A Thesis From a Pseudo-Economist: Start and End Your Stories With Questions

How do you tell your stories?

The best agencies work with their entire brains. The right side tells stories creatively. The left side, which I represent, tells stories with data and math. We both share a common goal: Tell the best stories.

My experience in academic research, along with my experience at The Richards Group, has taught me the importance of focusing on asking the right questions to tell better stories. Being creative with data and math has its limitations. We left-brainers must put value to those tools by asking questions that start our narratives and finish our stories. While this may sound obvious, it is challenging in an environment where the analytical capabilities of the new technologies overshadow the efforts of left-brainers.

We sometimes encounter products or concepts promoted as “AI-driven” or “powered by machine-learning algorithms.” These phrases certainly sound impressive, and they imply a level of numbers-driven credibility. But how often do we ask ourselves, “Why? Why does this product use AI? Why does this product need it?”

Yes, we can easily implement artificial intelligence, and you don’t have to be an expert to do so. AI provides immense value to academia; it is also important in developing business capabilities. But how – and by how much – does it really make businesses more competitive?

Amara’s Law points out the effect of not asking those questions: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

In the end, these technologies extend our capacities (which are important in the long term) but don’t necessarily improve our capabilities. Recent developments in these technologies are based on a view of the world’s events as if they happened by chance. One benefit is that we do not necessarily need to “math out” our thoughts, unlike more traditional ways – instead, we let the data speak for itself. Even with the machine-learning literature (a workhorse behind these technologies), we don’t come up with insights or predict outcomes that do not occur within the data.

Sorry, no magic here. We simply became tech-savvy with better computers, not necessarily smarter.

Then what makes us smarter? We must learn to identify the strengths of different mathematical methods, acknowledge their weaknesses, and improve how we logically and quantitatively tell our stories. We should not dwell on the type of math we use.

No quantitative method is perfect. Before we shop for new technologies to use, our work must focus on delivering realistic solutions that provide tangible value to the brands we serve. Being tech-savvy and being smart are not the same thing.

How do we find the actionable value from the technologies? How do we not fall for Amara’s Law, and instead provide valuable insights while adapting to promising technologies? How can we be both tech-savvy and smart?

Take it from a pseudo-economist: Step back. Start our stories with questions. End them with questions.

Here’s one to ponder: How do we take a technological function and make it into something more than math, something with real business value?

For example, how can we leverage user-generated content and image-recognition technology? Surely the technology on its own does not provide value to a brand. We need to ask, “What can we learn from the images? What attributes are valuable to the brand?”

Perhaps we use the technology to understand the perceptions and sentiments embedded in those images. We can quantify qualitative brand attributes and compare them against those of competitors. We can provide insights to better position the brands we serve via cycles of creative, media, and analytics – an agency as a fully functioning brain.

Analytics is about telling a story with quantifiable information. Data never tells us a full story. Algorithms only enhance our narratives. Questions always start and complete our stories.

I have a question for you:

What questions do you have for us?

Categories: Analytics, Industry

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