4 Ways to Apply Machine Learning to Business Problems

The technology boom is surging, and machine learning is the core way that businesses are taking this once new technique and turning it into something amazing.

As a testament to this growth, MIT Sloan Management Review reported that 76 percent of companies now use machine learning as a normal part of their business model to boost revenue. Maybe you're new to using machine learning in your company, or maybe you're looking for ways to improve your already established system.

There are a number of ways that you can apply this revolutionary model to your business and start getting results. We are going to look at some of the top ways to apply machine learning to your business problems and how this will benefit your company.

Let's talk numbers. Americans spent – brace yourself – 11,000 years shopping online just during Cyber Monday 2018. If that doesn't tell you everything you need to know about how many and how often people are shopping online, you should also know there were 7.9 billion dollars in sales during that day.

What does this mean? It means that depending on the website, it could be a hacker's paradise.

Machine learning can help curb the risk of fraud while customers are shopping on your website. Take PayPal, for example. They recently added new algorithms to their transaction process to prevent money laundering to and from unsuspecting customers.

The algorithm looks for data points that spark the system, such as first-time transactions,  the amount of money sent, sender/receiver location and more. PayPal has an automated, filtered system that flags specific transactions so humans can double-check them for fraud.

It's astounding to think about how far online businesses have come over the past few years. At one time, you had what was on the storefront and that was that. Now, with machine learning, your system is constantly running data points on customers based on their profile and the type of things they search for on your site/store.

You can change your algorithm to target customers who view certain content pages in your blog. For example, if you sell pet products, you can use machine learning to sell cat products to people who view your articles with cats in the title.

This is a win-win situation, because you can link to your products within your blog post. Machine learning is expanding in leaps and bounds and able to offer customers the right products at the right time.

On the fly pricing or dynamic pricing, is another feature found in the world of machine learning. In the old days, we had to offer everyone a flat rate. This takes the cross-selling aspect to a brand new level. Now, not only can you offer the right product at the right time, but you can offer it at the right price based on your user.

Airbnb is a great example of this type of machine learning, and it's carrying over to airlines and ride-sharing services like Uber. The artificial intelligence built into Uber's program analyzes each person, their route, other routes in the area and how they can offer their customers amazing deals that will keep them coming back for more.

The pricing structure can change depending on a variety of factors, including location, time, customer frequency and more. You can apply this type of pricing structure to your product through machine learning and build a business that really caters to every customer's needs.

Finally, you may want to consider using machine learning to help build your customer service presence. There are many businesses that use online chatbots to help answer customer questions.

There's no doubt that chatbots are boosting sales. Sites like Sephora, which sells beauty products, saw an 11 percent increase in sales when they switched to a bot to help customers on their Facebook page.

It's possible to build these chatbots from the ground up to reflect the personality of your business, help build rapport, acknowledge human slang and jargon and to carry on an online conversation as human-like as possible without a human on the other end!

If it wasn't clear before, it should be clear now that machine learning is only getting better as time goes on. You can use this technology to grow your business, protect your customers, offer the perfect products to people on your site and more.

There's no mystery – machine learning is here to stay.

In today's globalized economy, negotiating effectively is imperative if you want to be successful. In most fields, negotiating is part of the job, and you have to learn how to argue your points adeptly; otherwise, you can be on the losing end on deals, money and opportunities.

In normal, everyday life, negotiating is also important, because it's required in many different scenarios: When you buy a car, want a higher salary or to dispute your cell phone bill. Whatever the case may be, the ability to negotiate is a highly sought-after skill that more people should develop and refine.

To become a better negotiator, here are three tips to help you get started.

You wouldn't go into an important exam without studying beforehand, so why would you enter a negotiation unprepared?

Researching the company or individual(s) you will be negotiating with is absolutely essential if you're going to get the deal you want. Map out what they're looking for, what they need, what their limit may be and other factors that'll help you determine where you stand in the deal.

The more you research those with whom you will be negotiating and their options, the better the outcome will be for you. You will enter negotiations more confident and prepared.

They say we're born with two ears and one mouth for a reason, and when it feels like someone has been talking for ages, this advice comes in handy.

Negotiations aren't about dominating the discussions. It's more about understanding your opponent and the factors that attribute to their position and their offer. A lot of the time, you'll find out plenty of information just by sitting back and listening to what the other person has to say.

Follow the 70/30 rule of communication: Talk 30 percent of the time during your discussion and listen for 70 percent of the time. Show your opponent that you care about their offer and the reasons behind their offer by giving them your undivided attention.

By actively listening, you're showing respect for the other person rather than dismissing them, something that is far too common in negotiations. When you listen, communication and understanding improve, and you're both better able to see where the other is coming from. This allows for both parties to leave feeling satisfied and content with the outcome.

