How Data Driven Cultures Tackle branding
A data driven culture isn’t necessarily devoid of creativity or imagination. Just the opposite.
They’ll have to be especially patient around brand formation.
Brands
A brand exists in the mind of a person. It usually costs a lot of money for a brand to be impressed upon the cortex of a person. There are certain economies of scale that kick at scale, but still at a considerable cost. If that feels fuzzy, despair not. The framework below is fantastic:
Brand and CAC
The optimization objective of a startup is valuation. To maximize valuation, Customer Acquisition Cost has to be minimized. As previously explored, nature doesn’t cooperate to keep CAC low.
The point of the brand is to reduce CAC (at worst) and to generate a defendable moat once (if) the startup becomes a business. A brand done right, managed right, can generate returns for generations.
A data driven culture continuously improves and iterates.
Some parts of the brand key are more iterable than others.
A Market Hypothesis
Sometimes the founder gets it right the first time. They nail the product-market-solution fit from inception and they ride the market penetration curve with few bumps.
More often than not, the founder doesn’t.
It’s rare that a founder starts with a brand key. It’s typically an artifact that is developed over time, or when a VP Marketing is brought on board and they see that the messaging is a mess.
What the founder typically starts with is a paragraph:
“Hello, I’m xxx from xyz. We’re a <key value proposition> for <target>. Unlike <competitor>, we <discriminator>. <Reason to believe>. We’re <company name>.”
A founder, in the early months, may struggle a bit with the wording of <key value proposition>, the exact size and nature of <target> (along with estimating TAM), and will iterate a few times framing and reframing <competitor> – <discriminator> pairs. <Reason to believe> is also easy to iterate on because the founder can see whether or not anybody is believing it.
At the most cynical, the paragraph is just rhetoric. At the most optimistic, it springs organically from the <root strengths> of a firm.
At the core of LEAN, functional benefits emerge from continuous testing of assumptions about the market. Emotional benefits may, or may not, be a strategic priority. Value creation with the intent of some value capture is the core activity of the startup. These activities are iterable, but are an order of magnitude slower than retyping an elevator pitch paragraph.
Does the brand have to be true to work?
Probably. Maybe?
I want to believe is that the closer a brand is to truth, the greater its face validity, the more likely the brand will be to cause CAC to decline. I want to believe in a power-law curve where nature is ruthless is punishing imposters. I don’t think that’s quite true.
But that doesn’t mean the brand has to be 100% true for it to effectively reduce CAC.
Can you imagine a technically inferior brand that won? Pretty easy to do, right? (I won’t slag’em).
Just as a startup is a hunt for truth, forming a brand key is too. It’s very rare for firms to form a 100% seal between reality and perception. Often good enough is good enough.
A data driven culture will seek to create a good enough seal.
Emergent Properties of a Brand
Data driven cultures rooted in extreme customer centricity will tackle target segment and customer insight the hardest and fastest. Nailing the assumptions target and insight are key. What-iffing on functional benefits and testing them against target and insight can be iterated upon rapidly.
In a statement that may enrage technologists, a large part of the absolute riskiest activities can be done with very few lines (if any) of code written. These can be framed, dog, and ponied with target segments. The line between advocacy and inquiry is thin and incredibly dangerous. However, that particular data collection is important. These are qualitative experiences. Qualitative data is still data. It just shouldn’t be confused with quantitative data. (Quantitative verification of the customer insight occurs as the market penetration curve responds at an appropriate CAC. If it doesn’t respond, the target-insight fit isn’t tight).
Data driven cultures rooted in the ego complex of the founder will likely gravitate towards examining <root strengths> and <values, beliefs and personality>. These are far less iterable. Your stance informs the tools you’ll use, and the tools you use form your experiences (Roger Martin, 2007, The Opposable Mind). Stances are far less iterable than an elevator pitch. They evolve over time. However, the culture is iterable with hiring choices and strategic policy choices.
There’s a saying that no culture survives inundation exceeding 51%. A founder, moving from headcount 0 to headcount 48, is themselves diluted. The original team of 8 get to watch a startup double to 16, to 32, and 64…each time the core values, beliefs, and personalities shift. An interesting aspect is that sometimes the growth is so large that the <values, beliefs and personality> may have shifted faster than what the founder realizes. The good news, especially for Montgomery Burns types, is that <values, beliefs and personality> may be far more rhetorically manipulable.
