This interview is with Dennis Consorte, Data Scientist, Digital Marketing & Leadership Consultant for Startups at Consorte Marketing.

Dennis Consorte, Data Scientist, Digital Marketing & Leadership Consultant for Startups, Consorte Marketing

Can you introduce yourself and share your expertise in the fields of Data Science, Digital Marketing, and AI?

I’m Dennis Consorte, and my career has been defined by understanding what drives human decision-making and how data reveals patterns we’d otherwise miss. My first e-commerce business was acquired in 2004. It was an online DVD rental company, and the crux of the business was the recommendation engine that I coded to maximize use of our limited niche inventory across subscribers. That experience taught me the best decisions come from understanding both the numbers and the psychology behind them.

Since then, I’ve helped hundreds of businesses grow, and my work spans three interconnected areas:

Marketing strategy: I focus on the entire customer journey and how people actually make decisions, not just channel tactics. I’ve worked with startups, small-cap public companies, and enterprises. I publish regularly at Entrepreneur and Forbes on marketing psychology and AI’s impact on business.

Data analytics and decision science: My MBA training has a significant focus on business analytics, exploring how people use data and how cognitive biases influence interpretation. I’m fascinated by the gap between what data shows and what people do with it.

AI and practical business applications: My real passion right now. I’m not just interested in AI as a tool, but in human-AI collaboration. How do leaders evaluate AI recommendations? When should they trust them? When should they override them? I’m exploring these questions both academically and through client work.

What connects everything is my belief that the future of AI-driven marketing isn’t just about better algorithms. It’s about judging when to trust those algorithms, and when to go with your gut. That’s where data science, psychology, and strategy intersect.

I’m also a published author—my book “Back After Burnout” became an Amazon bestseller. While it’s about burnout recovery, it’s fundamentally about decision-making under pressure, which defines my professional aspirations.

I’m at an inflection point: moving beyond digital marketing execution toward broader questions like, “How do we make better decisions with imperfect information?”

How did your career journey lead you to specialize in these interconnected areas, and what inspired you to pursue this path?

My career evolved through necessity and curiosity. I graduated with a psychology degree in 1997 with no marketable skills. I became an office temp, taught myself Microsoft Office, and eventually got a full-time gig that included building the company website. That forced me to learn SEO and web development as an autodidact. I got tired of it, and built my first business with a co-founder in the evenings: a niche DVD rental company. He focused on logistics while I focused on coding the website and ranking #1 on Google. We sold it in 2004.

The sale became my pivot. I developed in-demand digital marketing skills and leveraged that to help others grow online. Eventually, I had a digital marketing shop and my new business grew.

My ‘useless’ psychology degree became my secret weapon. I realized marketing is fundamentally about human decision-making. Every conversion is a psychological moment.

That led me deeper into data analytics. I became obsessed with: why do people choose what they choose? I started tracking patterns, and deepening my understanding of cognitive biases.

Enter generative AI. Suddenly algorithms were making recommendations, and people were accepting them without understanding why. I saw they were outsourcing judgment.

I’ve pivoted again, to use my skills for research on ways AI influences decision-making. (Hint: it’s a self-reinforcing feedback loop)

I’ve always been fascinated by why people do what they do, and how data reveals those patterns. At this point, my career is icing on the cake.

Based on your experience, how are AI and machine learning transforming the landscape of digital marketing? Can you share a specific project where you’ve seen significant impact?

AI is fundamentally changing how marketers make decisions, but not how you might expect. The biggest transformation is the shift in decision-making authority. Platforms like Google Ads recommend actions with such confidence that many marketers simply accept without questioning the logic. They’ve given up agency and stopped understanding their own campaigns. It’s frightening!

I once worked with a health and wellness e-commerce company where Google’s Performance Max recommended tripling the ad budget with impressive projected returns. But when I cross-referenced Google’s dashboard with their financials, there was a mismatch.

At that point, I didn’t reject AI; I interrogated it. We increased budget by 40% instead of 200% and targeting segments where unit economics actually worked. Revenue and profit went up because I was the “human in the loop.”