Harvard Business Review describes anchoring as a cognitive bias that occurs when too much emphasis or focus is put on the first number or offer – known as the "anchor" – submitted in a negotiation. Often this information, or number, then skews one's judgment.

Making the first offer in a negotiation has its benefits because it puts the person making the offer in a position of control. It automatically leads the discussion in their favor.

But when do you know if you should drop your anchor or hold out on your offer? Harvard University says the final agreement reached should be based on two things: the range of options acceptable to both parties and your opponent's zone of possible agreement (ZOPA). ZOPA is the bargaining range created by the two opposing sides, or how much each side is willing to bargain their offers.

If the size of your opponent's ZOPA is on the lower end – meaning they are less willing to negotiate – it's more difficult to drop your anchor.  If you know your opponent's ZOPA has a higher range and they're more flexible, stating your offer first will likely turn out in your favor.

If your opponent beats you to the punch and drops their anchor first, the most important thing is not to give in right away. This can be difficult when you feel the pressure and demands from the other side, but the calmer and more collected you remain, the better results you'll get.

Instead of saying yes or no right away, suggest finding a middle ground by continuing the conversation. You might say something like, "I see where you're coming from, but let's discuss X and Y a bit further so we can come to an agreement we're both happy about."

It's all about reading your opponent and, as mentioned earlier, doing your homework.

Negotiations aren't about talking over the opposing side or expecting certain outcomes without doing your homework. A successful negotiation requires research and the confidence that an agreement can be reached that is positive for both sides.

How will you handle your next negotiation?

I’ll never forget something that psychologist Daniel Kahneman said a couple of years ago at a people analytics conference. The keynote was largely about how algorithms can reduce “noise” (random, irrelevant factors that cloud our judgment) when we’re rating job candidates and trying to predict people’s performance. About halfway through, Kahneman made a quick, almost offhand comment that really struck me: He said he was “quite worried” about AI’s dark, dystopian possibilities, despite its great potential for good. The better AI becomes at making decisions, the less we’ll need human judgment — and that, he suggested, will threaten the power structure in organizations. Leaders won’t like that, so they’ll resist adopting the technology for their biggest, most important decisions.

That rang true when Kahneman said it, and it still does. It’s consistent with the ongoing human struggle to maintain control in the face of technological advancement, though it’s not one of the unintended consequences we usually think of regarding AI in the workplace. We tend to focus on other risks — amplifying cognitive biases, cannibalizing livelihoods, disrupting businesses. Those concerns are more than justified, but perhaps we have been a bit myopic and have overlooked something that’s as dangerous as relying too heavily on technology or allowing it to run amok: failing to reap its benefits, out of a deep, paradoxically self-destructive desire to keep calling the shots and preserve our status.

It’s an unsettling thought. But two articles in this issue of MIT SMR (and others we’ve recently published) offer useful reminders that there’s reason for optimism, too. As keen as we may be to retain power and its privileges, we also see ourselves as fair, and we want to live up to that self-image. AI is starting to help us in that regard. As Josh Bersin and Tomas Chamorro-Premuzic argue in “New Ways to Gauge Talent and Potential” — and as Kahneman himself said at the conference — AI-enabled tools can greatly reduce the role of bias in hiring decisions by screening for traits that affect performance and by disregarding those that don’t, such as the extent to which people look or sound like us. And in “Using Artificial Intelligence to Promote Diversity,” Paul Daugherty, H. James Wilson, and Rumman Chowdhury, too, urge us to hold ourselves to a higher standard of organizational behavior. They call on makers of AI systems to design, train, and refine applications that “ignore data about race, gender, sexual orientation, and other characteristics that aren’t relevant to the decisions at hand.”

Though these articles acknowledge that there’s plenty of room for our darker instincts to assert themselves, they suggest ways to bring us into the light — which feels constructive.

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The eight-hour workday harkens back to 19-century socialism. When there was no upper limit to the hours that organizations could demand of factory workers, and the industrial revolution saw children as young as six-years-old working the coal mines, American labor unions fought hard to instill a 40-hour work week, eventually ratifying it as part of the Fair Labor Standards Act of 1938.

So much has changed since then. The internet fundamentally changed the way we live, work, and play, and the nature of work itself has transitioned in large part from algorithmic tasks to heuristic ones that require critical thinking, problem-solving, and creativity.

Adam Grant, organizational psychologist and New York Times bestselling author of Originals: How Non-Conformists Move the World, says that “the more complex and creative jobs are, the less it makes sense to pay attention to hours at all.” Yet despite all of this, the eight-hour workday still reigns supreme. “Like most humans,” Grant says, “leaders are remarkably good at anchoring on the past even when it’s irrelevant to the present.”

