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Predicting Consumer Behavior With AI: Do Machines Feel?

Have you ever asked yourself how some shopping apps seem to know exactly what you’re in the mood to buy before you even do?

How Spotify seems to always recommend the perfect playlist based on your current mood?

Or how Apple Maps knows your home address even though you never saved it to your iPhone? 

Well, the answer is Artificial Intelligence (AI), and it has revolutionized the way businesses predict consumer behavior.

But why all the hype about AI and why has it become the wingman for every marketer around the world?

In simple terms, I feel like every business owner wants that super-smart robot buddy that can learn, adapt, and make decisions for them.

I’m not talking about robots taking over the world and all that hoopla. That’s a Roko’s Basilisk story for another day.

What I’m referring to is systems that can understand, learn, and work smarter to make our lives easier.

There’s a lot to uncover on this subject and if you’re someone who does not like to geek on the science of things.. well, this can get very boring.

So I simplified it as much as I could and not only made it interesting but super easy for you to read.

You’ll not only grasp the basics of how the whole thing works but maybe also appreciate how AI impacts our daily lives.

Who knows, maybe by the end of this, AI might even predict that you love this article!

Table of Contents

Theoretical Background - What Happens Behind The Scenes

Remember back in the day when everyone relied on physical locations and traditional advertising to reach their customers?

Back when success hinged on perfect location, print ads, billboards, and so on?

Fast forward to today and you can see how there’s been a seismic shift.

All it takes now is a few clicks and a store owner in rural Alaska can appeal to customers all the way in Brisbane.

As far as AI goes, there’s a lot that happens behind the scenes to make that possible.

You might have heard of:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing

I’m no AI engineer so initially all of those things sounded like a bunch of techy mumbo-jumbo to me.

And I’m not going to claim I now understand all the intricacies, but to save you the same hassle I went through, I’ll break them down for you like a piece of cake.

Machine Learning: The Quick Learner

Imagine you own a dog and you’re trying to teach it to fetch the newspaper every morning.

You reward it with treats every time it gets it right and as time goes by, it learns exactly what to do.

Machine Learning (ML) works in a similar manner but instead of dog treats, it relies on data.

The more data you feed it, the smarter it gets, and it learns from patterns and past behaviors to make predictions.

If we bring that back into the world of consumer behavior, Machine Learning is like the eager puppy ready to learn what customers want – even before they know it themselves!

Deep Learning: The Brainy Bunch

This one was a little bit tougher to simplify.

But picture a group of scientists (aka neural networks) having a round-table discussion where they’re trying to solve all sorts of complex problems.

Deep Learning uses layers of those “scientists” to analyze data and make sense of the intricate patterns that are too complex for us (or traditional ML algorithms) to understand.

This is how businesses get insights we never thought were possible.

Natural Language Processing: The Smooth Talker

Know all those chats you’ve had with Siri, Alexa, or any other virtual assistant? How are they capable of understanding you?

That’s Natural Language Processing (NLP) at work. It’s all about teaching machines to understand and interpret human language.

NLP helps businesses understand customer sentiments, reviews, and feedback, then turns all of that into actionable insights.

Predictive Analytics: The Crystal Ball

When you bring everything together, what you end up with is Predictive Analytics which many people have now coined the crystal ball of the business world.

I read this article about Large Market Models (LMMs) and it’s amazing to see the shift in AI application towards predictive and prescriptive analytics.

We’re no longer relying on just historical data or current trends to predict future consumer behavior but shifting from reactive to proactive strategies.

Imagine knowing what your customers will want, when they’ll want it, and how much they’re willing to pay for it – very few business owners who wouldn’t want that type of knowledge.

Importance of AI in Predicting Consumer Behavior

End-use industries have started integrating artificial intelligence into their business processes to streamline their operations.

I’ll start with a few startling numbers I found online:

  • The global AI market was valued at USD 454.12 billion in 2022 and is projected to reach USD 2,575.16 billion by 2032 (Precedence Research).
  • 9 out of 10 organizations believe AI can give them a competitive edge over rivals (MIT Sloan Management).
  • 77% of devices in use feature AI in some form (Adobe).

Forbes also has a very interesting article with all sorts of AI Business stats you can read here.

