UNFILTERED - Brandon Willington on DIY Marketing Ads & the LIES That Marketing Agencies Sell You

S6:E82

Better Data, Better Decisions with Andy Janaitis What if your advertising dashboard says you're succeeding but your bank account says otherwise?

That's not necessarily a marketing problem.

It may be an interpretation problem.

Queue up this episode of Small Business Stories with Andy Janaitis, founder of PPC Pitbulls, for a grounded look at what increasingly automated advertising requires from small businesses: better data, clearer objectives, and enough human judgment to know whether the algorithm is optimizing the right thing.

Andy began his career in data science, where he learned an enduring lesson: sophisticated models cannot rescue bad inputs. Today, he sees the same problem playing out inside Google Ads, Meta, CRMs, e-commerce platforms, and increasingly AI.

A platform can report a conversion without that conversion becoming meaningful revenue. A business can optimize for cheap clicks and attract the lowest-quality traffic. Two systems can report different versions of the same result. And a founder can spend tens of thousands of dollars before realizing the metric everyone celebrated wasn't measuring what mattered.

If people don't trust the numbers, they can't confidently act on them. If leadership misunderstands what a metric actually represents, better technology can accelerate the wrong decision. And if AI interprets an incomplete picture of the business, "mostly right" may still be wrong enough to make the right customer effectively invisible.

Loralyn Mears, PhD, aka "Dr. LL," brings you thoughtful conversations with entrepreneurs and small business leaders navigating visibility, leadership, and growth. Thank you for being here.

👤 Guest Andy Janaitis

Founder, PPC Pitbulls

Data scientist turned PPC strategist specializing in paid advertising performance

⚠️ Core Problems • Starting with an advertising tactic instead of a business objective

• Optimizing for cheap traffic rather than valuable customers

• Feeding automated systems incomplete or incorrect conversion signals

• Trusting platform dashboards without examining what the numbers actually represent

• Different systems producing conflicting versions of performance

• Attribution becoming more complicated across AI search, organic search, social, and paid channels

• DIY AI advertising removing too much human judgment from the process

🥡 Practical Takeaways • Start with the outcome: what does the business actually need the advertising to accomplish?

• Cheap clicks aren't necessarily good clicks; algorithms optimize for what you ask them to optimize.

• Validate what a "conversion" actually represents before treating it as success.

• Establish one source of truth for the business outcome that matters.

• Revenue and profit are not interchangeable measures of advertising success.

• Omnichannel attribution is complicated, but small businesses don't need perfect modeling before they begin measuring.

• Automation works best when strong data signals are paired with human oversight.

• AI-generated understanding that is mostly correct can still miss the nuance that differentiates the right customer from the wrong one.

⏱️ Timestamps 01:10 Why "we need ads" is the wrong starting point

03:12 How PPC shifted from manual targeting to automation

04:51 When to trust the algorithm and when not to

07:13 Why advertising automation lives or dies on data

09:38 Rebuilding trust after businesses have been burned by agencies

13:24 Garbage in, garbage out: Andy's data-science lesson

16:07 Amplifying weak signals with more marketing

17:24 The metrics that actually matter

20:36 What's a realistic return on ad spend?

23:33 AI search, omnichannel discovery, and attribution

26:17 Why DIY AI still needs business strategy

29:09 The 20% AI gets wrong

🔖 Who This Episode Is For Founders and small business owners who want to understand whether their advertising is producing real business value rather than simply producing attractive dashboard metrics.

At STEERus, we see False Signal Confidence as an increasingly consequential form of misinterpretation risk. A signal doesn't become trustworthy simply because it is measurable and once AI begins acting on a misunderstood signal, the error can become faster, cheaper, and easier to scale.

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#entrepreneurship #smallbusiness #podcast #advertising #ppc #Googleads