AI technology is changing the way you market already. In fact, it's been doing it for quite a while, just most marketers didn't really think of it as AI. The recent onslaught of Generative AI tools provides unprecedented opportunities to increase output without increasing resources.
In this article, we’ll explore:
AI learning models
Practical ways to start implementing AI in your tech stack
Sales pitch: Want somebody to guide you through onboarding your AI stack? Talk to me
Understanding AI Learning Models
AI is changing the rules of the game. Those who don't learn the new rules will find themselves playing alone.
There are 4 different types of machine learning (ML) algorithms used in AI tools available for marketers today:
Supervised Learning (SL) algorithms are trained on labeled data, meaning that the inputs are labeled with specific outputs. SL in marketing predicts behavior, such as which customers are likely to churn or which products they will likely buy.
Unsupervised Learning (UL) algorithms are used with unlabeled data, meaning that they must identify patterns and relationships on their own. UL in marketing segments customers and identifies new opportunities.
Reinforcement Learning (RL) algorithms learn by trial and error. The algorithm receives feedback on its actions and adjusts its behavior accordingly. RL in marketing optimizes marketing campaigns and improves customer engagement.
Deep Learning (DL) uses neural networks to simulate the human brain. DL is used for text, image and video creation and much more. This is the latest advancement being used by ChatGPT and other generative AI tools.
Generative AI uses Natural Language Processing (NLP) algorithms to perform the chat function where the human writes a prompt in natural language and the AI spits out a blog or social media post.
AI-driven marketing goes beyond just using ML algorithms. It unlocks the potential of these solutions to optimize customer experience and target customers more accurately. To truly take advantage of AI, we must combine the creativity of the human marketer with the analytical capabilities of the machine.
Getting Started with AI-Driven Marketing
I decided to delete the section about AI benefits because that's well documented. Let's get practical. How do you start implementing AI-driven marketing today?
There are 2 main paths to take and they are similar to adopting any other technology.
Use case method
Problem solver method
Use Case Method
The use case method is exactly what it sounds like. Brainstorm a bunch of use cases and score them to see which will provide the most benefit to you and your team.
When brainstorming use cases, ask yourself the following questions:
Is my task data-driven?
Is it repetitive?
Is it making a prediction?
If the answer to any of these is yes, then put it on your list.
For each use case, assign a cost for your current investment (work-hours, tech, etc) and a cost for using AI. When AI adoption is lower, it will bring you a positive ROI.
Today I spend 15 hours a week writing social media posts. With AI writing starters for my posts, I will only spend 7 hours a week. My AI license costs $18/month.
My hours cost $50 (you wish!)
So we calculate the current investment:
15 hours * 4 weeks * $50 = $3,000/month
An then we calculate the cost when using AI:
7 hours * 4 weeks * $50 + $18 license = $1,418
Adopting this tool will save me:
$3,000 - $1,412 = $1,582/month
(15 - 7 hours) * 4 weeks = 32 hours/month
Use Case Method Continued
Based on value, choose the top 2-3 use cases for your team. Each use case should by owned by someone who understands the task well.
The owner will then evaluate the tools on the market and create an adoption plan with whichever team members will be using the tool.
Don't be afraid to purchase licenses; most of the generative AI tools out there have a fairly low licensing fee and their freemium version is great for testing.
Run a test for 30-90 days and you will see how the work is affected and if this tool is right and profitable for you.
Problem Solver Method
This method is a bit more intense, but it has the potential for a larger impact on your business.
In this method you find the key problems facing your team. Define your problem clearly using a SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) statement. Statements like:
We increased blog production 5x to boost organic traffic and are only seeing a 5% increase
Our $50,000 media spend is only bringing in $55,000 in revenue
Once you have your problem statements, define the value that will be gained by solving it. For example:
If our investment in organic content increases traffic 20% instead of 5%, conversions will increase 10%, leading to an additional $100,000 in revenue
If we increase our conversion rate on paid ads by 16% we will see an increase of $250,000 in revenue
Keep in mind compounded value for quarter on quarter and year on year.
Once you have your goals set, it is time to start ideating how to solve the problem and which tools will help you get there.
Generative AI is super new and it will change so much over the next months, let alone years with its new level of accessibility and simplicity. You can't afford to fall behind. It's time to take your AI Marchitecture seriously.
Want some advice? Get in touch 😎
** This blog was written in collaboration with generative AI from https://writer.com/
by Yoni Grysman on April 04, 2023
Yoni is our Director of AI marketing and senior marketing strategist. He is certified by the AI Marketing Institute and as a HubSpot trainer. Yoni helps companies adopt generative AI tools in their tech stack and works with AI generated content to produce the ultimate assets in record time. Yoni runs marketing strategy for clients from various industries including automotive tech, cybersecurity, finance and more. Yoni's not-so secret, marketing secret? Everything in marketing comes down to goals and audience. If you don't know who you're talking to and what you want to achieve, you're shooting in the dark.