Artificial intelligence or “AI” seems to be the new hot term—and for good reason.
Over the past decade, artificial intelligence has rapidly progressed with new data sets, open source code, and an overall growth in interest for the industry. As a result, we’ve seen the emergence of tools and literature that do everything from optimization to analysis and even creation.
In this blog, we’ll dive into the impact of AI, on the marketing world, and the things you should pay attention to.
Before we dive into the nuances of AI within the marketing space, let’s broadly define what “artificial intelligence” means. Please note for the purposes of simplicity, we will be broadly categorizing “AI” “Machine Learning” and “deep learning” as “AI.” Let’s look at this from a few different perspectives
Oxford Dictionary defines AI as: The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this.
A more advanced definition can be found in Stuart Russell and Peter Norvig’s textbook: Artificial Intelligence: A Modern Approach, a literary staple in the AI field. Russell and Norvig break down AI on the basis of thoughts and actions:
AI can be further segmented into categories such as “narrow” or “weak” AI vs “general” or “strong” AI. However, we will not dive so deep into technicalities in this blog.
Chat GPT by Open AI when prompted with the question “write a short paragraph about the following category → what is AI” responds with:
“AI creates intelligent machines that can perform tasks normally done by humans. It can analyze data, recognize patterns, and make decisions based on that data. AI has many applications, such as speech recognition and self-driving cars, and has great potential to transform industries and improve our lives.” (please note we asked Chat GPT to simply its original response 2 times)
Our view on AI can be summed up as a combination of all the definitions above. AI attempts to simulate intelligent behaviour: both rational/objective and also irrational/creative. AI can execute complex tasks such as predictive analytics or content creation. On the other hand, AI can simplify work processes and handle some routine tasks such as email marketing or content creation. All in all, AI aims to emulate the aspects of human behaviour that are “productive” in an effort to enhance our everyday lives (after all, we created them).
Now armed with a baseline understanding of AI, let’s look at a few examples and their impact on the marketing world. In general, the more “depth” (specificity) a tool has, the more useful it is to marketers. While tools like ChatGPT have a significant breadth, they can be difficult to apply to specific marketing initiatives. In this section, we’ll go over some broad categories of AI tools and talk about their practical applications.
AI’s implication on content creation is twofold. AI is capable of creating content from scratch with some hand-holding, but it is also concerned with optimizing the content creation process.
Copy creation tools can broadly be categorized as AI platforms which generate copy for various situations. Some common use cases include copy for emails, social captions, blogs, and website copy. Common tools within this category include Jasper.ai and Anyword, however, there are specialized tools like Ocoya which are specialized for social media copy. These tools make the copywriting process more seamless and efficient, saving time and increasing impact.
Media creation tools are similar to the category above, but it concentrates on visual content. These tools tend to be more specialized depending on the media type and usage scenario. For example, on the video front, there’s Lumen5: a drag-and-drop video maker with a unique feature, simply upload a blog or slide deck and the tool will generate a video for you. On the other hand, tools like Synthesia focus on the video creation experience, enhancing video editors with tons of voiceovers, text creation, and more. Midjouney is another popular media creation tool that blew up due to its ability to generate stunning imagery with prompts and edits while tools like Murf serve niches, in this case, AI voice generation.
Put simply, automation allows us to do more with less direct input (the term “set it and forget it” applies here).
The most versatile example in this space is Zapier, which connects apps without the need for code. Zapier supports an incredible amount of interactions, from creating lead notifications from paid ads, sending automated emails, organizing your CRM, or updating analytics.
In the social media space, tools like Lately and Hootsuite are big players which assist with automating social media management. On a basic level, these tools aid in automating posting on a variety of social platforms, but these tools can also utilize data for recommending ideal posting times.
On the customer engagement front, Chatbots are a popular tool for marketers to engage live visitors. Platforms such as Hubspot or Zoho allow users to create a chatbot with pre-existing conversations, tracks, and templates. These provide a much more engaging way of providing information with the added plus of providing a conversion point. Newer tools like Chatfuel takes this a step further with integrations for Instagram, Facebook, and even Whatsapp.
Needless to say, without automation, each of the above actions would require manual human input.
Data informs all our decisions and allows us to make calculated decisions. A challenge facing any marketing team is sorting through all the information for actionable data.
Google Analytics is probably the most popular and widely used Ai tool in this sphere. At a basic level, it aggregates everything you’ll want to know from a web traffic standpoint. Analytics goes above and beyond with a few key features such as Insights, which analyzes data and provides opportunities, e.g if a certain landing page is performing better than normal. Analytics also makes organization and modelling simple, with smart lists, smart goals, and conversion probabilities. This also extends to Google’s other tools, for example, Google Ads provides adword recommendations based on groups/lists.
On the SEO front, popular tools like Surfer SEO and Ahrefs provide a ton of value in analytics and optimization. Ahrefs utilizes AI to generate a variety of keyword recommendations from a parent term which can be further expanded with additional parameters (location, keyword length, etc). This allows SEO teams to make sense of a large amount of data quickly, and target the keywords which will have the most impact. In terms of optimization, Surfer scans any webpage for existing tactics and optimization opportunities. This allows you to emulate your competitor’s best practices while covering your weaknesses.
In this particular section, we could find examples of tools for each and every segment of marketing. All in all, AI makes data more consumable and provides practical recommendations to help teams make effective decisions.
So what next? Will chatGPT 4 become sentient and run the world’s most kickass marketing agency? Will Google release an algorithm update leaving all AI content null?
The truth is, no one really knows. What we do know is that AI aims to emulate rational human thoughts, so we can make some predictions on the categories we provided above.
Content will have to be more targeted and niche to stand out. With the barrier of entry for creating content becoming lower and lower, quality will win. Companies that rely on volume-based content marketing may see a reduction in results. Teams will have to put more time and effort into ideating and understanding consumer pain points to truly make a splash on the content front.
In a similar vein, organizations will be forced to do some form of content marketing. B2B companies can no longer neglect verticles like social media, especially when competitors are making their presence known on Instagram.
As automation improves, marketing teams will become more specialized and lean. Team members can look forward to strategic and creative work, with less focus on routine tasks (e.g copywriting, posting content, manually importing and managing leads). This will lead to teams which have more diverse niche skill sets.
Automation will also continue to enhance the already emerging presence of fractional roles. More companies are looking to fractional CMOs/Directors/VPs for high-level marketing assistance. Automation only continues to enhance the effectiveness of these roles, making them more attractive for companies.
The future of analytics is bright with AI. Due to AI’s efficiency in data analysis, marketers will no longer need to sort through data. Instead, their time will be focused on making sense of said data and making decisions. Furthermore, AI will likely be able to find correlations between stats that marketers were unable to see (for example, perhaps we were calculating ROI incorrectly as we were not considering the customer lifetime value).
Summed quickly, marketers will be able to make better decisions, faster.7