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Why Do People Buy Life Insurance? (Updated 2020)

Life insurance is purchased for three reasons.

  1. Estate Creation: from the moment you are protected by a policy, the people you care about will be provided for if you pass away.
  2. Estate Preservation: when estate or death tax must be paid, a life insurance policy can be a way to ensure that you don’t have to sell assets harder to turn into cash, such as a house.
  3.  Protection: when a primary income earner passes away, a life insurance policy can be built to provide enough money to pay for the surviving families’ living expenses.

All of these reasons for purchasing a life insurance policy have the common theme of providing money to the beneficiary of the policy. That is exactly what a beneficiary is: the person, or people, who receive the benefit. On the other hand, the policy owner is the person who pays the premiums, and the insured is the person who is protecting the beneficiary.

Typically, the policy owner and the insured are the same person, but they don’t have to be. When the insured passes away, the beneficiary typically receives the death benefit, or the amount of the policy, tax free. There are some exemptions to this rule, as is typically the case with tax laws (sigh…).

Types of Life Insurance

There are two types of life insurance, term and whole. Term life insurance provides protection for a specified period of time. Whole life insurance will provide a benefit (as long as the policy holder decides to keep paying the premiums, or other exemptions don’t apply), and can be paid out to the policy holder if he or she lives to a certain age, typically 121.

Whol Life Insurance

Whole life insurance policies can accumulate a cash value, which typically amounts to the amount paid in excess of the cost of the insurance and the fees. Also, the amount paid is put in an account and gains interest.

Because life insurance premiums are paid with after tax dollars, the amount of premiums paid is not taxable, but the interest can be. It is typically not taxable if paid to the beneficiary upon death of the policy holder, but if the cash value is withdrawn it may be. It also grows tax deferred in the insurance companies “general account”.

Sometimes, the policy owner will take a loan against the death benefit (usually not exceeding the cash value), which would mean that, although they would pay interest on the loan amount, they would not have to pay taxes on the interest upon receiving the money. If you do purchase a policy, all of the specifics will be explained to you so that you understand.

Indexed Life Insurance Policies

A newer option for life insurance is to index the amount paid to the stock market. The cool thing about these sorts of policies is that, although you can gain when the stock market goes up, you are protected if the stock market goes down.

This may be a good option because the economy has historically gone through economic cycles of expansion and contraction, where the stock market goes up and then it goes down. An example of a contraction would be the Great Recession of 2008. The average length of an economic expansion is 58 months, or just under five years. The United States is currently in the longest expansion in history.

If history is to repeat itself, as it usually does, we could be due for an economic contraction, or recession soon. This means it may be a good time to use an indexed financial product such as an indexed annuity of indexed whole life insurance policy.

The Bottom Line

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About Data

Data Analytics Offering

Machine learning is transforming industries, but businesses should be afraid of it. Transferring systems or integrating systems can lead to losses instead of gains. In some cases, businesses crumble when trying to innovate because their operations come to a screeching halt.

Implementation is Everything

There are a thousand offerings that can be sold to a business. Promises of automating expensive workflows sound great. Remember that when you are messing with important workflows there is much to lose, however. Businesses today know the importance of technology, but it’s good to keep in mind the importance of business as usual. Implementing new systems takes careful planning. Serving the customer today ensures the business will continue to serve customers tomorrow.

Start something great, something new, today for tomorrow.

Futuristic Products

That being said, there is much to gain from implementing the technology that already exists. Amazon has many awesome tools that, with that careful planning mentioned earlier, can maximize the potential of your business.

Now is time to talk about a few of them, and the implications that they bring. Keep in mind that this business (Sage Market) is all about innovating to use existing technologies to their maximum potential. I hope you will contact us through any of the channels below the header image to talk more about this.

Amazon Kendra

When I first heard about NoSQL databases, I was grateful that I had found a reason to slack off about learning SQL (I am happy to say I understand SQL databases).

Basically, a NoSQL database is a distributed database that is supposed to make it easier to query data, and I was taught in business school that it would have implications when it comes to unstructured data.

I think that a good analogy for this is Google. Google indexes and organizes lots of unstructured data and then decides what you mean when you search for something. When I took my second business analytics class they said that NoSQL was supposed to allow querying of data without using SQL, like Google.

Unstructured Data?