So, why the big shift?

Why are businesses ditching traditional market research for AI-driven research?

Why have the questions shifted from whether businesses should jump on the AI bandwagon to how fast they can do it?

Let me show you why.

1 - From Gut Feelings to Data-Driven Decisions

Gone are the days when marketing decisions were made based on gut feelings or HiPPOs (Highest Paid Person’s Opinion).

Nowadays data does all the talking. The problem is there is lots and lots of data but only so much time to go through all of it.

AI can sift through the mountains of data and identify patterns that might have taken us months or years to figure out or maybe even overlooked altogether.

Companies can now harness the full power of their data and make decisions that are backed by solid evidence instead of just intuition.

2 - The Need for Speed (and Accuracy)

Waiting weeks for market research reports is like waiting for a glacier to move.

Nobody has all that time to spare – not with competitors popping up left and right.

AI gives you real-time data processing that allows you to make swift, informed decisions.

Throw in its ability to learn and adapt and AI’s predictions are not just fast but also freakishly accurate.

It’s like hitting the bullseye every single time.

3 - Personalization

My partner drinks A LOT of coffee. Like she needs a sponsorship from Starbucks too much.

Every time she drives to work she orders 2 coffee drinks. One for now and one for later as she calls it.

The day before I wrote this article we were headed up the mountains to go snowboarding.

We drove up to Starbucks and she ordered her usual but didn’t get a second drink.

Well, she has a particular way of ordering her Grande iced shaken espresso with 2 pumps of toffee-nut, no classic, in a venti cup with extra vanilla cold foam.

So much so that the barista knew exactly who she was and asked if wanted a second drink for later.

You could see the elation in her face.

That’s personalization at a basic level. Now imagine that on steroids.

AI analyzes individual consumer behaviors, preferences, and interactions to offer personalized experiences at scale.

It’s like every brand suddenly knows whether you want that special coffee for now AND later.

4 - Efficiency: Doing More with Less

Like it or not, AI is the ultimate productivity booster.

I’m going to give you an example that is probably of little significance to you but highlights exactly what I mean.

Take out your phone and start typing a text.

Do you see any suggestions? What about when you make any typos? Is there autocorrect? Does it seem like it understands the context of your message?

That’s AI in its “simplest” form finishing and correcting what you have to say.

You don’t have to spell check everything or type out complete sentences.. aka productivity.

Same applies to business but on a larger scale.

Know all the repetitive and time-consuming tasks? AI can handle all that mundane stuff.

All the time spent on consumer analysis and predictive behavior is now allocated to different strategies.

Now you know where to invest your time, money, and efforts for the best returns.

5 - Dynamic Pricing

Ever noticed how flight prices seem to fluctuate more than a yo-yo on a caffeine rush?

That’s dynamic pricing at work and AI is the mastermind behind it.

Unlike traditional pricing models that remain fixed over time, dynamic pricing adjusts in real or near-real-time to respond to changes in supply and demand, competitor pricing, customer behavior, or other external factors like time of day, weather, or special events.

This is how AI comes into play:

  • Data Analysis: It can process huge amounts of data very quickly.
  • Pattern Recognition: AI can identify market trends and consumer demand.
  • Predictive Analytics: The ability to forecast market changes that allow for proactive price adjustments.
  • Real-Time Decision Making: Instant price adjustments in response to market fluctuations.
  • Personalization: Spoke about this in point #3.
  • Automated Optimization: It continuously refines pricing strategies, ensuring ongoing alignment with business goals.

That’s a lot of challenging, data-intensive work which AI can make a lot more manageable.

It’s the ultimate art of the deal such that the price is always right.

6 - Enhancing Customer Engagement & Building Brand Loyalty

AI has taken customer engagement to new heights and created experiences that are not just engaging but also deeply satisfying.

We have AI chatbots that provide instant customer support, AI-driven product recommendations that personalize your shopping experience, voice assistants, sentiment analysis. The list goes on and on.

Many businesses are leveraging those algorithms to turn casual customers into loyal brand advocates.

Speaking of..

How Does AI Predict Consumer Behavior?

This is the part that ruffles a lot of feathers because AI doesn’t just look at the ads you click or products you browse, it is capable of understanding subtle nuances of your digital footprint.

