What was the first Content OR Marketing? I think these two words go together for quite a while now. As we all spend a lot of out time ONLINE, I see the tendency in the acceleration of all the subsets in Digital Marketing. Think of mainstreaming Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) in our everyday life. Once tried we are happy to pay for all the conveniences provided by the latest technologies.
Every time when you see an online ad it is an AI sifting through your data to serve you a personalized experience. When you read an article, more likely it has been already researched, written, and optimized with the help of AI.
Digital Marketing is evolving with extremely rapid speed. Moreover, you cannot just learn the subject you should be an everyday practitioner. Marketing teams have had no choice but to adapt to new realities of the digital world and new consumer experiences. As a result, we have become more and more reliant on data, automation, efficiency and new ways of scaling.
Following the changes for the last decade, traditional content marketing and AI have both matured to a point where they’re ready to take a mutual benefit in one another. However, there are still not enough AI-empowered content marketing solutions that we can begin to get a feel for how the combination of traditional content marketing and AI are going to disrupt the industry.
In this article, we would like to unfold 4 ways that AI is changing content marketing, hopefully for good 😉
I. Content Discovery
Brainstorming new content ideas is hard work and the process that most of us have used to date has been draining, time-consuming, and frankly, inefficient.
But there are new ways…
We’ve always assumed that the best way to come up with content is by collaborating with other people, journaling, whiteboard or even old good notes, juggling ideas and watching closely what sticks.
An editorial focus on timeliness and relevance yields content that people A) you can’t find easily elsewhere, and B) can update as the story unfolds, holding people’s attention longer and making them come back for more.
Social listening and social monitoring unleashed with relatively recent AI adoption bring new opportunities is a process called “Content Discovery”. AI-powered research tools have the potential to go far beyond the capabilities of Google Alerts, Google News, or Cision, and similar platforms.
Natural language processing provides machine learning algorithms the tools to digest any data which can be represented in text, which now also includes many multimedia types of content thanks to Google transcribing YouTube videos, podcasts, and other formats.
This means that we can easily index the world’s content, parse it into entities (terms), discover relationships and meanings, and assess the similarities and differences.
We can analyze social networks engagements for any piece of content on the Internet, across the entire history of social media. From this method emerges a reliable means to measure content performance based on who’s reading, sharing, and engaging the content.
Content that is clicked on, liked, commented on, shared, or otherwise engaged with by an audience of a meaningful size can be digested, analyzed, and used to inspire and inform your own content efforts.
AI enhances content marketer capabilities to possess, and not to spend to much time on research, edit, promotion, analysis and other activities.
AI could be identifying potential trends that need to be covered or divergent ideas that present creative new possibilities. Social networks can be monitored and mined algorithmically for relevant customer insights which can inform content strategy and identify content opportunities.
II. Predictive Analytics
AI’s uncanny ability to process incredible amounts of data at high speed and efficiency opens the door to a world of new possibilities in predictive analytics.
Predictive Analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events. Wikipedia
In other words, machine-learning algorithms, using unlimited amounts of labelled data can make incredible predictions.
This is accomplished by identifying common data points and patterns of activity which, historically, tend to result in predictable outcomes and then applying that logic to future scenarios and making assumptions based on what data is and isn’t available at the time.
When applied at scale, predictive analytics literally takes the guesswork out of forecasting future outcomes, so that data scientists/statisticians can make actionable projections without personal bias casting a shadow on their interpretations.
Important applications for AI platforms in content marketing include use cases like predicting content performance, modelling the results of potential optimizations (before implementing solutions), identifying popular topics and trends, and identifying which influencers are likely to engage with and share your content.
AI learns as it works, evolving predictive algorithms over time. As it encounters dissonant situations the algorithm updates itself and thus results, recommendations, etc. improve overtime as well.
III. Content Promotion
No doubt there is a huge opportunity and value for B2B marketers in promoting content on social media platforms like Linkedin. Decision-makers actually do spend a large portion of their time online.
The problem is that B2B marketers struggle to develop relevant strategies to generate leads and quality prospects using social. Take a Linkedin for example, it’s a fantastic channel for marketers who knows how to share their story.
49% of B2B marketers consider [social media] the hardest tactic to generate leads from.
It’s not for lack of trying…B2B marketers are almost universally investing in paid ads on social media, including advertising on platforms like Facebook (which is not the best strategy in 2020 and looking forward).
If there is a challenge there is an opportunity… for AI
Audience management platforms are a growing segment of digital advertising platforms as a result of the increasing challenges B2B advertisers face in wake of Facebook facing criticisms for its handling user data.
Some platforms integrate data sources like Google Analytics, your search ad campaigns, or third-party data providers to discover relevant audiences on social networks. Others rely on the same Facebook Insights you’re accustomed to, but leverage AI to optimize your campaigns over time.
Popular Interests are easily discoverable on Linkedin and using Linkedin’s campaign manager you can target people with spesific ‘job titles’ which is surprisingly still ignored by many social media agences and advertisers, driving up your advertising costs.
With this longtail approach, KG Digital is able to target highly-relevant audiences at much-reduced rates, which increases CTR while significantly lowering per click and per lead costs. Apart from other organic and valuable strategies which actually depends on your specific business model, niche, industry, tone etc.
IV. Performance Analysis
AI is best suited for repetitive tasks that require a lot of time and calculation to process efficiently. We, human, can still continue to do most of these tasks manually, but the time and effort involved are cost-prohibitive.
Until the introduction of AI into the mainstream, businesses might recognize the value of such activities but would have had to let those opportunities go as a matter of practicality. Now, with a nearly infinite amount of processing power available within “the cloud”, tools are being developed to monitor and analyze previously impossible amounts of business intelligence.
Content performance analysis is one of the areas where these new capabilities will begin to impact content marketers in the near term. Imagine being able to instantly calculate not only the number of social media views your content receives on Twitter, but the number of shares by accounts that have also shared your competitors’ content within the last 30 days.
Producing the results of such a query would require the Twitter API for an initial list of engaged accounts and then requiring multiple times for every account in that initial list. If you’re a big brand with hundreds or thousands of engaged readers, this quickly becomes a mountain of data to sift through and analyze.
That amount of data is infeasible for any human worker to manage and would have taken a traditional piece of software hours to process.
Even “weak” AI capabilities help to process all the data, and perform many complex calculations, requiring less time and providing much better insights. Tools like Acoustic, Knotch, and even Google Analytics could be a good fit place to start with.
The lines between performance and predictive analytics are blurry since one process informs the other, but one thing is clear: the data we generate today will provide the insights to improve tomorrow.
As the world around us becomes more intelligent our tasks will take less time, require less effort, and yield results on a larger scale. It’s inevitable, but it’s also necessary.
The world is getting smaller but there are a lot more people in it than ever before. That’s more opportunity, more responsibility, and well…just more work to be done.
We would utilize new technology and Alogos on our digital transformation journey. Marketing teams are almost universally understaffed for the volume of activities they’re asked to manage. The automation of the future will have us outsourcing less because the software will be able to do more.
It is the perfect moment, start point if you will, to embrace the change and to look at the AI revolution as an opportunity to do much more with the given time.
A machine-enabled future for humanity is extremely exciting and we are happy to be a part of it.