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Human

On intellectual humility

On intellectual humility

We can describe intellectual humility in different ways: the opposite of intellectual arrogance (Whitcomb, et.al., 2017); the low concern for social status (Roberts and Wood, 2003); a virtuous mean (Church, 2016); an attitude (Tanesini, 2018). In this small corner of the world, I’d like to shine a different light on intellectual humility. I hope we can use it as we learn from each other.

Willingness to learn

It’s possible that the willingness to learn is a sign of intellectual humility. It’s when someone is willing to grow because they realise that there is room for growth. Even though the learning is too challenging, or labeled too easy by others. That could mean having the initiative to connect the dots yourself, awareness of your own limits, and the grit to take on tasks deemed impossible.

Failing better

With intellectual humility comes many failures. In other words, it may be alright to fail  as long as you fail better. When you find out why you failed and try to improve on it next time, or take that lesson and apply it somewhere else, that’s failing better. Mistakes are part of the journey.

Openness to diverse perspectives

It’s hard to flourish with narrow thinking. On the other hand, it’s exciting to learn different perspectives. There’s a delightful moment when you realise that something that has always been one way can be done or seen another way. That stands for many aspects of cultures, societies, disciplines, and  domains.

References

Church, I. (2016) ‘The Doxastic Account of Intellectual Humility‘, Logos and Episteme, 7(4), pp. 413-433. doi: 10.5840/logos-episteme20167441.

Roberts, R. and Wood, J. (2003) ‘Humility and epistemic goods‘, Intellectual Virtue: Perspectives From Ethics and Epistemology. In Zagzebski, L. And DePaul, M. (eds.). New York: Oxford University Press.

Tanesini, A. (2018) ‘Intellectual Humility as Attitude‘, Philosophy and Phenomenological Research, 92(2), pp. 399-420.

Whitcomb, D., Battaly, H., Baehr, J., Howard-Snyder, D. (2017) ‘Intellectual Humility: Owning Our Limitations‘, Philosophy and Phenomenological Research, 94(3), pp. 509-539. doi: 10.1111/phpr.12228.

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Human

Not yet: You didn’t fail, you just haven’t passed yet

Not yet: You didn’t fail, you just haven’t passed yet

Beyond intelligence and talent

Praise the process and effort, strategies and focus, perseverance and improvement (Dweck, 2014). This develops something called the growth-mindset. It creates resilience. It also shifts our focus from praise to process.

Young companies adopting this approach are refreshing. These places do not punish someone’s failure. Instead, they reward initiative and resourcefulness. They are disrupting industries and blazing trails.

Don’t praise intelligence and talent. That has failed.

Praising intelligence is detrimental. People turn to cheating, point fingers, or find people who they deem are worse than they are. It feeds the impostor syndrome; and the impostor takes every chance he or she gets to manipulate anyone who listens. Relying on talent alone is no more helpful because sooner or later, ego could get in the way.

In social contexts and many other life settings, someone who is smart or can fake smarts remain in control. It’s not sustainable. History demonstrates the consequences of such folly. 

Business value does not equal bottom-line

At work, and in a majority cases outside work, generating value does not mean taking home more financial rewards. Value also lies in the learning that takes place, the goodwill generated, the social repercussions, and the long-term implications of an undertaking.

Not yet

If you make mistakes, that’s ok. If you are rejected, that’s ok. If it’s not working out, that’s ok too. All these just mean, not yet. You didn’t fail.

References

Dweck, C. (2014) The power of believing that you can improve [Video]. Available at: https://www.ted.com/talks/carol_dweck_the_power_of_believing_that_you_can_improve/transcript.

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Tech

Machine learning use cases in marketing

Machine learning use cases in marketing

Machine learning in marketing for consumers, businesses, and practitioners

Marketing is an interesting place to start exploring the applications of machine learning because it impacts all of us as consumers. On the other hand, businesses will find that marketing is quite a fertile ground for machine learning. As a practitioner, it is fascinating to find how one can find insights from the data to help a business  identify, anticipate, and satisfy customer needs and wants. What are some examples of machine learning use cases in marketing?

Machine learning use cases in marketing

Churn or attrition modelling can help predict the chances of a customer leaving. It can also help determine the key drivers of churn and serve as a metric in daily business operations to minimise chances of churn. Meanwhile, cross-sell and up-sell models can find opportunities to provide a comprehensive purchase experience for the customer.