AI is great at pattern recognition, but can’t understand context it hasn’t been trained on. I filled that gap and we had the perfect human-AI partnership.

You’ve mentioned using AI-generated content in marketing strategies. How do you balance leveraging AI tools while ensuring authenticity and avoiding detection by algorithms?

Treat AI-generated content as a starting point, not an endpoint. Your authenticity shines through when you curate what it produces and layer in your own voice and experience.

Generative AI is great for structure, and personal experience is awesome for nuance. So, use ChatGPT and other tools for outlines, headlines, or first drafts. Then, make it your own. That’s what I do, and it saves me time without sacrificing my agency in decision-making.

Keep in mind, it’s best to follow the rules. When I write for Entrepreneur or Forbes, I might ask AI to suggest angles or research data. The rest is on me, and my insights come from my 20 years with clients, frameworks I’ve developed, and stories only I can tell.

The “detection” question misses the point. Google isn’t detecting AI content; it’s detecting low-value content. So, make it valuable! Start by developing your understanding of Google E-E-A-T. Apply it to your persona as an author, and to the content you create.

In your data storytelling work, what’s a unique approach you’ve developed to make complex data insights more accessible and actionable for clients?

Many people collect data first, then try to figure out what it means. I prefer the opposite. I design the data collection around the specific question we’re trying to answer.

If a client wants to know if their marketing is “working,” many analysts build a dashboard around mounds of data and try to interpret it. I dig deeper into the psychology and my client’s “why.” First, I want to know what “working” means to them. A client who describes that as getting lots of eyeballs is very different than one who describes it as maximizing customer lifetime value. That’s my starting point.

For example, if you want to determine whether content marketing justifies the investment, you need to figure out what “justify” actually means. Marketers will throw around terms like “KPIs” and that’s great. Just make sure you’re focused on the ones that matter most. If you’re only interested in reducing clicks to conversions, then you’re better off spending time on your product pages. But if you’re interested in building a loyal base of customers and maximizing lifetime value, then you might want to focus on your content, even if that means the payoff doesn’t happen right away. This is especially true today, when SEO is transforming before our eyes into GEO (Generative Engine Optimization) where the goal isn’t rankings, but AI citations.

How do you see the role of data scientists evolving in digital marketing teams? What skills should aspiring professionals focus on to stay competitive?

Data scientists in marketing are evolving from report builders to decision architects. We don’t just analyze what happened. We anticipate what will happen and recommend what should happen next to get the outcomes we want.

Here’s the shift: technical skills alone aren’t enough. You need proficiency in translating between data and business strategy.

If you’re a data geek like me, then you probably need to work on your people skills. If you’re a great communicator with a bit of empathy, you’ll be light-years ahead of many others in this field. Learn how businesses actually make money. Understand basic microeconomic principles, and measures of success like customer acquisition cost and lifetime value. Data scientists who can connect their analysis to P&L statements, AND COMMUNICATE are indispensable.

Looking ahead, what do you believe will be the next big trend at the intersection of Data Science, Digital Marketing, and AI? How can businesses prepare for this shift?

The next big trend isn’t better algorithms… it’s better human judgment about when to trust AI, and when to rely more heavily on your own experience.

Right now, businesses are outsourcing decisions to algorithms without understanding the underlying logic. Marketing platforms recommend budgets, audiences, and bid adjustments. Generative AI spits out intelligent-sounding answers to every question. If you’re like most people, you just take what it gives you. Be confident!

AI optimizes for patterns in past data. It can’t account for strategic context, competitive dynamics, or changing market conditions unless you guide it very carefully and tell it how to interpret the data you provide. It can even amplify cognitive biases. Automation bias makes us over-trust recommendations, confirmation bias makes us seek AI outputs that validate what we already believe, and anchoring bias locks us into AI’s initial suggestions.

Businesses that prosper will develop decision frameworks. They’ll use and create systems to evaluate when to trust AI and when to override it. The competitive advantage is your judgment and systems when it comes to AI usage. So, invest in teaching teams critical thinking about algorithms, and give them the freedom to decide when to use them, and when not to.