From the time you spend on specific pages to the way you navigate through a site, every action feeds into the AI’s learning and allows it to predict what you’ll likely be interested in next.

That predictive power is what makes AI an invaluable asset for businesses – both good and bad.

For now let’s focus on how they can “read your mind.”

1 - Data Collection

This is what I like to call the treasure hunt.

There is a lot of data everywhere.

From your browsing history, what you like on social media, things you buy, and even your interactions with customer service – every click, like, or swipe is a piece of the puzzle.

AI is that relentless detective who gathers all those clues to understand your habits, your preferences, all the way down to your pet peeves.

2 - Pattern Recognition

Once you have all that data, you need to do something with it.

This is where AI connects the dots.

It pieces everything together and finds patterns and trends that might escape the human eye.

All of a sudden something that may have seemed random starts to make perfect sense.

3 - Predictive Modeling

This goes back to the crystal ball analogy. Trying to predict future behavior.

Will a customer prefer product A over product B?

When are they most likely to make a purchase?

How does pricing affect sales?

AI’s predictive models can answer thousands of questions for you with uncanny accuracy.

AI Tools For Marketing and Consumer Behavior

The AI tools used in predicting customer behavior can be classified into 2 main categories:

  • Backend AI
  • Frontend AI

A - Backend AI

These are the models that process the data, learn from it, and give you all the insight you need.

They often need customization or some sort of development effort to apply in a specific business context.

Basically everything that gives you broad capabilities and works behind the scenes.

B - Frontend AI

This is everything you or your customers interact with.

They’re more accessible to the end-user without exposing the complexities of the technology “behind” it.

The table below shows you the differences between both categories.

Backend AI
Frontend AI
Also Called
AI Core, AI Engine, General AI Tools
AI Interface, AI Applications, Specialized AI Tools
Primary Focus
Data processing, learning algorithms, and insight generation.
User interface, engagement, and experience enhancement.
User Interaction
Minimal direct interaction; operates behind the scenes.
High direct interaction, often through conversational interfaces or personalized content.
High, due to the complexity of algorithms and data processing.
Varies, can be less complex due to pre-built models and APIs.
Requires significant customization to fit specific use cases.
Less customization needed; often uses pre-trained models or services.
Typically involves more extensive development and integration efforts.
Easier to implement with existing platforms and services.
Visibility to End-User
Usually invisible; its outputs inform frontend applications or decisions.
Highly visible and interactive, directly affecting the user experience.

Down below you have some examples from the different categories and their purpose.

Examples of Backend AI Tools for Customer Behavior Prediction

Machine Learning Platforms
Design, train, and deploy machine learning models.
Amazon Sagemaker, Microsoft Azure ML, RapidMiner
Data Analytics and Processing Tools
Process and analyze large datasets.
Apache Kafka, PixelPlex
Predictive Analytics Software
Identify the likelihood of future outcomes based on historical data.
CRM Systems with AI Capabilities
Incorporate AI to offer insights into customer behavior and personalize interactions.
Salesforce Einstein, HubSpot, Zoho CRM
Natural Language Processing (NLP) Tools
Analyze consumer sentiment and feedback from various sources.
Google Cloud Natural Language, IBM Watson NLP, NLTK
AI-powered Recommendation Engines
Generate personalized recommendations for users.
Recombee, Coveo
Behavioral Analytics Platforms
Analyze and interpret complex customer data from various touchpoints.
Mixpanel, Amplitude
Deep Learning Frameworks
Develop neural networks for modeling high-level abstractions in data.
TensorFlow, Pytorch, MXNet
Computer Vision Tools
Analyze images and videos to understand consumer behaviors and preferences.
OpenCV, Scikit-Image

