishing reader attention spans have combined to make most marketing content, be it B2B or B2C, follow the same blueprint, which has proven successful for online content across the board. An attention-grabbing headline – a bit of basic information – then the sales pitch, often all in just a few hundred words.”

Quality traffic comes from useful and engaging content

So, writing content to drive traffic – regardless of the quality of the traffic – is obviously one pitfall. “Writing boring content is another,” says Parry. “it’s critical that content is not just valuable, but also engaging. Give your audience content that’s actually worth their time, and make their time as fun as possible.”

Either way, the biggest challenge of the next 5 years “will be much the same as the biggest challenge of the last 5. That is: finding a way to stand out in a subscriber’s crowded inbox” – or screen or whatever touch point she chooses to use at that moment.

Unfortunately, every touch point is saturated with marketing messages. Marketers need to start preparing for a future where marketing content is not just generated efficiently at scale, but is also intelligent, contextual and designed to optimize revenue. What is doubtful though, in the multi-touchpoint environment we live in today, is whether humans will be able to deliver that sort of relevant, platform-specific content at the scale and speed marketers need.

Is AI the answer?

I know what you are thinking already. All the news-worthy times AI generated cringe-worthy content. Who would want to risk their brand reputation with that, right? But the important thing to remember is that AI is nothing without the data on which it’s based. And when based on the right data and highly controlled parameters, it can deliver exciting results. In spite of that argument, it is still a pretty giant leap of faith to leave the content writing to a machine. In fact, as Parry confirms, the most common questions/clarifications he gets from CMOs while considering an investment in AI-powered content tools include:

1. Why should I invest in something my team already manages?

2. What are the proof points and how does this apply to my brand?

3. How much is this going to cost me and what is the anticipated ROI?

All fair questions, I would argue, considering we are conditioned to think writing is inherently a human skill. But like it or not, the world has moved on and AI is already being used to make things easier across diverse applications. So, let’s take stock of where we stand with AI in content marketing.

AI generated Marketing Content: multiple ways to drive business outcomes

1. AI for content curation and atomization: One of the earliest use cases for AI in marketing content was to curate content from multiple sources (build newsletters etc.). Repurposing content via either atomization (breaking long format content into several short formats) or then to turn one form into another is also a common way AI can help leverage the content production investment optimally.

2. AI for repetitive content at scale: Another obvious use case for AI in content marketing is the quick scale-up of content that is repetitive and tedious. Imagine a message that needs to be intelligently rewritten for posters, e-mailers, websites, social media platforms and so many other platforms available today. In several languages. It may not be the best use of a full-time writer, and AI could easily take over. The recent example of the Alibaba AI-based content tool addresses just that. The Alibaba AI writer can write up to 20,000 lines of copy per minute and millions of small and medium-sized online retailers use it every day to write e-commerce copy. Generating a steady supply of e-commerce copy – product descriptions and introductions – in fact, has been a huge pain point for online sellers. But AI can address that efficiently.

Both of the above are the efficiency/productivity argument for AI in content.

But what if AI could have the right context and the right data, and not have to rely on gut instinct like humans – even the best writers – would tend to do (after all, a single writer cannot relate to every target segment she writes for)?

3. AI to drive content and digital marketing ROI: When it comes to writing marketing copy that drives conversions – on emails, on websites, “one brand’s ‘cringe worthy’ could fit another brand’s tone of voice perfectly, so it’s really important that unique boundaries are set for each Brand” says Parry. So, if AI had the data about what content works and what doesn’t, the context for when a particular kind of content works and when it doesn’t, and strict parameters within which to generate content – for each brand and segment – then it could potentially do what Phrasee claims their solution does – content that performs better than human-generated content 98% of the time. Here, says Parry, the focus is on ROI and in that sense, it’s far more than just a productivity tool. And while tools such as the Alibaba tool are bringing the use of AI into the mainstream, progressive marketers need to also think about making it deliver more than savings.

4. AI to drive real-time content personalization: At the next level, AI could also take that content, context, and rules; and apply it to deliver real-time personalized content at scale. That means every reader sees something slightly different, and thus, has a personalized brand experience.

5. AI for predictive content analytics: Given that AI is capable of using past data to generate the most relevant content for each brand and segment, undoubtedly, it can use that data to predict what campaigns will perform the best, once it has enough campaign performance data and customer behavior variables to work with. Using AI to predict performance can also mean that a lot less content can do a lot more, instead of marketers creating a wide basket of content hoping something will work. That is efficiency and ROI. In the same context, we ask Parry what he thinks is next. Probably the most interesting thing in the next few years is the ongoing drive to monetize social channels more effectively. We’ve barely scratched the surface of the massive digital marketing potential that social networking sites like Facebook and Instagram have to offer yet, but the world’s best marketing and tech minds (including us!) are on the case as we speak.”

We also think the potential of AI in creating content in immersive formats (AR/ VR/ Voice) is something to watch for, as is its potential for, as Parry says, use in social media and influencer marketing content. As we usually like to say at the end these sorts of conversations on MarTech Advisor, ‘we’ll be tracking the space’!

 

Original post: https://www.martechadvisor.com/articles/machine-learning-ai/5-ways-how-content-marketing-is-leveraging-ai-to-drive-business-outcomes/

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