• LinkedIn
• Twitter
• Instagram
• YouTube
• RSS Feed

Apple •Spotify• Pocket Casts •Youtube •Overcast •RSS

What’s up everyone, today we have the pleasure of sitting down with John Saunders, VP of Product at Power Digital Marketing.

Summary: Agencies are drowning in tools, dashboards, and AI gimmicks, but John Saunders has spent years building something that actually works. nova started as an internal fix and grew into an operating system that strips away noise, delivers context with every number, and gives AI a cockpit filled with real operational data. Along the way John learned that trust comes from accuracy, speed, and transparency, and that adoption only happens when products remove steps instead of adding them. From client portals to analytics to AI, his story shows how clarity beats complexity, and why agencies that chase it finally get technology that feels like leverage instead of liability.

In this Episode…

• How an Agency Operating System Reduces Silos
• Why Context Driven Analytics Replaces Dashboards
• Chasing a Single Source of Truth in Marketing Data
• Building an AI Cockpit Before AI Copilots
• Designing Internal Data Products for Agencies
• Reducing Complexity in Martech Product Development
• How To Tell If An AI Tool Is More Than A Wrapper
• How to Build Client Portals That Clients Actually Use

About John

John Saunders is the Vice President of Product at Power Digital Marketing. He leads strategy, UX, operations, and AI for nova, the agency’s enterprise marketing technology platform that connects with more than 2,000 integrations. Since 2021, he has grown the technology team from 2 to 40 members, delivered more than 20 production-ready applications, and developed intelligence tools that improve client retention and increase lifetime value. He has also built partnerships with Google, Meta, TikTok, and Amazon that resulted in new product capabilities.

Prior to his current role, John served as Vice President of Technology. He built the first applications that became the foundation of nova and improved scalable systems, API integrations, cloud performance, and automation for the firm. He previously worked as Software Development Project Manager at Internet Marketing Inc. (now REQ), and Co-Founder of Brightside Network Media, a platform that combined technical design with storytelling to highlight culture and music.

How an Agency Operating System Reduces Silos

Agencies are drowning in tools. CRMs handle sales, project boards track tasks, invoicing software manages billing, and analytics dashboards measure performance. Each tool may solve a specific problem, but together they create a scattered system where every team works in isolation. John Saunders has seen this problem repeat across agencies, and his solution is direct. Build a single operating system that reflects how the agency actually works rather than relying on disconnected platforms that never sync.

John described nova as that system. Instead of forcing teams to reinvent contracts or pricing every time, nova uses a service library with set rates and guidelines. Automation handles the repetitive work, so teams spend less time drafting proposals and more time serving clients. nova acts as a hub for the agency’s real workflows. It connects sales, operations, and delivery into one shared environment where everyone can see the same information.

“With an agency OS, we are trying to fix this problem where there are so many tools and platforms that people work on, and that inherently creates silos. With one system focused on operations, it provides a central spot for everybody to work from, which creates efficiency and alignment.”

The need for this kind of system is obvious once you look closely at agency life. Account managers keep their own spreadsheets, sales leaders adjust pricing rules on the fly, and creative teams use tools that never connect with operations. The result is misalignment, duplicated effort, and wasted hours. An operating system forces the agency to define its rules and then codify them into the platform. That way you can cut the daily noise and create repeatable workflows that scale.

Agencies often assume the next SaaS subscription will solve their problems. The reality is that the core problems are internal. Building an operating system like nova does not replace tools, it makes them work together. It creates one place where every team operates from the same playbook. That way you can reduce inefficiency, strengthen alignment, and free people to focus on client work instead of wrestling with tool silos.

Key takeaway: An agency operating system reduces silos by centralizing contracts, pricing, and service guidelines inside one platform. Standardized rules and automation save time, while a shared hub keeps every team aligned. Instead of adding another tool to an already bloated stack, define your workflows, codify them into an operating system, and create an environment where teams work together with speed and clarity.

Back to the top ⬆️

Why Context Driven Analytics Replaces Dashboards

Dashboards impress people for about five minutes. They get pasted into a slide deck, admired in a meeting, and then forgotten. They look sleek but rarely change how teams actually work. John Saunders describes them as “dead weight,” and he is right. Most dashboards are static trophies, not decision-making tools.