Examples of Frontend AI Tools for Customer Behavior Prediction

Generate and optimize ad copy and creative content using AI.
ChatGPT, Jasper, Copy.ai
Content Management
Manage and optimize content creation, curation, and distribution.
WordPress with AI plugins, HubSpot CMS Hub
Content Editing
Enhance writing quality and readability with AI-driven suggestions.
Grammarly, Hemingway Editor
Email Marketing
Personalize and automate email campaigns based on user behavior.
Rasa.io, Mailchimp, Klaviyo
Task Automation
Automate repetitive tasks and workflows using AI-driven tools.
Zapier, Microsoft Power Automate
Improve personal and team productivity with AI-enhanced task management.
Notion AI, Asana, Atlassian
Optimize advertising campaigns with AI for targeting and bid adjustments.
Google Smart Campaigns, Braze
Provide automated customer support and engagement through conversational AI.
DevRev, Smith.ai
Sentiment Analysis
Analyze customer sentiment from text data in reviews, social media, etc.
Brandwatch, MonkeyLearn
Deliver personalized user experiences in e-commerce, websites, and apps.
Bloomreach, Twilio Customer AI

That’s a very, very small sample of all the AI tools that can be used in business but they give you a glimpse into all the different things you can do to understand your customers.

If you’re a business owner it comes down to finding the right tool for the job at hand.

Which means clearly defining your business needs, objectives, and any challenges you’re currently facing.

Challenges and Ethical Considerations

Infographic on AI challenges in consumer behavior prediction, highlighting privacy, bias, and ethical considerations, in a vintage comic style

They say with great power comes great responsibility, AI is no exception.

The more AI becomes integrated into our daily lives and critical decision-making processes, the bigger the questions and concerns.

Let’s tread those murky waters.

1 - Privacy Concerns

Privacy (or lack thereof) has become the golden goose everyone’s after.

The huge amounts of data AI systems consume to learn and make decisions has raised some significant privacy concerns.

And it seems like the line between personalization and privacy invasion is getting blurrier by the day.

It’s like snowboarding. Lean too far one way and you’ll find out snow isn’t as forgiving as it may seem.

Same applies to consumer trust. Striking the right balance can definitely a challenge.

2 - Data Security

The more data you have, the more vulnerable you become.

One breach and the whole thing will come crumbling down.

According to Cybercrime Magazine, 43% of cyber attacks target small businesses and 60% of those victims are usually out of business within 6 months. And that’s not including the customers affected.

What even makes that scarier is the fact that at least 1 small business owner falls victim to ransomware attack every 14 seconds!

Needless to say, it’s a constant battle.

3 - Consumer Manipulation

AI’s ability to predict and influence consumer behavior is a prime example of a double-edged sword.

On one edge, it’s every marketer’s dream – the power to sway consumer decisions.

On the flip side, it’s borderline manipulation.

So it raises a lot of ethical questions about the extent to which businesses should influence our (consumer) choices.

There’s a fine line between persuasion and manipulation. Cross it and you’ll find yourself in a trust deficit no AI can predict.

4 - Bias in AI Algorithms

AI is trained on massive amounts of historical data.

So if there are any inherent biases in said data, it could lead to skewed predictions and decisions.

Here are a few instances:

  • Recruitment Tools: Amazon’s recruiting tool favored male candidates, a clear reflection of gender bias.
  • Facial Recognition: Gender Shades project showed significantly higher error rates for darker-skinned and female individuals.
  • Healthcare Algorithms: Healthcare management system biased against Black patients, using cost as a proxy for healthcare needs.
  • Credit Decision Algorithms: Lots of cases of higher interest rates for African American and Latino borrowers.
  • Crime Prediction Tools: COMPAS flagged Black defendants as higher risk more often than white defendants.
  • Speech Recognition: Voice-activated AI assistants and speech recognition systems often have lower accuracy for non-standard accents.

All of those biases are like wearing tinted glasses and believing that’s how the world looks.

I’d be naive if I didn’t think we still need lots of rigorous testing and validation to mitigate some of those issues.

5 - Transparent AI Practices

There’s a reason why many AI models are often seen as “black boxes.”

Complex systems, opaque decision-making, and nobody apparently willing to pull back the curtains.

I think every business needs to show consumers how their data is used and how decisions are made.

The only way an AI-driven ecosystem works is if we build trust and make sure everyone feels respected and valued.

6 - Accountability and Liability

Who do you blame when an AI system makes a mistake or causes harm?

Is it the user? The developer? The regulatory body?

Lots of nuisances here but there’s a clear indication that we need better guidelines and frameworks to address any liability issues.

7 - Autonomy and Control

I’m not going to go into any deep theories here because they go way beyond the scope of this article.