Heuristic work requires people to get into the physiological state of flow, coined by Hungarian-American psychologist Mihaly Csikszentmihalyi in 1975. Flow refers to the state of full immersion in an activity, and you might know it best as “the zone.” A 10-year McKinsey study on flow found that top executives are up to 500% more productive when they’re in a state of flow. A study by scientists at Advanced Brain Monitoring also found that being in flow cut the time it took to train novice marksmen up to an expert level in half.

The Modern Organization Sabotages Productivity

Many of today’s organizations sabotage flow by setting counter-productive expectations on availability, responsiveness, and meeting attendance, with research by Adobe finding that employees spend an average of six hours per day on email. Another study found that the average employee checks email 74 times a day, while people touch their smartphones 2,617 times a day. Employees are in a constant state of distraction and hyper-responsiveness.

Jason Fried, co-founder of Basecamp and author of It Doesn’t Have to Be Crazy at Work, said on my podcast, Future Squared, that for creative jobs such as programming and writing, people need time to truly think about the work that they’re doing. “If you asked them when the last time they had a chance to really think at work was, most people would tell you they haven’t had a chance to think in quite a long time, which is really unfortunate.”

The typical employee day is characterized by:

“People waste a lot of time at work,” according to Grant. “I’d be willing to bet that in most jobs, people would get more done in six focused hours than eight unfocused hours.”

Cal Newport, best-selling author of Deep Work: Rules for Focused Success in a Distracted World, echoes Grant’s sentiments, saying that “three to four hours of continuous, undisturbed deep work each day is all it takes to see a transformational change in our productivity and our lives.”

Fried agreed, saying that he gets into flow for about half the day. “If you don’t get a good four hours of flow to yourself a day, putting more hours in isn’t going to make up for it. It’s just not true that if you stay at the office longer you get more work done.”

Despite advances in technology, and perhaps in large part because of it, many find themselves working well beyond 5 PM just to keep up with their workloads, but it doesn’t have to be that way.

How to Foster a Shorter, More Productive Workday

I conducted a two-week, six-hour workday experiment with my team at Collective Campus, an innovation accelerator based in Melbourne, Australia. The shorter workday forced the team to prioritize effectively, limit interruptions, and operate at a much more deliberate level for the first few hours of the day. The team maintained, and in some cases increased, its quantity and quality of work, with people reporting an improved mental state, and that they had more time for rest, family, friends, and other endeavors.

When I announced the experiment on LinkedIn, a connection responded with: “It’s nice in theory, but I can’t finish all of my tasks in six hours!” — as if all tasks were created equally. The law of nature that is the Pareto principle stipulates that about 20% of your tasks will create about 80% of the value, so it’s about focusing on those high-value tasks.

If you’re the manager of a small team with limited resources, take a moment to reflect on the following productivity techniques and remember that your job as a leader is to facilitate outcomes, not just the illusion of them.

Prioritize: Channel Pareto and focus on high-value tasks, aligned with both employee strengths and the team’s goals.
Cut: Reduce or eliminate tasks that don’t add value. Cutting your default meeting time from 60 minutes to 30 minutes, turning off notifications, and batch checking your email are all incredibly effective places to start.
Automate: If it’s a step-by-step process-oriented task, it can probably be automated, saving you from doing it yourself.
Outsource: If it can’t be automated, it can probably be delegated or outsourced. You’re probably not being paid to work on $10-an-hour tasks.
Test: A lot of time is wasted in paralysis analysis and on over-investing in the wrong things. Managers can avoid both through effective experimentation, measurement, and adapting accordingly.
Start: Do whatever it takes to start your engine. Block out time in your calendar, work on one thing at a time, do the hardest thing first, try listening to binaural beats or use the Pomodoro technique, a time management method that uses a timer to break work down into intervals, traditionally 25 minutes in length, separated by short breaks.

Set Realistic Expectations

Make it okay for employees to not be in a hyper-responsive state and schedule uninterrupted time to get into a state of flow. Similarly, make it not okay to be interrupting people on a whim. My team has a simple rule; if a team member has their headphones in, you are not to disturb them unless it absolutely, positively can’t wait (which is hardly ever, by the way). Doing so has been shown to decrease workplace stress, according to research by Gloria Mark at the University of California, which found that stress levels declined when email was taken away from U.S. Army civilian employees for five days, because they felt more in control of their working lives.

Some Things Are Worth Fighting For

By cultivating a flow-friendly workplace and introducing a shorter workday, you’re setting the scene not only for higher productivity and better outcomes, but for more motivated and less-stressed employees, improved rates of employee acquisition and retention, and more time for all that fun stuff that goes on outside of office walls, otherwise known as life.

Organizations are spending big money on digital transformation, but they could reap an immediate, and far more cost-effective transformational benefit just by changing the way they work, instead of what they use to work. Sure, it would be easy to pull out the “some great sentiments here, but it would never work in our organization” card, but some things are worth fighting for; ensuring that our people do their best work and live their best lives are certainly worth it.