Let me back up a bit. What is unstructured data, and how does it differ from “structured data”? I back up like this because this is a very important point not only to the idea of NoSQL, but to the application of machine learning to querying databases. Extracting the truth from the data is easy when the labels give you the answer, but becomes much more difficult when the algorithm has to label the data itself.

"BUSINESS HAS
ONLY TWO FUNCTIONS
MARKETING AND
INNOVATION"
-MILAN KUNDERA

Sage Market Technology Consulting

The real reason I got so excited when I heard about NoSQL was that it seemed that to create a NoSQL database (as the hype misinformed me), the unstructured data would need to somehow structure, or label itself. Otherwise, how could you query the data in a number of different ways and receive the same information? The query would need to contain a kernal of truth that the system could search for in the data.

Enter Amazon Kendra... (again)

So my interest in NoSQL has really just manifested itself in Amazon’s Kendra product. Instead of putting a bunch of effort into labeling and organizing your data so that it is available to the people who need to communicate it, gain from Amazon’s machine learning algorithm. Let it do as much heavy lifting as possible, and only after you find out what the tech can do should you invest more time and energy.

Clarify with an Example

I used to work for a Credit Union, answering the phone and talking to customers about their accounts. Surprisingly, customers would ask me, being paid close to minimum wage, about esoteric financial regulation. We were supposed to say, “Sir/Mad’am, I am not a financial advisor/accountant, and therefore cannot advise”, but I like to talk about esoteric financial regulations, strangely enough.

One day, a woman called and asked me about the title on a boat that she was going to purchase. It was different from a normal loan, however, because it had been owned by the Coast Guard. It had a special lean on it, called a Ship’s Preferred Mortgage, or something like that. You might think, there is no way anyone could know about that, but my boss pulled it out of thin air. I was jealous, but impressed. I found out later that he had just entered a slightly different search into our internal intranet, and it was written there in plain English.

Ok?

You might be thinking to yourself, Hayden how does this relate to using machine learning to improve business processes? I’ll tell you how. When that person asked that question, a voice recognition software could have triggered a search into the internal database, using Amazon Kendra’s artificial intelligence to query and return the right information. Then, it could have just popped up on my screen.

I want to stress that this sort of technology could be possible for your application, too! With a little imagination and the right research, any business can implement these awesome tools with little to no risk. Then, benefit from the profits, give back to the community, and go down in history as generally awesome.

Who are you?

Thanks for reading this! I hope that you contact me to talk about how machine learning can be used to improve you business effectively and ethically. Also like us on Facebook and Instagram, or e-mail me directly.

Sage Market is Growing

We want to create a network of entrepreneurs to spread the goodness of technology. If you are added to the e-mail list, I will reach out to you personally. To be added to our automated Facebook Messenger list & receive personal communication from me, click the button.

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Artificial Intelligence Neural Networks Intuition Introduction

Intuitively Understanding Machine Learning

Artificial Intelligence. The buzzword if the decade. Some people are scared of it, others are scornful. Populist voices angrily express their fear of being automated or worse deny the possibility. Is it denial or misunderstanding?
 
One thing is for sure: artificial intelligence is misunderstood for good reason. The same mentality from Einstein’s famous quote about quantum mechanics applies: “if you think you understand [artificial intelligence], you don’t understand [artificial intelligence]”. 
 
This is especially true for artificial intelligence that is a “black box”: we actually can’t explain the predictions, but we can measure the accuracy of them. 
 
We can measure the accuracy of a prediction by pretending we don’t know the answer and then comparing the predicted value to the actual value. In order to increase the accuracy of our predicted accuracy, we can split the data up as many different ways we want, training on the one section and testing on the other, ten, twenty, or even a hundred times. This is called cross-validation.
 

Neural networks are famously hard to model. When I say model them, I mean explain why they come up with the prediction that they come up with. This is because they use something called  automated feature selection. This means that the variables that make the prediction are automatically chosen by the neural network from the data.

Each one of these ‘features’ is called a perceptron, and they can be layered, each layer and link between representing the relationship between the features/perceptrons.

These are exactly like the associations that we have baked into our brains, and that’s the analogy to our brains that the “neural network” represents.

Slope Intercept Form (Yawn...) for Neural Networks (Yay!)

Remember slope-intercept form from basic algebra?  y=mx+b shows the relationship between y and x, which are both variables. In this equation, b is the constant
 
*eyes glaze over* – “WAKE UP!”
 