However, as AI systems gain more autonomy, we need to make sure they operate within ethical boundaries and there still needs to be some human oversight.

It’s a delicate dance but one worth mastering for the future of ethical AI in marketing.

Real-World Applications and Case Studies

The previous section was gloomy (but necessary).

But with that out of the way, I’ll shift the spotlight to some real-world examples of businesses who’ve harnessed AI to not only predict consumer behavior but redefine the very essence of consumer engagement.

These are some captivating case studies that showcase AI’s prowess.


Shocker, right?

Amazon is the behemoth of e-commerce and has been at the forefront of the AI revolution for decades.

They have some of the most sophisticated algorithms that turn shopping into a science.

I’m sure none of this is news to you. You’ve either experienced it firsthand or you’ve heard the stories.

Netflix's Content Curation

Netflix has transformed the way we consume media, thanks in large part to its AI-driven recommendation engine.

It analyzes viewing habits, ratings, and even the time spent on titles, to curate a personalized experience for each user.

It’s like having that friend who knows exactly what you’re in the mood to watch.

There’s a reason why a Netflix & Chill’d Ice Cream exists.

Stitch Fix's Style Algorithms

Stitch Fix has taken personalized styling to a whole new level. They really help take the guesswork out of looking good.

They combine client preferences with data on trends, fashion expertise, body measurements, and feedback, to find the best clothing selections for each subscriber.

But it goes beyond the shopping experience.

Stitch Fix’s AI extends to inventory management and trend forecasting, optimizing their stock to align with emerging fashion trends and customer demands.

The one thing I appreciate about them is the amount of transparency they have in their processes. Kudos to whoever came up with their Algorithm tour.

IBM Watson's Predictive Health Insights

IBM Watson has made some serious strides in healthcare by predicting patient health trends and outcomes.

I think the push for healthcare systems that are predictive instead of reactive is a step in the right direction.

These are some of the standout examples:

  • Oncology and Cancer Treatment: Watson has been used to assist oncologists in identifying personalized treatment options for cancer patients, analyzing medical literature and patient data to recommend tailored treatment plans.
  • Clinical Trial Matching: To help match patients with appropriate clinical trials, accelerating recruitment processes and enabling patients to access potentially life-saving treatments more quickly.
  • Genomic Analysis: In genomics, Watson’s capabilities are used to analyze genetic data from patients’ tumors to identify mutations and recommend personalized medicine options.
  • Healthcare Operational Efficiency: Several instances where it has been deployed to improve hospital operational efficiency by predicting patient admission rates and helping hospitals optimize staff allocation and resources.
  • Chronic Disease Management: Watson assists in managing chronic diseases by analyzing patient data and providing insights for personalized care plans. And also helps monitor patient health and predict exacerbations.
  • Mental Health: Lots of cases in mental health care, analyzing speech and writing patterns to assist clinicians in diagnosing and treating mental health conditions.

These case studies are just the tip of the AI iceberg. You can run down a very deep rabbit hole.

AI is transforming all sorts of industries from the folks that handle your trash all the way to space exploration.

Every application demonstrates not only how versatile AI can be but also its potential to fundamentally reshape how we live, work, play, and interact with the world around us.

As the technology evolves, AI’s impact will continue to be woven into the fabric of our daily lives.

What’s Next For AI in Consumer Behavior Prediction?

Conceptual infographic questioning 'Do Machines Feel?' in AI consumer behavior prediction, with mechanical heart and brain visuals

The leaps and bounds we’ve seen so far are just the beginning.

AI is not just inching closer to human-like understanding, we’re moving towards a future where the line between technology and intuition will become practically invisible.

These are some of the things I think will redefine the landscape of predictive marketing.

The Rise of Augmented Reality Shopping

A lot of this is happening already.

Trying on clothes, testing makeup, or visualizing furniture in your home without leaving your couch.

Retail giants like Amazon, Home Depot, Wayfair, and Warby Parker have been leading the charge.

BMW and Audi use Augmented Reality (AR) to let customers visualize the look and feel of their cars.

You can even try on watches from Rolex or Tiffany & Co. with AR.

All of the advances in AR shopping enhance the customer experience and reduce some of the uncertainty that comes with online shopping.

