Content Marketing vs Storytelling
Content is king. It can also be the Joker. Good content may generate likes, but unless the content we are using is building trust, credibility, and engagement, we are focusing on vanity statistics…
- Published in marketing, Psychology
Virtue Signalling
Virtue signalling, a term coined in 2015 has been defined as an attempt to show other people you are a good person by expressing opinions that are acceptable to them, especially on social media.
- Published in Psychology
Space, Silence and Creativity
Music is built on the harmonies of notes. A statement which, while correct at one level is only half correct because music is also built on the spaces and rests between notes. It is the balance of both silence and harmonies that create music.
- Published in Psychology, Wellbeing
Nudge Theory
Choosing an apple or banana when paying for petrol because the fruit is where we pay or snacks in a vending machine replaced by healthy choices are examples where customers are nudged in their decision-making process.
- Published in Data, marketing, Psychology
Rule of 7
The rule of 7 states, it takes 7 interactions with your brand before a person will engage with it and become a client or customer.
- Published in marketing, Psychology
Daring to be different
Fear creates uncertainty. Uncertainty creates a sense of dis-equilibrium within us that we try to correct, so we can feel balanced and in equilibrium again.
- Published in marketing, Psychology
Clickbait and the damage done
Clickbait is the tactic of teasing users with intriguing ads or posts of your content to entice them to click-through and read.
- Published in marketing, Psychology
Communication in an age of misinformation
We need to check our biases and assumptions and know our market, so our messaging has the best chance of cutting through.
- Published in Psychology, Workplace
Starting with intent
There is a sense of satisfaction that comes at the conclusion of our shopping escapes, and it’s not always just the shiny new purchase that affirms our behaviour.
When we set out on a journey to make a purchase, it’s not necessarily because we want that particular product or service, it’s because we have the intent to find a solution to a perceived pre-identified problem.
So where does our intent come from?
- Published in marketing, Psychology
Decision-making traps using data
KEY TAKEOUTS
DECISION-MAKING TRAPS USING DATA
The nature of descriptive analytics exposes an array of decision-making pitfalls for decision-makers. By developing an awareness around common cognitive traps, we can use our understanding of this process to make more balanced and meaningful decisions.
In this article, we reflect on the individual traps in the data decision-making process, learn how to frame a problem so that we can use insights to make better decisions, and understand these golden rules around decision-making pitfalls:
- The way we make decisions is far from the rational model
- Decision biases affect every step of the decision-making process
- Analytics can offer multiple tools to overcome biases and decision traps
Decision trap 1: Availability bias
Availability (bias) is a heuristic; whereby people make judgments about the likelihood of an event based on how easily an example, instance, or case comes to mind. We all experience availability bias in everyday dialogue, but especially in marketing when we hear; “but, you know it’s much harder to get data about that”, and this is often true. This emphases that it must be taken into consideration that there is an awareness and acknowledgement that the data being used may not necessarily answer your question.
It depends on how important the question that you’re trying to answer is; if it’s a high-stakes decision, you should be creative about collecting various sources of data to avoid this trap.
Decision trap 2: Short-term Emotions
Every decision we make is influenced by our emotions, and when high pressure situations are present, it’s very easy for our decision-making judgment to get clouded by these emotions, especially in group environments.
This obvious trap simply emphasises the importance of taking a moment to assess the external view. By allowing some distance from short-term pressures and emotions, we can assess the data without having our judgement being clouded and distracted. These windows create an opportunity for emotional intelligence (EQ) to flourish, where awareness and mindfulness will act as a key deterrent to this trap.
There is room for both emotions and data, particularly in creative environments. However, it’s often the case that the former can outweigh the latter when the pressure is very high, and that’s precisely what we need to avoid.
Decision trap 3: Confirmation Bias
Confirmation bias is the tendency to process information by looking for, or interpreting, information that is consistent with one’s existing beliefs. This biased approach to decision-making is largely unintentional and often results in ignoring inconsistent information.
We’re all very likely to fall into confirmation bias for the simple reason that it makes us feel intelligent. When we pursue a risky avenue, we seek people who will give us positive feedback and encourage us which is precisely what we want in terms of building our confidence. At the same time this should not be interpreted as objective evidence. And it’s not necessarily what we need to make a good decision.
What we actually need is the disconfirming evidence, which is evidence that refutes an opinion or forecast. Interestingly disconfirming evidence is regularly overlooked in data analysis and widely-considered to be the most underused.
Decision trap 4: Overconfidence
Overconfidence is ever pervasive in communications, and is arguably a requirement in this field to validate pitches or concepts. However, it’s a pitfall in the data science world, even for people whose jobs are to look at data and be objective. While we don’t want people who aren’t hopeful, as we would never have those breakthrough findings, at the same time, we need to stay objective and understand when something works and when something doesn’t. Therefore, we can see precisely why being able to use data in the right way is so critical.
Decision trap 5: Narrow Framing
Psychologically, the framing effect is a cognitive bias where people decide on options based on whether the options are presented with positive or negative connotations. When we use analytics, we can clearly define the objectives and the boundaries of those projects from the start. And this is not just important for our own understanding, it’s also critical for our peers so everybody can build a common consensus about what is it that we’re trying to achieve, how we measure success and what might be some of the limitations of our analysis.
When we suffer from a narrow frame effect, big data and advanced analytics can help us widen our frame because we can explore directions or hypothesis that normally we wouldn’t be able to simply because we wouldn’t have the opportunity to collect this data and infer patterns from them.
Summary
The general consensus indicates that the main obstacle for good decision-making is that we’re unaware of our own biases. If we think about common behaviour in the workplace, such as seeking positive feedback and recalling this only, we can see how our thinking can be susceptible to all these traps. However, as we know, avoiding negative feedback equals missing important insight for continued learning and development, a sentiment that is echoed greatly in data analysis.
- Published in Data, Psychology
