Story Time: Creating a Brand Narrative
Probably one of the more significant projects that we, the XBrain team, collaborated on would be our brand narrative and tagline, “we lead big fish to bluer oceans”. Our brand narrative’s central value is one of inequality mitigation, of breaking down barriers and democratizing access to technology that could change how we think about the world. We visualize our potential customers as big fish in small tanks — untapped sources of potential trapped by restrictive environments. Our role in this narrative is the passage that leads such big fish to bigger, bluer waters, where they would be free to roam the great unknown, not just with data analysis but the greater things they could do with such insights.
Our aspirations reach far beyond acting as mere passageways to greatness, however. The estuary itself is one of the most opulent ecosystems around the world, acting home to a diverse array of wildlife that must learn to adapt to the constant change around them — just as our customers have done, generating creative solutions in a highly volatile industry. Our ultimate aim, then, is to create an environment in which different ideas from various industries could come together and thrive. As such, the estuary perfectly encapsulates the kind of sprawling, lush creativity we want to inspire in the data world.
We should probably explain how we ended up with this conceit. As a neophyte startup, we believe that a collective faith in the mission we’ve embarked on is a must-have for our team. Defining such a mission, however, wasn’t a simple path. The team had been struggling with renaming the product, coming up with names that each highlighted a unique aspect of our machine learning tool, but never one that captured its entirety. Ideas included Prune (because the product would make the path to machine learning simpler, like pruning shears), Cir-Kit (because the product would be like a data science toolkit), and Auton (because, like, AutoML).
This is where our designer KH stepped in. Joining the team as its first visual designer, he had shouldered the burden of not only crystallizing the aesthetics of our product, but creating a collective vision for it. He rose to the occasion, leading the team through a series of branding sessions that included extensive interviews with the co-founders, digging deep to find that thread of identity that could blend the different projects that the team had worked on into a cohesive idea.
It became somewhat clearer once we figured out that the branding had to be centered around the “why” of the company rather than the “what”. We had had so much trouble agreeing on a name for our product because we’d been focusing on showcasing the specific parts of the product rather than the whole of its vision, like the proverbial blind men and the elephant.
Before we could come up with a name for the our machine learning tool, we’d have to figure out what values it stemmed from. To do this, KH created a “word cloud” that clustered together the words mentioned most frequently during his interviews. The patterns showed that a significant part of the company’s vision was focused on mitigating a certain inequality of access to technology. And some of our team members had pursued such causes even before they had joined XBrain — AC, our product manager, brought up intensely fundamental questions of how disparate levels of privilege affected people with great potential, something he had thought about in great detail throughout his own secondary and higher education. In a similar vein, SZ, our machine learning engineer, wanted to cultivate her knowledge of computer science to give back to the underprivileged in society.
This inequality was also something that was widely felt among the newly emerging startups of the Korean venture ecosystem, which we had set as our target customers. Machine learning had caught on the heat that it did partly because it had such accessible potential — most industries have some sort of accumulated data that could be mined for potential insights into how business could be managed better. Contrary to its widespread potential value, however, machine learning requires a great deal of very expensive, hard-to-find personnel and specialized infrastructure construction, which makes applying this technology to actual general operations difficult for companies that don’t have such resources at their disposal.
Once we agreed on the values we wanted to pursue, translating them into the aforementioned metaphors was an easy step. The next step was organizing the ideas that had come up into a single document, less than 300 words, that we could look to when in need of a centralized message.
All that remained was deciding on an actual name for the product, which seemed easy enough at the time. Turns out we were very wrong, but that’s another blog post in itself, which you can read here.
