In my career spanning over 2 decades as a marketer, I have witnessed a huge shift in how companies reach and engage their customers. It's almost a cliché to say that Internet, and technology in general, have been the biggest drivers behind this shift. And it's not just the marketers, it's pretty much every human being on the planet. The biggest advantage marketers have got from all the changes that have happened is that they are able to reach their customers, tell compelling stories to engage with them, convert them, and even find new customers whom they could not reach earlier.
We are now witnessing a new generation of technology called AI. Contrary to those who diss AI as just another bubble waiting to pop, I think that AI is a technological advancement that falls in the category of machines, TV, and computers. It has already started to prove beneficial in different aspects of businesses and different walks of life. Particularly for marketers, AI has delivered jaw-dropping use cases in amplifying brand awareness, engagement and conversion, that have gone beyond challenging the status quo. Like a superhero who is constantly under scrutiny, AI is staring at marketers and asking, "What are you waiting for, Christmas?"
Still, a lot of marketers are on the fence. Not because they're afraid of trying something new. It's because there are aspects of AI that aren't fully understood or carry risks that can be hard to manage. Here's the story about this superhero who has helped marketers behind the scenes, brought their efforts to the main stage, thrilled the audience, but still has a lot to prove...
Early years of AI: How has the superhero been helping marketers from behind the scenes?
Let's go behind the scenes to see how AI has helped marketers for over two decades and AI is helping them still. As e-commerce and the Internet grew in the 1990s and 2000s, marketers got access to tons of data. However, it was hard to make sense of the data in the beginning. This is where early AI tools like web analytics and SEO came in. Marketers could use web analytics to see how their site does, what keywords are trending, and how users behave. Analyzing website traffic could show which pages were doing well and which ones needed work. The behavior analytics provided insight into how users interacted with the website, like where they clicked and how long they spent on each page. Marketers used the information to optimize the website.
In addition, SEO tools helped marketers figure out what keywords customers were searching for and optimize their content accordingly. As a result, the website got more organic traffic from search engines.
Big Data, cloud computing, and advanced AI techniques like NLP and deep learning transformed how marketers reached, targeted, and engaged their customers. As more marketers adopted digital marketing, datasets got bigger and more complex. Customer records, social media activity, website clickstreams, and e-mail marketing data all made up this Big Data. Accessible and cost-effective ways to store all that data were needed. That's what cloud computing solved for marketers. Using these technologies together led to advanced AI techniques like NLP and deep learning.
An average user also used AI-based features online without realizing it. The early 2000s saw recommender systems take off. Do you remember those "People Who Bought This Also Bought..." sections on Amazon? It was AI, analyzing purchase patterns to suggest relevant products. Music streaming services like Pandora created personalized playlists based on your listening habits with AI.
The AutoComplete feature on Google Search has been suggesting related search terms to its users. There's now a 'People Also Ask' feature next to every search result. Thanks to AI.
Then there were AI-powered assistants like Siri, Google Assistant, and Alexa. Pretty soon people were using them to set alarms, answer questions, and even control smart home devices. People have grown used to interacting with AI-powered chatbots and virtual assistants in real-time, getting answers to their questions, getting help, and being guided through the purchase process.
Let's see how AI is helping marketers and making this easy for them
These are just a few of the AI-powered features and functionalities that have enriched the online experience for users and given marketers more ways to connect with them. AI has evolved quite quickly in the past few years, and it's had a huge impact on how businesses market themselves online. Among the biggest benefits AI has brought to marketers is the ability to rapidly create content like product descriptions, social media posts, blogs, video scripts, and personalized emails.
Based on specific demographics, interests, and online behavior, AI can also help marketers serve hyper-personalized ads at the right time to the right audience. Hyper-personalized ads and scaled content boosted the pull factor for marketers. These activities piled up data that needed to be crunched and analyzed. AI helped there, too. By analyzing historical data and customer interactions, it can predict future behavior. Marketers can then personalize the customer experience, recommend relevant products and services, and address issues proactively.
The future AI is likely to lead marketing into…
AI brings big benefits to marketing organizations in a variety of ways. The fact that these benefits are continuosly growing is a sign that AI will make it all very interesting and exciting for marketers.
For one, content production will go beyond text and images, and it'll be faster, more efficient, and better. There's a lot of good examples of that, like Stable Video Diffusion and Sora. If the promo videos of these tools are anything to go by, marketers are looking at times when developing photorealistic videos will be rapid and cost-effective. Optimizing and customizing that content for SEO, social media, content marketing, PPC, e-mail, and affiliate marketing could be a breeze.
This leads to new features and features being offered by digital platforms like TikTok, where users can upload long-form videos, and YouTube, which has AI-based conversational AI and comments summarization. All of that means even more need for new content and content formats like FOOH and XR. It's likely that AI will fill a lot of these needs.
Personalized targeting by dynamic creative optimization or DCO is another way. The Google Responsive Ads are a good example. You can give multiple titles and descriptions, and the algorithm will create an ad creative based on search intent. Both responsive ads and Performance Max campaigns have been around and will stick around for a while.
Then there's AI-based platforms like 10Web that help deploy websites fast. The website can be built in a fraction of the time and resources, and they can be optimized faster to keep users engaged.
... but some mess still needs to be cleared out.
With so many AI-backed tools and marketing use cases popping up, a little bit of chaos can be expected. Obviously, this means AI implementation has wrinkles that need to be ironed out.
I talked about this in an earlier episode. As a technology, AI still needs to mature. I think maturity is when its applications have gone through enough iterations to start becoming commonplace. It's a bit chaotic where AI is now compared to where we want it to go. Marketers know this. Business cases for adopting AI sit at the intersection of potential and expected outcomes. A chaotic situation needs cautiousness to avoid hidden surprises, even if outcomes are delivered.
Look at what AI can do for content production. I know marketers who write blogs on the fly, review them quickly, and publish them to expand their reach. It's one of the best use cases for AI in marketing. Once you peel back a few layers, you'll see major problems. Ethics is the most talked about topic. Which data sources are being used to train the language models? Does it come from a legal source?
There's also AI cannibalism, where AI publishes so much data that its language models use it to train. There's a possibility that AI is eating itself. This is especially true for public LLM solutions. That makes the case for private LLMs that are well-curated and supervised.
How does this affect marketers? Do they have to build their own LLM? Maybe, but it takes clean data. AI trained on public data will probably produce garbage. There's a lot of data sitting around in businesses, a lot of it siloed. Gathering all that data, sifting through to find the most relevant pieces, cleaning them, and preparing for analysis is time-consuming. Procuring clean data from third-party providers is an option but it comes with its own set of challenges and risks.
Using an AI-based tool that sits on an off-prem server is something that seems practical given some of these risks. As we move into a world where strict data privacy norms and hyper-personalization must co-exist, AI-based tools and platforms need to improve a lot more. The superhero has to learn to control the superhuman power.
It's really all about trust, if you look closely. Even with its magical powers, AI is not Dr. Strange. It is more like Hancock who saves innocent people but leaves a big mess behind for the city to clean up. Having said that, it is important to remember that it is us humans who have created AI. It is up to us to help Hancock transition into Dr. Strange. Let's not forget that trust is a human trait, not something a machine can do!
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