John insists that analytics must carry a point of view. Agencies do their best work when they stop presenting raw numbers and start tying those numbers to judgment. nova, the product his team builds at Power Digital, bakes that opinion into everything it produces. Every measurement is run through a filter: does this reflect the right way to evaluate performance? If the answer is no, it never makes it to the client. That rule sounds simple, yet it separates meaningful analytics from the noise of charts that show data without direction.

He also points out that numbers without context fail to tell the full story. Performance depends on more than what a database records. It depends on client conversations, launch dates, migrations, and campaign decisions that live outside structured tables. nova integrates those details directly into the analytics layer. The result is data that reads like a story, not a sterile snapshot.

“Performance isn’t just the data itself. It’s everything around it.”

John sees analytics moving toward systems that feel conversational. Static dashboards freeze data in time, while teams need a living engine that blends numbers with the narrative behind them. Instead of flipping between charts and email threads, the analysis itself should surface both at once. That way analytics become a dialogue with context, not a set of disconnected metrics.

Key takeaway: Treat dashboards as disposable and focus on analytics that combine three things: a strong opinion about what matters, context from the real world, and delivery in a format that feels like a conversation. When you give your team numbers plus narrative, you give them clarity that drives decisions. Replace static charts with context driven analytics so people act faster, waste less energy, and actually understand what the data is telling them.

Back to the top ⬆️

Chasing a Single Source of Truth in Marketing Data

The term “single source of truth” has become a fixture in marketing conversations. Agencies, vendors, and consultants all chase it, yet most teams quietly admit their dashboards rarely deliver. John Saunders argues that the only way it works is when the organization itself agrees to treat the platform as the truth. Without that alignment, no amount of data engineering matters.

“Is the source of truth real for us as an organization? Is everybody aligned that this is the source of truth? That’s the real question,” John explained.

nova’s early years focused on consolidating everything into one platform. Salesforce records, client service metrics, and ad spend data all landed in the same place. On paper, this was progress. In practice, it created another problem. People did not want to sift through endless charts each morning. They wanted clarity. They wanted a quick story of what changed and what to do about it.

That realization led nova to invest in proactive signals. Instead of dashboards that require constant digging, the platform now sends direct notifications about anomalies. If traffic drops 50 percent, nova not only alerts the account manager but also explains that the client’s site was down for scheduled maintenance. By embedding context into the data, nova transforms numbers into guidance. The platform points people straight toward decisions rather than burying them in reporting.

Agencies often market transparency as a differentiator, but transparency without clarity is just noise. What John highlights is that data platforms earn their place when they combine alignment with context. Agreement on the definition of truth keeps everyone on the same page, and contextual delivery ensures people know what action to take.

Key takeaway: A single source of truth works when two things happen together. First, leadership and teams agree to recognize the platform as the truth. Second, the platform translates raw numbers into contextual explanations and delivers them directly to the right people. If you are building or buying a marketing data platform, prioritize alignment and context. That way you can reduce wasted hours on dashboards and increase the time your team spends making decisions that actually move results.

Back to the top ⬆️

Building an AI Cockpit Before AI Copilots

Vendors have flooded the market with AI copilots for marketers, yet few ask the harder question of what cockpit those copilots are supposed to fly in. Without a real operating environment, the model is left making guesses. John made the point clearly when he said,

“AI is only as good as what you put into it. Garbage in, garbage out.”

At Power Digital, the cockpit is a unified data layer that covers more than just ad performance. It tracks when a client signed, when they left, what services they used, and every operational detail in between. Billions of data points flow into this system, combining performance signals with the daily mechanics of running accounts. That full context gives the AI something meaningful to work with, which means sharper outputs from the very first run.

Most copilots on the market today run inside narrow walls. They rely on the vendor’s prebuilt model and whatever data a user has handy. They can generate responses, yet those responses rarely carry weight because they lack the operational depth that drives real decision-making. A cockpit filled with historical and operational data changes that equation entirely. It makes the co-pilot feel less like a toy and more like a tool you can trust.

The obsession with copilots exists because they demo well and look impressive. The grind of building integrated data systems takes years and rarely gets the spotlight, but it determines whether the AI adds value or becomes another distraction. Teams that invest in the cockpit position themselves to get real leverage from AI instead of surface-level gimmicks.

