Due to the manual work involved, formulating hypotheses represents a bottleneck when scaling up experimentation in an organization. Even more importantly, the hypothesis-based, confirmatory approach to testing limits the outcomes of the experimentation efforts as congruence bias and confirmation bias are two common tricks that theory-driven experimentation can play with our minds.
Especially when circumstances are complex, formulating a theory and coming up with a hypothesis can lead to thinking inside a box. Examining a hypothesis on the basis of a previously established theory, makes us try to make sense of the test results within the constraints of previous knowledge and research.
The problem with both biases is that we only know they have affected our findings in hindsight or after they have happened.
There is a bounty of valuable insights when expanding the result interpretation above and beyond the obvious, the expected, promising, or desired. In contrast to confirmatory experimentation that tries to establish correlation and potentially causation, exploratory experiments are more focused on conceptualization, searching for factual correlations that may be later applied to define phenomena and discover potential causal regularities.
Exploratory experimentation is theory-informed rather than theory-driven which means that theory is still important as a framework for organizing insights, but not as restrictive as in confirmatory experimentation that may hold the testing results “hostage”.
Expect The Unexpected
Many occurrences are neglected by traditional testing methods because they do not conform to the typical ways a human mind makes sense out of data, since the human mindset is wired to follow familiar theoretical ways of doing things.
To surface correlations and insights exploratory experimentation breaks these boundaries by testing countless scenarios automatically without the cognitive bias and restrictions of preconceived hypotheses.
Due to the high complexity of consumer decision making processes, finding the optimal audience and appropriate messaging to market a certain product is the perfect ground for applying exploratory experimentation. In an ideal world one can continuously test and explore the efficacy of a value proposition on certain audience segments, contextual categories and channels while validating most efficient creative design and messaging.
Exploring such a vast number of possible combinations requires an augmented analytics solution that supports running always-on experimentation on your marketing data.
Here at Admetrics we provide the first and only unified analytics solution with integrated experimentation engine that is capable of that. Get in touch for a demo.