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[Part 3] Our Data-Driven Approach to Creativity: Applications
[Part
3]
Our
Data-Driven
Approach
to
Creativity:
Applications
Data Scientist
Upon initial inspection, data may seem to
set itself up as the antithesis to creative
flow — as if data is industrial and brutalist
architecture sitting a world away from the
flowing rivers and rolling hills of creativity.
This isn’t a new source of discourse; in the
90s, the poet Mary Oliver explored this
duality in her essay Of Power and Time*.
Here, she mentions that creativity requires
loyalty and links this to the idea that artists
cannot always be disorganised lunatics, but
also require efforts in self-discipline to
finish a project. Structure consistently
offers guidance to the creative process. In
our case, it sets creativity on a particular
direction to facilitate a meaningful,
applicable outcome. This is an
ever-evolving balance that we are trying to
strike in an effort to understand where and
when data and structure can facilitate the
creative process.
In this article, we will explore three practical
applications of data within the creative
process, demonstrating the ways in which
we’ve used data to learn about a particular
client or topic in order to facilitate better
design, better branding, or better online
experiences.
1. Identifying content gaps in Sustainable Digital Design
The Question: Coming up to the re-release
of our SDD platform, with a stronger focus
on content and education, we asked
ourselves: what already exists in this space
and where can we fill the gaps?
How: First, we scraped articles, blog posts,
and scientific articles using the Google RSS
architecture and used a series of boolean
queries (Think: CO2 emissions OR
sustainability AND web design OR graphic
design). Then, we used Natural Language
Processing (NLP), (specifically, text analysis)
to classify articles as having either positive
or negative sentiment, and being
education-oriented versus action-oriented.
The scores were standardised between 0
and 1, before using a k-means cluster
analysis to identify groups of content.
What we learned: The landscape is sparse,
but this gives us room to grow and create
within the the space. Specifically, there
seems to be a lot of negatively classified
content, which, upon further investigation,
is primarily academic or scientific.
Understanding this gives us room to explore
these topics, dig into the science further,
and bring the research into a digestible
format through our platform.
2. Understanding audiences: Personality traits and creative success.
The Question: It’s important to understand
our audience. In one case, we can conduct
a survey to understand their personal
attributes and affinities to certain
competitor brands or design elements. In
another case, we may be able to find an
open-source dataset that has already
collected data on our potential audience.
As an example, we uncovered an
interesting dataset** which included scores
on multiple personal attributes and levels of
success across different fields (art, science,
design, etc). From here, we could ask and
answer: What personal attributes and
personality traits are related to creative
success?
How: Once the data is collected, cleaned,
and the variables of interest are selected,
we used a lasso-regularised network model
to illustrate the correlations between each
of the variables.
What we learned: Creative success was not
associated with any of the personality traits
that were measured in this study. Instead,
creative success in one industry was highly
related to creative success in another
industry. In this way, creativity cannot be
predicted ahead of time, nor can it be
connected to any measurable personality
trait — or at least by our current standards
in personality measurement and
understanding.
3. Optimising existing platforms: content and behaviour flows through websites
The Question: Often, ‘understanding’ user
behaviour on a website is attributed to
Google Analytics metrics — bounce rates,
page views, screen time, etc. But, what if we
want a more in-depth understanding of our
users and their interests? In the case of
this particular client, we wanted to know
how users were navigating through the
content on their website, and if we could
identify patterns or groups of content that
were likely to be read by the same type of
user.
How: Using the Google Analytics
anonymised Session ID variable, we can
see what articles were read within the same
user session. From here, we can pull all of
this data from the Google Analytics API and
use an item set mining algorithm to
uncover which articles were read together
in the same session.
What we learned: We learned which articles
users were likely to navigate to after
reading something from the homepage of
the website. Using this information, we can
move forward by providing accurate
recommendations to the user on which
articles they should read next. In doing this,
we improve behaviour flow on the website,
drive traffic to niche or older articles, and
improve the user experience on the website
by showing them more articles that they are
likely to enjoy.
References
*Heather Horton, originally sourced from "Of
Power And Time" by Mary Oliver
**Zabelina, D.L., Zaonegina, E., Revelle, W., &
Condon, D. M. (2021). Creative achievement
and individual differences: Associations
across and within the domains of creativity.
"Psychology of Aesthetics, Creativity, and
the Arts". Advance online publication.
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