Key takeaway: AI copilots only perform when they sit inside a cockpit of unified, operationally rich data. Build a single data layer that blends client history, performance metrics, and day-to-day account details. Feed billions of consistent data points into that system. That way you can create an environment where AI delivers meaningful output immediately, rather than shallow results that drain time and focus.

Back to the top ⬆️

Why Data Accuracy and Transparency Build AI Trust

Accuracy determines whether marketers believe what a platform tells them. John explained that even one number out of place can send a user straight to their account rep with a list of questions. He admitted that he has done exactly this after logging into a new tool and spotting something that felt wrong. The reaction is instinctive: if one number is off, then everything else might be suspect. AI platforms only win credibility when their math holds up every single time.

Speed makes the next difference. People want fewer steps in their day, not extra hurdles disguised as features. John said it plainly: “You can’t just build products that add a step for people’s process. All they want is to cut down on the number of steps.” When a system shaves off wasted minutes instead of piling them on, the relationship changes. Clients notice this too. If a tool consistently hands them back time, it becomes part of their rhythm instead of a burden.

Transparency creates staying power. Marketers want to see exactly how a system reached its conclusion. John said the first reaction to a chart is almost always, “How does this work?” Products that dodge that question lose credibility fast. The better ones are exposing their process. Retrieval systems show the SQL query used to generate an answer. Agent frameworks narrate their reasoning step by step with logs that prove how the data was pulled and analyzed. Some platforms even let users switch the underlying model with a simple dropdown, shifting between OpenAI and Gemini when performance changes. That flexibility matters because it shows the platform is confident enough to give control back to the user.

Marketers have good reason to demand proof. Vendors spent years selling black-box systems that promised intelligence without receipts. The companies breaking through now do the opposite. They are precise with their numbers, ruthless about saving time, and willing to expose their logic. Once a product delivers those three things (accuracy, speed, and transparency) users stop second-guessing and start trusting. That trust becomes advocacy, which is the strongest signal that a platform has earned its place.

Key takeaway: Marketers trust AI systems that prove their accuracy, save them measurable time, and make their reasoning visible. Build products that show the math, cut steps out of workflows, and keep every number precise. When users stop questioning outputs and start feeling time flow back into their day, the product stops being a tool they monitor and becomes a system they rely on.

Back to the top ⬆️

Designing Internal Data Products for Agencies

Phil asked John when it actually makes sense for an agency to move from stitching together platforms to building its own product. John explained that nova began as a tool to improve efficiency inside Power Digital. Over time, it started handling complex client data and powering analysis across channels. At that point the team realized nova was more than an internal utility. It was a system with the potential to stand alone, which sparked ongoing debates about whether it should grow into a SaaS product.

John shared that the real trigger is when operations outgrow what vendor tools can handle. Teams restructure, processes evolve, and expensive platforms quickly feel rigid. Adoption declines, frustration builds, and value disappears. nova avoided that fate because it was designed to evolve alongside the agency itself. It adapts as workflows shift, and that flexibility has become its most valuable characteristic.

“We’ve bought tools before. It’s not like nova is the only tool we have as an agency. But then we’d change how a team is structured or how we run processes, and the tool could no longer adjust. nova always could.”

The larger story centers on data. Platforms now provide thinner signals than ever, and the scraps they do share rarely align. Agencies are left stitching together reports that look more like guesses than reality. nova ingests first-party client data along with those platform feeds and applies Power’s own contribution model. That way the agency controls how performance is evaluated instead of relying on incomplete views delivered by external vendors.

Plenty of measurement platforms sell similar promises, but John made it clear that owning the system creates a different outcome. nova lets Power define its own methodology, move as fast as the market, and hold a stronger position with clients. The product has become an extension of the agency’s point of view, giving Power the authority to show clients a consistent version of the truth rather than patchwork reporting.

Key takeaway: Agencies should build internal products once they consistently hit limits with vendor tools. Internal systems like nova create value when they adapt with your team structure, unify first-party data with platform feeds, and apply a measurement model that reflects your own perspective. That way you can respond to operational changes in real time, stay independent from vendor roadmaps, and provide clients with a clear, consistent view of what drives results. If your agency is bending every workflow around rigid software, it is time to consider building something that bends with you.

Back to the top ⬆️

Reducing Complexity in Martech Product Development

Analytics and attribution tools have a history of overwhelming the very people they aim to help. Phil asked John about shifting versus removing complexity in product design, especially since John once admitted to building features that looked great in planning but failed in practice. The reason was obvious: they made already complex jobs even harder.

