Marketing Decision Intelligence The Next Frontier in AI-Powered Marketing

In this session, host David Berkowitz and guest Bill Lederer discuss Marketing Decision Intelligence (MDI), a new approach that unifies marketing data and uses AI to deliver prescriptive insights and optimize decision-making.

Bill explains how MDI extends beyond traditional business intelligence by integrating disparate data sources into one cohesive system, while also addressing organizational and technical challenges.

0:05 – David:
Hello everyone and welcome back to another edition of AI Insiders by AI Marketers Guild. I'm your host, David Berkowitz. Today we have a special treat with Bill Lederer, a founder I've gotten to know over the past few months. Bill is developing exciting new technology focused on applying AI to our data challenges. For those who are new here, please join the conversation. Bill, it's great to have you. Who are you?

1:13 – Bill:
That's the most important answer—a trophy husband. I'm a longtime marketer, though not by trade; I was a quant on Wall Street before becoming an e-commerce marketer. I founded art.com in the late 1990s, where I quickly learned that most marketing efforts fail. It was crucial to control our spend to secure a decent ROI so my wife wouldn’t endure a difficult startup process. More than 25 years later, we’re still fighting for that ROI.

2:20 – Bill:
Much has changed and yet some fundamentals remain—like the need to separate facts from myths quickly. Whether you're on the buy or sell side, or providing services and software, effective decision-making remains paramount. My focus now is on unifying data to drive smarter marketing decisions.

3:26 – Bill:
I spent nearly 11 years handling outsourced managed services for many marketers, agencies, and media companies. We built and ran systems 24/7, refining our processes over several technology generations. Through that experience, I discovered significant gaps in not just accessing data faster, but in driving impactful decisions.

4:32 – Bill:
Consider this: why can’t our systems work like the human brain? We ingest data from paid, owned, and earned channels, plus sales and financial metrics, yet our current tools remain siloed. I’m exploring whether a distinct field—Marketing Decision Intelligence—can bridge that gap and streamline decision-making.

5:37 – Bill:
There are very few companies excelling at unified data integration. Many vendors overpromise and then underdeliver—take the CDP space as one example. I see Marketing Decision Intelligence as a way to cut down on time wasted fighting disparate systems and create a single source of truth for decisions.

6:39 – Bill:
Think of the human brain: data enters through our senses, gets processed in the cortex, and then guides decisions. In marketing, we rely on isolated channels and siloed analytics. I’m asking if we can build a unified system that mirrors how our brain processes information.

7:41 – David:
The way you describe it reminds me of organization challenges, where different parts don’t communicate. Are people really asking for this integration, or do they just want to focus on their own departments?

8:46 – Bill:
I believe early adopters will embrace greater transparency and accountability—even if it challenges their current routines. Those who resist risk missing a competitive edge.

9:46 – David:
Moving on, many ask: Why launch another category when business intelligence or marketing analytics already exists? Can you clarify what sets MDI apart?

10:53 – Bill:
Absolutely. Marketing Decision Intelligence is defined as the strategic and tactical application of automation and AI to unified marketing and related data for improved decision-making. It’s not just about analytics; it's about combining descriptive, predictive, and prescriptive insights to drive marketing strategies.

11:55 – Bill:
Traditional BI stops at description and prediction—while our approach delivers actionable recommendations. Many current BI solutions require extensive manual effort, lack scalability, and incur high costs.

12:58 – David:
And what about marketing analytics tools like Funnel IO? Do they cover this capability?

14:07 – Bill:
Funnel IO typically focuses on acquisition funnels or on-site conversion. We're talking about a complete lifecycle analysis—from campaign performance to customer data integration. Our system leverages hundreds of pre-built data models covering areas like incrementality, causality, and RFM analysis.

15:08 – Bill:
We’re even considering APIs that plug directly into execution platforms such as ESPs, DSPs, and CDPs. It’s like giving marketing its own brain—the first systems of their kind, with others sure to follow.

16:11 – Bill:
I reference the Wizard of Oz moment—a call for a “marketing brain.” While I might be the first to pioneer this, I expect many will join as the need for unified decision-making becomes clear.

17:13 – David:
There are questions about aligning problem definitions by discipline with customer lifecycles and growth tactics that span creative, SDR, and customer success teams.

18:16 – Bill:
MDI must be broad, supporting various use cases—from media campaign optimization to detailed benchmarking. It should allow deep customization for different business models, revenue streams, and departmental needs.

19:18 – Bill:
For example, benchmarking can compare performance across clients and time. It provides collective intelligence so that even if you haven’t tried a tactic yet, you know what the best approach might be—all while aggregating and anonymizing data for security.

20:20 – Bill:
All data interactions must be permissioned. Clients should be able to opt out at any point to protect their sensitive information, ensuring complete data control and privacy.

21:21 – David:
Adam raised a point: How granular should the preservation of data provenance be? Should users share some data in aggregate while keeping personal details private?

22:24 – Bill:
It must be extremely granular—ideally, a “customer bill of rights” that clearly outlines what data is shared and for what purposes. For instance, tagging subject lines and metadata can help derive best practices without exposing sensitive individual data.

23:28 – Bill:
I’ve seen this work in practice before. At a previous company, when enough participants were grouped, we moved from annual to weekly reporting. With MDI, users can set how frequently they receive updates via SMS, Teams, Slack, or email.

