Automation through AI: A New Era in Marketing
Marketing automation is not new, however AI is now part of the marketing vocabulary, with many content creators and brands using AI to create short-cuts to scale out content quickly. This evolution has revolutionised how businesses approach digital marketing strategies.
The Role of AI in Modern Marketing Strategies
Tools like ChatGPT and similar AI technologies are increasingly important to optimising content creation processes. For insights into leveraging AI for marketing efficiency, explore how an SEO Agency can integrate these innovations into your strategy.
Optimising Content Creation with AI Tools
AI gets a bad rap; it is simply a technological development that people abuse because everyone wants to “increase productivity” without having to think. Marketers have become lazy.
AI content systems such as ChatGPT have been around for some time with many existing in the SEO sector – scraping websites feeding this data into an ML language model so the tool can learn how to better optimise the page. This is something that used to cost around £100 per month but after ChatGPT this tool does it better and far cheaper.
Google’s Response to AI-Generate Content
The recent algorithm update as part of the web spam team aims to eliminate “low quality” scale out content such as ChatGPT outputs. Like I say, marketers have become lazy – most just copy and paste the output from ChatGPT and spend ages being clever building complicated prompts.
Understanding Google’s Deep Learning and Algorithm Updates
Google is not dull. In 2014, Google acquired DeepMind a UK based AI research laboratory to further develop their deep learning capabilities. In 2015, they created AlphaGo an algorithm to beat an 18-times world champion in the game Go a simple game in concept but generally regarded as more difficult than chess due to the size of the board and the possible configurations a player can make. It has been stated that there are more possible board configurations in Go than there are atoms in the universe. Since then, there have been further developments in the AlphaGo model.
Challenges of Relying on AI for Content Creation
Google have gone on record stating that they use deep-learning models from around 2015 within their search algorithms to aid information retrieval and provide most times the top 20 pages in the SERPs.
Therefore, it would be naïve to believe (as many SEO’s do on social media) that Google wouldn’t be able to spot AI content without even trying that hard. In fact, although these tools are basic a free AI pattern detector can with relative ease spot AI content.
Detecting AI Content: Google’s Approach
In fact, forget that an SEO practitioner can easily spot AI content as there are certain words, phrase structures and NLP factors that are so plain to see that it wouldn’t take Google long to build a script into their algorithm to find these.
Also don’t forget ChatGPT is trained on the same basic language model as Google was many years ago. Google is smart, has its own data and has approx. 10 years gain on OpenAI.
Common Patterns of Penalised Sites
There is a general statement used within data science “if a human expert can spot the pattern it is generally not worth the effort or time for data science to “discover”.
Machine learning algorithms exist to assess large data-sets to discover complex patterns undetectable by the human eye. All Google would have to do is a) feed in human rater signals checking suspected AI content or b) create thousands of prompts into OpenAI to pull the outputs into a ML dataset for it to run through multiple programmes to define a common sentence structure and common words not used that commonly within its corpus of billions of sites.
For example, if a word “craft” is not used that frequently outside its parent topic sector “crafting, making something” and used on B2B sites in a less frequency than the baseline then Google could easily test the assumption that this term is more AI than natural.
Recent sites penalised by the web spam team as below had the following patterns:
/100% published AI content
/90% use of AI generated articles within copy
/Sites promoted through social showing AI content being used
/Use of ad content
/Multiple low quality programmatic pages (non-AI but pattern based)
/New sites, low DA having thousands of pages in a short-time frame
/Sites created using expired domains for ranking purpose onl
So best to stay away from unnatural site scale out and unhelpful content.
Leveraging AI for Marketing Success in 2024/25
How can AI help marketers in 2024/25:
/ChatGPT, Claude, etc. can be used for research and feeding datasets into the engine to prompt specific results
/Perplexity – citations and higher quality source data
/Content AI tools – checking coverage of terms based on sector/topic
/Image AI – no more use of stock imagery
/Video editing – ideal for social, content marketing and podcasts
/Python scripts – automation of SEO tasks (plus build your own tools so you know “exactly what is in there”
/Sora – create videos from text new product from OpenAI
/Zapier – automate data inputs into your email/internal messaging channels (Teams/Slack) or build datasets based on filtered records to integrate into project management/ CRM/marketing reports
Best Practices for Using AI in Marketing
Looking ahead, AI continues to redefine marketing strategies, from content creation to data analysis and automation. Embracing AI tools like ChatGPT and others can enhance productivity and innovation in your marketing efforts. However, marketers must balance efficiency with maintaining content quality to avoid penalties from search engines like Google’s recent algorithm updates.
For assistance integrating AI into your digital strategy, consider consulting with an Ecommerce SEO Agency.