A customer lifetime value model determines the length of customer engagement with survival models. Classification models can help estimate the probability of customers leaving. Another avenue of interest is projected revenue based on knowledge of revenue made from a customer thus far. Meanwhile, a loyalty model can determine key drivers of customer loyalty.

The marketing mix model can help target the right audience at the right time with the right frequency through the right channel at the right price. This is a very powerful combination of insights for the business to meet or even exceed customer expectations. It benefits the consumer because they would receive highly relevant offerings tailored to their needs and wants.

Sales forecast models helps estimate short-term and long-term sales. It uses time series, multiple regression, and many other types of machine learning models. On the other hand, price elasticity models can measure the change in demand with respect to the change in price. This way, revenue may be optimised. Another way to optimise marketing decisions is through A/B testing models.

Market segmentation models can help find the optimal number of market and customer segments. Through these models, we also discover different types of segments. From there, we can design marketing strategies that fit each respective segment well.

Machine learning in marketing and our daily lives

Machine learning leverages data to make predictions, find interesting patterns, and perform particular tasks without explicit instructions. It may sound boring to most folks, but there are in fact many exciting applications of machine learning in business and our daily lives. There’s a gamut of machine learning use cases in marketing alone. Marketing can use machine learning as an optimisation engine to enhance strategic and tactical decisions, and keep customers happy.

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Human

The role that charm plays

The role that charm plays

It’s not explicitly written on  job postings or candidate profiles but it’s the first thing that sells. Charm is an emotional sleight-of-hand. Unfortunately, the role that charm plays can sometimes replace even sincerity, passion, and  a genuinely promising prospect.

Rough on the edges

There are many extremely talented and deeply cerebral individuals for whom the social conventions don’t come naturally. What comes naturally to them is what they do well. They find solace in the state of flow (Csikszentmihalyi, 1998) but are uncaught-up in things unrelated to their passion. What kind of work would be possible if sincerity and empathy were enough to make up for the rough edges? If it wasn’t just about charm, I imagine better  results in the short-term and long-term because then there is both talent and heart in it.

About culture fit

Over the years, I’ve observed that shared values between the company and an individual are good indicators of culture-fit. This applies to professional and personal company. Common values, drivers, and goals overcomes differences in race, age, gender, professions, and socio-economic status. It even trumps skills-fit and experience, in my opinion. The reason is that skills and experience are learnable and attainable overtime but values don’t just change.

The reason why

Of course, charm has a place and will always have a place. Diplomacy and politeness are always desirable. Presentable looks don’t hurt either. But I reckon, if we step back, we will find that there are other things we wouldn’t want to trade for the X factor. Traits like sincerity, humility, and loyalty. Values like grit and growth. And things like meaning, purpose, and impact. They matter more than charm, don’t they?

Lest we forget.

References

Csikszentmihalyi, M. (1998) Finding Flow: The Psychology of Engagement with Everyday Life.

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Human

Learning fast and slow

Learning fast and slow

Fast learning is exhilarating and gets rapid results while slow learning is a profound and  rewarding experience with compounding benefits.

The author of the book A Mind for Numbers described slow learning as a profound experience. The fast learner is the hotshot on the race track with a Formula-1 machine blazing ’round and ’round, getting all the claps and hurrays. Meanwhile, the slow learner is the hiker up on the mountain, breathing the fresh air, taking in nature, and appreciating life one moment at a time.

Learning fast

Fast learning is exhilarating and gets rapid results. It can be glorious, although easily blinding. When it is stripped off of the buzz and sometimes ego-pumping compliments from the audience, it can be genuinely useful in getting things done. Ticking clocks and backlogs benefit from fast learning. However, too many shortcuts build debt overtime and an unhealthy habit of quick fixes that overlook the long term. It’s not for the long run but it works upfront.

Learning slow

Slow learning is a profound and  rewarding experience with compounding benefits. Learning doesn’t have to be glorious, it can be humbling instead. Most times there really is no need for an audience. It’s just you learning, it’s just you and the journey. The benefit is that you build flow, depth, and  focus, among other things. The outlook is not narrow nor shallow and it stretches beyond just getting things done. There may not be upfront benefit all the time, but it pays manifolds in the long run.

Learning fast and slow

There is a place for both learning fast and slow. Although slow learning is ever-so-often discounted in the mainstream, it creates profound experiences and  results. Fast learning is sometimes overhyped, but if harnessed thoughtfully, it can build momentum and rapid results.

References

Oakley, B. (2014) Learning how to learn [Video]. Available at: https://www.youtube.com/watch?v=O96fE1E-rf8.

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