24:30 – David:
Bill, how do you get an entire organization aligned with this approach, breaking down silos and ensuring all functions communicate effectively?

25:38 – Bill:
Change must start at the top. It’s typically senior leadership—often above vice presidents—that drives such integrated change. Early adopters often have a strong financial motive: they want to save time, money, and resources.

26:36 – David:
That CFO call you mentioned—a quick payback validation—is the kind of success story that can overcome internal resistance.

27:40 – Bill:
Exactly. Our early clients have seen substantial benefits, like saving $300,000 in the first 30 days. When the CFO calls praising the payback speed, it’s hard to argue with that kind of evidence.

28:43 – Adam:
Quick question: Who’s responsible for integrating and configuring all these diverse data sources into one unified system?

29:43 – Bill:
I’ve spent nine years automating data pipelines and developing robust ETL/ELT solutions for marketing data. Our platform connects hundreds of connectors and transforms messy data seamlessly into unified models.

30:44 – Bill:
APIs change constantly—whether it’s JSON, SQL, or even screen scraping—and our built-in tools account for that. This reduces manual labor and keeps your system performant around the clock.

31:48 – Bill:
Our solution includes an enterprise-grade IP pass platform specifically built for marketing data. It not only consolidates data but also handles complex transformations and taxonomization for your data warehouse.

32:51 – Adam:
This is pretty technical. Some viewers have asked if there’s a demo available to see it all in action.

33:55 – Bill:
Definitely. We have a concise product tour on madtec.ai that gives you an overview in just a few minutes, and I’m available for detailed offline demos as well.

34:58 – David:
I want to emphasize that MDI delivers ROI in two ways: by increasing operational efficiency and by enhancing campaign effectiveness.

36:05 – Earl:
I have an observation—it might seem overengineered and overly tech-centered. Marketing also involves people, processes, and priorities. Sometimes simpler solutions to fix data pipelines are all you need.

37:07 – Bill:
I agree—it’s a combination of people, processes, platforms, and priorities. Typically, an analytics leader or someone in revenue operations—not the CMO—will be best suited to champion MDI within an organization.

38:12 – Bill:
Our company, MADTECH.AI, sits at the intersection of MarTech and AdTech, so our deep-rooted expertise in data analytics helps us deliver a truly unified solution.

39:18 – Earl:
That’s well said. It’s not just about creating another platform but about fostering a culture of data sharing and alignment across teams.

40:20 – Bill:
Exactly. While technology changes rapidly, building trust and encouraging collaboration is equally vital. Early adopters often experience efficiency gains first, and over time they see marked improvements in campaign effectiveness.

41:21 – David:
Jason asked about pricing and service requirements. How much ongoing service is needed beyond the software itself? And what mix of in-house AI versus external solutions are your clients using?

42:22 – Bill:
Let me break it down. Our annual license for MDI is $48,000. We have login fees starting at $49, a designer version for $99 per month, and pass-through cloud fees with no markup. Customizations incur a one-time setup fee billed at $100 per hour. Our clients rarely need heavy services unless they require new data models or connectors.

43:22 – Bill:
As for in-house AI, most clients currently use it for basic creative tasks—simple prompts or generating ideas. They turn to us for the heavy lifting in analytics and advanced modeling.

44:25 – David:
Jim, can you share your take? It sounds like you need a solution to fix your messy pipeline without all the extra bells and whistles.

45:30 – Jim:
There are two issues. First, data is always messy and wrangling it is a huge challenge. Second, while creating a new category is interesting, what I really need is a straightforward fix for my pipeline. If you’ve cracked that, I’m all in.

46:35 – Bill:
Our early customers are already replacing spreadsheets and tools like Looker with our platform. We provide descriptive, predictive, and prescriptive insights in a visually accessible format—including branded PowerPoint exports.

47:40 – David:
Does your system also train users to ask better questions, or is that something you handle on the back end?

48:44 – Bill:
At this point, we are training our chatbot, Maddie, with thousands of questions to ensure users get valuable answers from their data. Teaching customers to ask better questions is a future goal as our system evolves.

49:48 – Bill:
I’m deeply committed to this vision—just as I once focused on a niche in art, I now believe MDI will scale to meet broader needs. Scaling from a small focused idea to a comprehensive solution is challenging, but it’s necessary.

50:55 – David:
Your passion and innovation really resonate. Your early experiences in e-commerce show that big ideas often start with a niche and then expand.

51:55 – Bill:
Thank you. For anyone interested, you can reach me at bill.lmanc.ai or find me on LinkedIn. I’m available for demos, further conversations, and potential partnerships. I must also thank my advisory board member, Ken Evans, for his longstanding support.

53:00 – Bill:
A quick note—our company is primarily based in India, with 95% of our headcount there. I want to acknowledge Ashweeni Tamaya, our country manager, whose leadership and dedication make all this possible.

54:00 – David:
Thank you, Bill, and thanks to everyone for your thoughtful questions. We have an exciting schedule ahead, discussing ethics, bias, and AI-powered websites in upcoming sessions.

55:03 – David:
That wraps up today’s session. Goodbye everyone, and see you next week in our Slack and future events.