John explained that the problem runs deep across product development. The AI boom only amplified it. Vendors rushed to release shiny new features, assuming novelty would guarantee adoption. In reality, most employees already juggle packed routines, and forcing a bulky new process into their day is often a deal-breaker.

“Adding a new thing into someone’s job is maybe the most difficult thing you can do as a product person,” John said.

nova’s team has shifted toward building with users instead of for them. Instead of dreaming up massive products, they start with the backbone and then add small, digestible improvements. They watch how agency employees actually work, then prioritize features that save time or reduce steps. A product that shaves five minutes off a repetitive task has a far better shot at adoption than a sprawling tool that demands fifteen new inputs before delivering value.

The companies that thrived in 2024 followed this pattern. John pointed to ChatGPT and Gemini as examples of how iteration beats spectacle. Their advantage came from layering practical improvements on familiar workflows rather than rebranding the same model with a different interface. Incremental progress made the tools more useful without disrupting existing routines.

Key takeaway: Build products that make small, immediate improvements in a user’s day. Sit with real employees to identify friction points. Ship features that are simple to grasp in seconds and easy to fold into established workflows. Iterate often so the product grows with the way people already work. By focusing on small releases that remove friction, you give your product a fighting chance to stick.

Back to the top ⬆️

How To Tell If An AI Tool Is More Than A Wrapper

The market is crowded with AI products that look impressive on the surface but collapse when you scratch beyond the interface. John Saunders uses a simple standard to decide if a tool is worth exploring. He looks for transparency. He wants to see how the system thinks, where it pulls context from, and what methodology shapes the outputs. Tools that cannot show their reasoning quickly expose themselves as shallow wrappers.

“If they’re not giving me visibility into how these products think, that’s step one. I need to know where the answers come from.”

John has watched hype cycles inflate and then deflate. In 2024, many vendors rushed wrappers to market and branded them as innovation. That moment has faded. He now sees stronger progress in agent architectures and tools that connect directly to a company’s own data stack. The tools worth paying attention to are the ones that can ingest your customer data and return structured outputs that carry actual business value. Everything else drifts toward noise.

Inside Power Digital, John tested these ideas with nova. Their Creative Affinity application mapped first-party customer data to specific creative assets, tracking which ads connected to which purchases. On paper, it sounded powerful. In practice, teams were paralyzed by the volume of data. Analysts sat on millions of creative assets with no direction. The answer was Playbooks. Instead of delivering raw data, Playbooks condensed strategist-level thinking into clear recommendations. They surfaced top-performing assets, highlighted patterns, and created a path forward. By building guidance into the product, the tool moved from overwhelming to actionable.

John applies the same philosophy to nova’s broader design. A knowledge graph adds an opinion layer to raw metrics, giving context that helps people understand what the numbers mean. If the platform spots a customer segment with potential for a 20 percent lift in lifetime value, it does not dictate a single action. It presents options such as drafting an email, creating a Slack update, or flagging it for a client report. Users stay in control, and the platform improves as they refine the context.

Key takeaway: The best way to judge whether an AI tool has staying power is to check for three markers: transparency, data integration, and flexibility. Transparency means the system shows how it thinks and where its reasoning comes from. Data integration means the product connects directly to your architecture and delivers outputs tied to real business value. Flexibility means the tool presents options that support your judgment rather than replacing it. If a platform meets those standards, it has potential to scale decision making in your organization. If it does not, you can safely ignore it and focus your time elsewhere.

Back to the top ⬆️

How to Build Client Portals That Clients Actually Use

Client portals often collapse into cluttered dashboards and half-baked file storage. Agencies treat them as a necessity, yet clients rarely get what they actually need. John calls dashboards “table stakes,” because every agency has one and none of them create loyalty on their own. A portal earns attention only when it gives clients leverage they can feel in their daily workflow.

Clients repeatedly ask for something straightforward. They want:

• A single place to find the files that define the relationship
• A running feed of weekly updates they can reference without digging through emails
• A team roster that shows who does what and how to reach them

“They want a combination of here’s all the most pertinent files that mean something to our relationship, plus your weekly update, plus who is on your team and how to contact them.”

