Every other person wants to be an entrepreneur or claims to be one. Start-ups pursue billion-dollar valuations, but as this article here shows, not many are able to pull the mythical ‘unicorn’ out of a hat. In fact, the majority are better at pulling the ‘disappearing act’.

As I was reading this particular article, I couldn’t help but ponder, ‘what is true entrepreneurship and innovation’? Having once been an accidental entrepreneur myself, I know that it’s not all smoke and mirrors in a magic show. An inflight movie on entrepreneurship answered my question. (And no, it wasn’t The Social Network.)

The movie, Pad Man, chronicled Arunachalam Muruganantham’s journey as an accidental entrepreneur, which started with wanting to solve one simple problem: making sure the women in his family had access to affordable sanitary pads. While the movie may have been embellished in some ways, Arunachalam’s story imbues simple lessons that strike the true core of what entrepreneurship is and what drives innovation.

Fix a problem

Most people have an innate instinct to shun problems. Trying to solve them takes a lot of time, and there’s no guarantee of a payoff. By contrast, entrepreneurs and innovators actively seek problems out. When they see a problem, they see an opportunity. They would ask, ‘How can I fix this?’.

I’ve always believed that questions are far more important than answers because questions set you on a journey of curiosity, testing, discovery and learning. Answers, without further questions, are a killer of knowledge.

Passion without action is a burden

People love to tout and shout about their passions. I call them ‘passion fruits’ — full of flesh and seeds; there’s so much potential for fruition. There are many ‘passion fruits’ out there, but whether one rots away with time or becomes a tree full of potential harvest, time will reveal.

If you have a business goal or personal dream that you truly believe in but don’t do anything about, then that passion is nothing but an unfulfilled emotion that will burden you instead of driving you.

Successful entrepreneurs and innovators are not made in R&D labs

Companies spend billions in R&D to create the best products or solutions. But that’s not where successful entrepreneurs are made. ‘Pad Man’ didn’t have millions of dollars to develop his product, but what he did have was T&F – the school of ‘Try & Fail’.

It wasn’t just his product that improved. Perhaps more importantly, he gradually became a better entrepreneur. With every failure, he learned a lesson about aspects of product creation or marketing and became grittier. Despite a series of devastating setbacks – being left by his wife and mother, ostracised and chased out of his village, and losing money the whole way, he never gave up.

Making millions ≠ Making millions happy

Every start-up seems to have the ultimate goal of reaching a billion dollar valuation. Some are inflating their worth more than others. There’s nothing wrong with that. After all, every company needs cash flow to keep daily operations running and investors smiling.

However, making millions or billions of dollars doesn’t always mean you’re making millions of people happy. And, that’s what made Arunachalam Muruganantham stand out for me. He understood and embraced that simple rule.

A businessman is someone who wants to make billions; an entrepreneur and innovator is someone who wants to help billions. When you focus on winning hearts by changing lives, the money will more often than not naturally follow.

Real disruptions

‘Disruption’ has been another buzzword thrown around, often with little substance behind it. These days, everything in new tech seems to be ‘disruptive’. A new generation of iPhones with super retina lens is not disruptive. A new online marketplace to buy and sell used products is not disruptive.

No new innovation or technology is disruptive on its own. Only when it changes and disrupts mindsets, lifestyles and habits can it truly earn that title.

Entrepreneurship is NOT exclusive

Many people still think that to be a successful entrepreneur, you need millions in capital to start, or that you need to have attended an Ivy League or a top notch business school. There is a certain ‘qualification’ or academic pedigree that has come to be associated with, and define, entrepreneurship.

Entrepreneurship and innovation are not and should not be exclusive to certain backgrounds or demographics. The story of the Pad Man has doubly proved that entrepreneurship and innovation can come from anywhere and anyone.

Ultimately, true entrepreneurship and innovation are not about making billions and chasing fame. At its heart, it is about solving problems and inspiring others to do the same.

This article was originally posted on ExchangeWire

AI is infiltrating everything we do, even if we aren’t aware of it. And it’s no different in digital advertising. In this piece for ExchangeWire, Cheryl Guzman Ng (pictured below), APAC head of marketing and innovation, Xaxis, explains how marketers can look to adopt it in their marketing operations.

For advertisers, the objective of a media buying strategy is simple but challenging: to reach the right audience with the right message, at the right time, in the right context, and the best price. To achieve these measures often requires a tedious process of gathering enough information to formulate a plan, and then to implement that plan and continuously optimise towards the desired results.

On the other hand, Artificial Intelligence (AI) – a much-hyped technology in recent years – is steadily infiltrating every industry; and advertising is no exception. The good news is that as the buzzword becomes reality, we can finally start to say goodbye to long hours of manual data gathering and analysis. The bad news is that, for those who don’t embrace the potential and possibilities of AI, it will be increasingly difficult to remain competitive and relevant.

Regardless of where you are trying to reach or convert your consumer in the customer cycle, there is bound to be an AI-powered marketing tool that is deployed: recommendation engines, retargeting, predictive analysis, dynamic creative optimisation (DCO), chat bots, social engagement, etc.

One area where AI excels, and its capabilities are undoubtedly unquestionable, is programmatic advertising: a world where billions of impressions are auctioned off in a split second off the back of insurmountable data. Of course, it’s not compulsory to use AI, but the efficiency and effectiveness of your campaign won’t be able to compete with others who do, and you probably should not be in business if you’re still in ‘manual mode’.

Speaking of ‘manual’, it still remains that to fully utilise AI, you can’t do so without talent and data. An AI-based bidding strategy should begin with programmatic specialists who have a thorough understanding of the current advertising landscape and can design, implement, optimise, and analyse. In addition to marketing expertise, programmatic specialists will need data science and engineering acumen to optimise AI-based campaigns in ways that lead to the best outcomes.

To be specific, the two types of talent you will need on your side are scientists and engineers. The scientists will define the fundamentals of the project, developing a sound proof of concept. Then they will choose an algorithm that will test against the proofs, and decide what input and outputs to implement, depending on the data available and desired business outcomes.

Meanwhile, engineers will assemble, customise, and deploy the algorithms, scaling and making them repeatable, all while gathering and defining the data. This explains why even though there are plenty of free open source algorithms available online, you don’t have the advantage until you have the talent (and data!) who can customised them to your business needs. That explains why data scientists and engineers who specialise in AI are so in demand.

If your company doesn’t have these skills in-house, you should consider partnering with an agency. To help you choose the right partner, here are a few things you can request from them:

1. Examples of machine-learning models and algorithms they’ve run successfully
2. Evidence of return on investment that their AI installations have produced
3. Evidence that their AI outperformed manually traded campaigns
4. Examples of their ability to handle multiple competing requirements across multiple geographies and languages

Perhaps most crucially, the potential partner must give examples of ways they’ve customised their algorithms to not only work with clients’ KPIs, but also their proprietary, first-party data. After all, data is the new oil that fuels the machine intelligence.

At Xaxis, AI is at the core of what we do. An example of how we deployed machine learning with customised algorithms was to help a luxury retail brand with a unique return on ad spend goal for their digital campaigns. Our team customised a regression algorithm to analyse log-level data and predict better-performing impressions. Copilot, Xaxis’ AI engine, used its ‘Predictor Strategy’ to look for features with the strongest correlation to performance to drive smarter optimisation. Thanks to the AI-customised algorithm, we were able to achieve a 53% increase in conversion rate and 35% increase in CTR, compared to manual.

As I said, it is not compulsory to include AI into your tech stack.

As a matter of fact, it is a necessity.