Innovative Writing Techniques: Harnessing Science for Creativity
Written on
Chapter 1: The Data Dilemma in Writing
In the realm of writing, one recurring frustration for many, including myself as a data analyst, is the lack of sufficient data. Writing serves as an expressive outlet, a way to organize thoughts, and, for some, a source of income. Ideally, I'd want disaggregated data showcasing individual readers' top five interests along with their engagement time on each article—information that raises ethical questions regarding user privacy.
Currently, the statistics provided by platforms like Medium offer a broad overview but fall short in allowing us to fine-tune our content. This is akin to having a competent manager who provides precise feedback versus a vague one who leaves you feeling stagnant in your career.
For instance, Medium requires around 500 views to begin revealing insights into readers' interests. Unfortunately, what you receive are merely six topics with corresponding percentages.
An example of reader interest data from one of my articles shows limited utility since categories like data science and machine learning overlap significantly. While each has its nuances, they are interconnected. It’s likely that if you write about data science, readers will also be interested in related fields like machine learning or artificial intelligence.
Moreover, examining reader interests over a week reveals that terms like 'data science', 'machine learning', 'artificial intelligence', 'programming', and 'technology' are correlated. Although Medium offers high-level statistics about views, traffic sources, and earnings, aspiring data analysts seeking to profit from their writing often find this data insufficient.
But, there's a silver lining: even minimal data can offer insights. Rather than abandoning Medium, I’ve adopted scientific and statistical methods to enhance my writing. Let me share my approach.
Making tape for junk journals - DIY - traditional & new techniques & unexpected experiments! - YouTube
Chapter 2: Embracing Experimental Techniques
In scientific research, this approach is known as hypothesis testing, which involves establishing a null hypothesis, a control group, and a controlled environment. However, who has the time for that? Instead, I like to think of it as falsification—the art of trying to disprove your own assumptions.
The process involves selecting a topic, writing style, or publication, and evaluating whether your approach resonates with readers. For example, I’ve analyzed my reading stats on Web3, blockchain, and related technologies.
Despite some initial success, the data indicates that my performance in this area isn’t stellar compared to other subjects. Notably, combining 'metaverse' with 'value' tends to attract more views than other titles. However, due to the limited sample size, definitive conclusions are hard to draw.
So what topics yield better results? Unsurprisingly, articles about data consistently garner the most attention. If you're interested in testing your content, consider these tips:
- Choose a topic, writing style, or publication and produce multiple articles.
- Allow some time—around a month—before analyzing the results to inform your next steps.
The astute among us will recognize that interpreting small data sets can lead to flawed conclusions (e.g., small number bias). While delving into such statistical nuances is beyond this article's scope, I can offer strategies for dealing with limited data.
Section 2.1: Targeting Broad Topics
Stick to broader themes and compare your views with others covering similar subjects. If several articles on the same topic attract significantly more views over time, it may indicate that your writing style isn’t resonating.
Additionally, Medium's algorithm tends to favor articles that gain traction, meaning those that do well continue to perform well. If your articles haven’t gained momentum, perhaps your style isn’t hitting the mark.
Using Tags as Inspiration
My next strategy involves targeting a niche within a broader category. I delve into general topics like 'money' and explore related tags and their readership.
For instance, within the 'money' category, readers often explore 'money management' or 'money mindset'. I leverage these tags to craft targeted content, yet I strategically avoid tagging 'money management' itself.
Why? Because only five tags are allowed, making each one valuable. The likelihood that all followers of a niche topic will engage with my article is uncertain, especially since I can’t gauge the activity levels of those followers.
Let’s hypothesize that 1% of 'money' followers might be interested in 'money management'. This could translate to around 8,730 potential readers—far exceeding the 364 followers of 'money management'.
The takeaway? Focus on broader tags, but feel free to use niche tags as placeholders when necessary.
Your next junk journal: From chaos to creative organization, decorative bits & first ideas! - YouTube
Conclusion: The Path Forward
Can I guarantee these methods will be effective? Not definitively! They are experimental, yet grounded in scientific principles. The first method aligns with the scientific method, which has been validated over centuries, while the second is based on Fermi Problem Solving, a concept well-established in physics and engineering.
Both strategies are rooted in science, giving me confidence that results will emerge over time. Ultimately, the key message is to keep exploring diverse problem-solving methods to enhance your writing consistently and creatively.