The big leap comes with self service AI layered on top of those basics. John’s team built Insights AI so a client can type a natural question instead of pinging their strategist. If they want to know which discount code performed best last year, they get the answer instantly. That same system is now being expanded to crawl through call transcripts and reports. Clients will soon be able to ask what priority was highlighted in their last monthly review and see the exact section of the document that covered it.

Agencies worry about giving away too much access, yet John sees empowerment as the strongest glue. When clients can get answers on their own, they stop wasting strategist time with repetitive requests. That shift frees account teams to focus on higher judgment work, and it signals to clients that the agency values their independence rather than guarding information.

Key takeaway: Build client portals that act like real products. Centralize files, updates, and team directories, then add self service AI that answers the questions clients actually ask. That way you reduce reliance on strategist bandwidth, build trust through transparency, and create space for higher value conversations that focus on decision making rather than data retrieval.

Back to the top ⬆️

Finding Happiness in Building and Experimentation

Happiness in a demanding leadership role is rarely about chasing some mythical work-life balance. John Saunders frames it differently. For him, happiness comes from building, tinkering, and experimenting, both in his professional and personal life. He describes himself as a builder at heart, the type who thrives when there is something new to test or improve. That orientation toward innovation fuels his energy and keeps him engaged.

This mindset matters because product leaders often get trapped in the grind of delivery, roadmaps, and endless meetings. Many lose the creative spark that made them effective in the first place. John avoids that fate by actively leaning into experimentation. He ties his professional satisfaction directly to his ability to push boundaries, trial ideas, and bring fresh thinking into the systems he oversees. It is a subtle but powerful reminder that creativity is not a side project in leadership, it is the core fuel.

John’s perspective also pushes back against the industry cliché of “balance.” He does not describe happiness as a perfect equilibrium between work and personal life. Instead, he blends the two through his passions. Testing out new tools or frameworks scratches the same itch as trying a new hobby or spending time on the golf course. The experimentation mindset carries across every domain, making his career feel less like a burden and more like an extension of how he approaches life.

Of course, the personal anchors still matter. John points to his wife, his dogs, and time on the golf course as sources of happiness. These ground him and offer perspective outside of the agency walls. But those anchors are not a counterweight to the work; they are complementary. His professional and personal satisfaction share the same engine: curiosity and building.

Key takeaway: Happiness in leadership does not come from some perfect balance between work and life. It comes from staying close to the things that make you tick. For John, that means building, experimenting, and moving forward. Leaders who keep that creative spark alive are far more likely to stay fulfilled while navigating the demands of their roles.

Back to the top ⬆️

Episode Recap

Agencies run on chaos. Sales keeps numbers in one place, account managers track spreadsheets, and creative teams use tools that never connect. John Saunders saw this cycle repeat, so he built nova, an operating system that forces alignment and automates the noise so everyone works from the same playbook.

Dashboards became the next target. They look slick but rarely drive action. John believes analytics matter only when paired with judgment and context, so nova delivers numbers alongside narrative. Instead of digging through reports, teams get clarity in real time, like alerts that explain a traffic drop and tie it to site maintenance.

AI brought another wave of hype. Copilots demo well, but without a cockpit of operational data, they are useless. nova’s cockpit combines billions of client and performance records, giving AI something meaningful to work with. Trust follows when the system is accurate, fast, and transparent enough to show its math.

nova itself started small, built to fix gaps vendor tools couldn’t. Over time it evolved into a full product that reflects Power’s own measurement model. John’s lesson along the way: remove complexity, ship small improvements, and build with people instead of for them. Even client portals followed this rule, basic documents and updates first, then AI that answers real client questions.

The thread across all of John’s stories is simple. Tools alone do not create value. Clarity does. nova works because it strips away noise, integrates context, and bends with the people using it. For agencies overwhelmed by bloated stacks and shallow AI, it shows what technology looks like when it actually earns trust.

Listen to the full episode ⬇️ or Back to the top ⬆️

Follow John, nova and Power Digital 👇

• LinkedIn
• nova
• Power Digital

✌️

—
Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

Apple •Spotify• Pocket Casts •Youtube •Overcast •RSS

Related tags

🔎 Search for an episode

Search

See all episodes

Future-proofing the humans behind the tech

Apple •Pocket Casts•Google •Overcast •Spotify •Breaker •Castro •RSS

• LinkedIn
• Twitter
• Instagram
• YouTube
• RSS Feed