The Integration of IoT and Consumer Insights

The Internet of Things (IoT) is weaving a crazy web of connected devices, from your fridge to your thermostat, all the way to your fitness tracker.

It’s a treasure trove of data that gives deeper insights into our daily habits and preferences.

Your fridge knows you’re out of milk before you do, your thermostat adjusts the perfect temperature even when you’re not home, your watch reminds you it is time to stand up, even cars, connected to the IoT, can provide data on driving habits. 

Yes, I’m well aware of the privacy concerns but the seamless integration of IoT and AI also opens up new avenues for personalized marketing and makes the consumer experience more intuitive and responsive than ever before.

The Emergence of Predictive Personal Assistants

The personal assistants we have today, like Siri and Alexa, are just scratching the surface of what’s possible.

Bill Gates apparently predicts we’ll all have an AI-powered personal assistant at some point.

There are so many things in our daily lives we all need help with.

Meal planning, fitness routines, travel itineraries, productivity… it’s a very long list, and the next generation of personal assistants is poised to become more proactive and predictive.

Imagine an assistant that not only responds to your commands but also anticipates your needs.

Anticipatory AI is a lot closer than you think.

The Evolution of Ethical AI

As AI evolves, so too will the ethical frameworks around consumer rights.

We all agree there needs to be data privacy, bias prevention, and lots of transparency.

Many regulatory bodies around the world are already implementing guidelines and standards to govern the ethical use of AI.

There’s a growing emphasis on “Explainable AI” (XAI) to help make AI decisions more understandable to us.

If you’re interested, I found some pretty good stuff on the subject:

The Advent of Emotional AI

The next frontier for AI is decoding emotions.

Emotional AI, or affective computing, analyzes and responds to human emotions.

The technology is being integrated into a variety of applications:

  • Customer service bots that adjust their responses based on the customer’s mood
  • Mental health apps that provide support by recognizing signs of stress or depression
  • Monitor driver alertness and emotional states
  • With Smart homes and IoT, devices can adjust the environment based on the occupants’ moods

All of those things make interaction not just personalized but also emotionally resonant.

The convergence of AI with technologies like AR, IoT, and emotional AI shows us that predicting consumer behavior will move beyond data and algorithms to creating experiences that resonate on a deeply human level.

Sure, we still need to make smarter business decisions but it’ll be all about creating a world that feels tailor-made for each and every one of us.

The possibilities are as boundless as our imagination.

FAQs About Predicting Consumer Behavior with AI

I did a quick roundup of some of the common questions and here they are with a few  short answers.

How is AI used in consumer behavior?

The short answer is AI analyzes consumer data from various sources to identify patterns and preferences which allows for personalized marketing, product recommendations, and improved customer experiences.

The longer version is up here.

How do you predict consumer behavior?

You can use machine learning models trained on historical consumer data to predict future consumer actions such as the likelihood of purchasing something and how well they’ll respond to your marketing campaigns.

How does AI work in prediction?

The first step is using algorithms to analyze past data and identify patterns. Then those models continue to learn from new data over time to refine its predictions and how it adapts to changing trends and behavior.

How is AI used in consumer research?

It helps identify market segments, consumer sentiments, and trends which give us deeper insights into different consumer needs and behaviors.

Can consumer behavior be predicted?

Yes, you can predict consumer behavior to some extent using data analytics, machine learning, market research, and trend analysis.

All of those methods analyze past and current consumer actions to give you an idea of what may happen in the future.

But you have to remember predictions are not foolproof. You have to take into account the unpredictable human nature and all the external factors that influence consumer decisions.

Final Thoughts

The more you read about this stuff, the more exciting it gets.

There’s the good, the bad, and limitless potential.

Regardless of which side you stand on, you can’t deny the fact that AI is capable of redefining the landscape of consumer engagement.

There are a lot of challenges ahead but I think we can engage with it, question it, and shape AI so that as it evolves, it does so in a way that benefits not just businesses but society as a whole.

Because despite all the technological advancements, the essence of business remains unchanged.

The core principles of trust, quality, and customer satisfaction are as relevant today as they were when we relied on billboards.

The challenge for modern businesses is to blend the new tools with the same values that have always defined commercial success.

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