Ian Baer on Emotional AI: Redefining Predictive Marketing with Sooth’s 93% Advantage

It’s really all of data-driven marketing that needs a reset right now. At the dawn of modern advertising, it wasn’t unusual for brands to see conversion rates of 20% or more — and repurchase rates near 50%. Today, when a brand achieves a 1–2% conversion rate, it’s cause for celebration. Retention is harder, too — meaning brands are often forced to re-win the same customers over and over again.

 

The reason is simple: nearly every brand is competing to optimize the same 7% of buyer data — demographics and past transactions. These signals have been the easiest to track for the last 100 years. They were stored in shoeboxes before computers existed, and despite all the advances in technology and data modeling, most strategic decisions are still based on those same commoditized signals.

 

But people leave clear behavioral trails that reveal their emotional priorities — and the practical and situational filters that shape real brand choices. That’s why we’ve shifted focus to analyze the remaining 93% — the part that predicts 13 out of every 14 decisions consumers actually make.

 

Marketing mix modeling and attribution analysis are just the latest attempts at a data-driven holy grail for brands. Ninety-seven percent of the Fortune 500 invest heavily each year in data intelligence to establish a unified set of truths from which all functions, including marketing, can operate. Simultaneously, due to poor targeting, ad fraud, and various inefficiencies, brands are wasting between 20% and 50% of their media budgets, putting the credibility of marketing itself at risk. That’s why Meta talks about making brand marketing obsolete with its AI ad model, and people are leaning in.

This situation has turned marketing into a competition focused on doing things cheaper and faster, because most have given up on improving effectiveness. Without better data governance and visibility, CMOs find themselves in a precarious position with their leadership and shareholders, which is a significant reason why one-third of Fortune 500 companies no longer employ a chief marketing officer. For business-to-business brands, it’s more than half. Marketing has become so enamored with vanity metrics that are divorced from business outcomes – such as page views and brand awareness – that it no longer speaks the same language as those with true spending power in most organizations. I recently heard from one of our clients in a business strategy role, “Marketing gets all this money but I truly have no idea where it goes.” As long as marketing budgets are seen as a black hole rather than a predictable investment, marketing teams will continue to lose their seats at the big table. When we clarify the 93% of buyer signals that truly drive behavior, we not only reduce waste — we re-establish marketing as a source of measurable profit.

 

One consistent truth I’ve seen throughout my career is that when budgets tighten, brands often cut or eliminate their investment in research and measurement. Without clarity on what will work or why, brands tend to produce more assets and invest more in media. That reaction is understandable, especially when research, testing, and programmatic media are often seen as more costly than effective.

 

To meet this need, we developed a creative scoring engine that can ingest creative content in any format and deliver predictive intelligence across various audiences within one or two days. With predictive intelligence based on data-verified needs analysis, we can improve performance through emotional connection. We are enabling understanding as a central part of every creative and investment decision, without losing time or momentum.

 

 

One of the most overlooked metrics in modern marketing is share of requirement — the portion of a customer’s total category spend that a brand earns. While brands do a very good job of measuring and optimizing the business a customer already does with them, they have almost no visibility into the share of category spend each customer allocates to competing brands. What looks like a low-value customer to one bank may, in fact, be someone whose primary financial relationship is simply elsewhere, but they have no category visibility. Combine that with outdated demographic clustering and internal silos, and many brands end up flying blind — relying on the law of averages instead of verified emotional insights. The significant evolution in our model is category-wide visibility, so we can track shifts in real-time and recommend strategies to address the opportunities as well as the threats to their customer base. We have enhanced or replaced traditional research-based brand tracking with a much more dynamic and opportunistic model designed to steadily increase and protect each customer’s mind and wallet share.

 

A core part of our model has always been to keep insights both accessible and instantly actionable. Our model does not rely on demographic assumptions. Still, we output precise target profiles for media buying that incorporate both behavioral and demographic signals, fully compatible with existing targeting, delivery, and dynamic optimization platforms. This enables creative to be tailored in real time, around the emotional and situational drivers we know lead to profitable outcomes. It’s just one way our use of AI in insights and strategy dovetails seamlessly with emerging AI-driven media targeting and delivery. It’s truly hand-in-glove.

 

 

We knew we weren’t choosing the easy path when we told the marketing world it’s been focusing on the wrong inputs for decades. Another headwind we face is that when it comes to AI in marketing, the term has become shorthand for generative tools and workflow automation—solutions designed to make marketing faster, not necessarily better. Failing cheaper is still failing, and when a customer leaves because another brand met them more effectively in the moment, almost none return. This has put marketing on the clock across industries worldwide. That’s why we spend so much time with marketing leaders—not just sharing tools, but reshaping how they think about AI in marketing through a completely different lens—one that focuses on shifting 13 out of every 14 buying decisions, rather than the one that marketing has been obsessed with for a century. Where our approach flies in the face of traditional marketing and its outdated concept of “best practices,” our message is finally taking root. While the hardest work is ahead of us, I couldn’t be more excited about the challenges and opportunities that lie ahead.