Think of all the seriously evil (lack of empathy with the societies in which they operate, including willful destruction of society, deliberate regulatory capture, deliberate externality generation as a matter of privatizing gains made, slavery as a business objective) firms. The very fact you can name them means that their brand key isn’t good. The set of firms that engage in one or more of those activities is difficult to size. Thanks to branding. The upper left corner of the brand key doesn’t have to be completely true. It just has to be true enough to work.
That leaves <root strengths> as something far more difficult to tackle.
Root Strengths: Pop Business Lit Myth or Management Science Fact?
A lot of the narratives I’ve read about successful entrepreneurs are extraordinarily clean. I rarely get to read about wrong turns, rejected hypotheses, and how they learn. I’m suspicious about the case study literature because I can’t tell if I’m reading a piece of marketing mythology, or if I’m reading an objective recollection.
Tony Hsieh founded a firm with a root strength in customer service. That story agrees with my experience with the brand. He’s been consistent about that root strength in every interview I’ve read. Seems true. Impossible for me to tell if that was a day 0 decision. I can’t tell. That doesn’t mean it passes into being true. It just means I don’t have a verdict. It just has to work good enough.
Root strength, once in place, would be very difficult to iterate upon. And they’d have to be true.
It is very unlikely that a data driven culture would take root in an environment rooted on a lie.
Prospective CMO: “What would you say the root strength of your firm is?”
CEO: “I’d say that it’s our engineering.”
Prospective CMO: (surprised) “Why?”
CEO: “Well, we lead the market. We’re number one!”
Prospective CMO: “You’re behind X. And Y. And Micro…”
CEO: “Well no, we’re not behind them.”
Prospective CMO: “Your behind in market share, revenue, patents filed, number of customers, customer satisfaction…”
CEO: “We’re number one in trying.”
If the energy of a culture is in truth-seeking for a purpose, how could it perform if it was continuously misaligned with what the CEO wanted to think of themselves. The degree of alignment and agreement between what the root strength of a startup truly is, and what the firm thinks it is, is extremely important. Founding a brand key on a delusion ends poorly.
Optimistically, every startup has a root strength. Sometimes it’s in the pure hustle. Sometimes it’s in a radical form of integrity. Sometimes, even, it’s the stance that it’s even worth enslaving a generation of children to pick cocoa beans just to realize an extra penny in EPS, that pure ruthlessness, could be a root strength. The degree to which the root strength is realized by a data driven culture is a core risk, and should be evaluated rapidly.
The root strength is the least iterable of all. It might as well be a discovered archeological artifact to the data driven culture. Know it. Accept it. Build on it.
Once Nailed, Scale
If target, TAM, and the customer insight are all nailed, the discriminator and the essence are working, do not change it out of boredom or vanity. It’s hard enough to train customers to repeat a story on the firms story. Modifying it too far after hypergrowth has begun is ill advised. There are typically new benefits to offer, and the emotional impact of the brand may be built during this period (See: Google, which always features a large number of product in every spot, and always taps into some emotion). Modifications to the competitive set do evolve, and, tactical testing of reasons to believe and discriminators are advised.
If penetration sputters, and there is not an macroeconomic explanation, the problems go beyond the brand-key.
Managing the Agency
Creativity doesn’t appreciate criticism. The bad news is that the data driven culture generates a lot of criticism.
The manner in which data and judgement is expressed to the agency is paramount. Too much and you crush their creative spirit. Too little and they won’t learn.
Agencies invited to pitch should be given the brand key. Take special note if the brand key is rejected without countering evidence from the agency. Be wary if the agency is rejecting assertions about the insight, target, or benefits without any data to support their statements. Many agencies will attempt to put together a parallel data driven culture if you specifically ask for one. A handful of creative agencies will not.
The goal should be to maximize the number of hypotheses about creative treatments while minimizing the common urge to destroy the brand key and remake it in another image.
Conclusion
The top three ideas are:
- Use the brand key;
- Iterate early and often, tweak only as necessary once scale phase starts;
- Executive must be aligned on root strengths, get the best from your agency.
Other posts in the Data Driven Culture series: