Want To Use AI? Start By Fixing Your Data

Cofounder and CEO, overseeing Zappi's global business, product and growth strategies.

When new technology comes along, we often begin by using it in the same way we've used older technology. Radio advertising began by having actors stand in front of a microphone to read copy off of print ads. When TV became the norm, advertisers added simple, static visuals to their radio ad copy.

In the ever-evolving landscape of technology, AI is the latest frontier, promising untapped potential for businesses across industries. But as with any new technology, there's skepticism about how it will help people do their jobs. According to a recent eMarketer survey, while 55% of marketers use AI for content, only 46% believe it enhances creativity.

Let's look at AI through the lens of radio. How have you interacted with AI in the past? Likely, it's via chatbots and simple solutions that process questions or prompts and provide straightforward, linear responses—this is what we're accustomed to with the technology.

Now, generative AI is challenging our expectations and perceptions, revealing its immense potential for creativity. And creativity isn't linear; just like the creativity in a brainstorming session, ideas ebb and flow before we find the connection from one influence to the next.

When it comes to AI, we won't truly see its creativity flourish until we give it the right context. Success with AI won't hinge on its implementation, but the context it's provided to create the right ideas for a marketer. That's why, before brands consider leveraging AI for creativity, they first need to get their data in order.

Finding The Right Data

The engineering cliche "garbage in, garbage out" is pertinent when we look at AI. If we feed it data that only tells half the story, then it will generate text or images to match.

Most marketing data is programmatic, designed to tell a clear story about what advertisements and products are performing well and which are falling flat. This data can help inform strategy, but ultimately can't inform creativity. Yet, it's the most abundant source of information available to marketing teams.

The data that informs creativity is the attitudinal market research data from concept, product and brand evaluation that analyzes the emotions, preferences and considerations that explain why consumers make decisions. This is the data that AI thrives on; it creates clear lines and trends through data that help identify new developments and inspire creativity.

The problem is that market research data is historically done on a project basis, lacking connection to broader business goals and objectives. Creatives test ideas, use consumer feedback to consider changes to an idea, then that data is forgotten—relegated to email inboxes and hard drives.

What if we were to take this data and use it more effectively as a source of creative inspiration?

It would provide a lifetime of learning for creative AI, becoming an invaluable resource for marketers to connect the qualities and traits of high-impact ideas to their current business objectives.

Before we can feed data to AI, it has to be connected to our broader business goals.

The Framework For Connected Insights

The key to successful marketing is understanding the ins and outs of consumer—and human—behavior; it's a combination of psychology, sociology and observation rolled into one. When it comes to the level of insight maturity needed to create this human understanding, most brands are falling short.

My company, Zappi, created the Connected Insights Framework, a model that taps into years of working with leading brands to help teams reach the point of maturity when teams are weaving consumer data through every decision. There are three essential stages: disconnected, fragmented and connected—each classified by how brands connect with their consumers and inform decisions with data.

As you read, think about which level best reflects how your team uses data.

Stage 1: Disconnected

This is the stage where the vast majority of businesses are stuck—they just might not know it. Here, data is disconnected because it is gathered from one project to the next, without any continuity across projects and teams. Because of this, there's very limited ability for teams to learn from each other across projects, so while it's easy to get answers fast, it's very difficult to zoom out and identify trends.

Stage 2: Fragmented

This is the level at which many insights teams operate. They are beginning to connect data across projects—and even brands—but these connections are ad hoc, rather than systematic. Bringing these fragmented datasets into AI could accelerate innovation in some facets of the business, but the collective data asset lacks context. It can create a disparity from one brand to the next, where advanced datasets work for some, but can't yet be applied to others.

Stage 3: Connected

This is the level at which only a small fraction of teams are operating with a systematic, uniform and consistent approach to consumer insight data at their disposal. At this point, data is democratized, connected and accessible to any team at any moment. From local teams operating in a specific region with a specific brand to teams across the country, all are compared against consistently shared metrics for success. As a result, there's a clear picture of what ideas resonate and why, creating a dynamic data asset that fuels creativity at every step.

Developments in generative AI are moving faster than ever before, and the technology will soon be foundational to every marketing team globally. Until then, the adoption of this technology is still at an early stage where actors are in the studio reading their print ad copy aloud. The first brands to adequately harness the technology will have a massive competitive advantage.

Until then, we need to get our data right.

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