Okay, so now think really hard. We have two variables that we are trying to show a relationship between. Sound important? If we can model the relationship between two variables, we can make predictions and provide insight into why there is a relationship! 
 
So the m in this equation is called the coefficient and it is important because it quantifies the relationship. It allows us to model the relationship! Things get more complicated though, and then much more complicated (we’ll talk in a bit how to use generalize this idea of slop-intercept form to think about neural networks). 
 
Think about adding more variables. Instead of a relationship between two variables, such as height and weight, now we add other features of a person, such as hair color. Some of them won’t have an impact on what we are trying to predict (height), in which case the coefficient will be zero. So then we have multiple m’s/coefficients. 
 
Now’s the kicker. Take each one of those m’s/coefficients and put another equation inside of it, with it’s own m’s/coefficients. Now consider that maybe there’s a relationship between the m’s/coefficients in that m/coefficient and the m’s/coefficients of the other m’s/coefficients. Mmmm.. M&M’s.

Neural Networks Simplified for Intuition

There are many different types of neural networks, and one thing that differentiates them from each other is the relationship between the perceptrons, or the m’s. This is the more simple way to understand them that provides the intuition. When one perceptron’s impact on the prediction is visualized, it seems disjointed and random, but together they make the prediction. 
 
A great example is a number recognition system. AI’s are famously good at image recognition. For example, one of the features might be the way that a four has a horizontal line in the middle, and that’s all. The prediction power comes from the way that this relates to other lines, parts of numbers, or other types of features.
 
Thanks for reading this! Stay tuned and follow us on Twitter or Instagram for more.

Product is to Research is NOT as Research is to Product

Extreme Content Marketing

One of the issues with the nature of academic research is that it is inaccessible to the public, or the wrong pieces of information (sometimes lies) are propagated as click bait, because that is what sells. Today I’m going to talk about a project I’m working on (in general terms) and lay out a strategy that I like because it is not only good marketing, but it could have a positive impact on the world.

If your marketing looks like marketing, it's not good marketing

Essentially, if the product offers real value then it may take some time to educate the consumer about it. This is especially true in the wellness niche because the body and brain are super complicated. Moreover, it takes real research to transcribe academic papers and sift through the bullsh*t to find the truth.

Product discovery is all about finding the truth about what really offers value. Content marketing can basically be about communicating the research that led to the product, but sometimes it gets really complicated. In this case, where nobody really understands the value of the product except for the researchers the led to it’s creation, the sales funnel essentially becomes a class on the value of the product.

People are afraid of what they don't know

Everyone hates marketers, because there is such a clear monetary incentive. People hate to be manipulated, but this is what marketers do. Education is a form of manipulation, especially if you are building a sales funnel that educates about the product, because the end goal is a monetary incentive.

Question: when do you have enough credibility to reveal the monetary incentive?

In order to promote a product, the agency problem needs to be resolved in the purchasers mind. This is just a fancy way of saying that the customer needs to trust that you have their best interests at heart. If you are operating within the health & wellness space, they need to think that their health is a priority and is aligned with the profit-maximizing motive.

Essentially, the monetary incentive for selling the product needs to align with their wellness goals. This is a delicate process, because if the content seems too good to be true, it turns the customer off. If they don’t understand what you are saying, they think they are being manipulated, especially if the content contradicts something they already believe.

Therefore, if there is no way to engage with the customer directly, the content needs to anticipate these objections that present barriers to proper product education. Market research is a most important process.

The role of MARKETER

Content marketing presents a unique presence to the agency problem. Obviously, the goal is for conversion. This is best understood by describing the creation of content in general. On one hand, you have a journalist who is supposed to report the news and the facts. Their job is to be unbiased so that their reputation will not be tarnished.

The incentive here means a strict loyalty to what will build a certain type of audience over the long term, not what will sell a product. The type of audience they are trying to build determines the extent to which the content is politicized or radicalized.

Reputation is important, but understand that it is not loyalty to the whole truth for all reporters, necessarily and unfortunately. For some it is if they are building the type of audience that craves this sort of content. For others, they will purposefully politicize and radicalize because they are after that niche. The journalist keeps their audience in mind, with the goal being to “preach to the choir”, to some extent, one of those choirs being loyal to the idea that they are not a choir.

On the other hand, you have cost per action for impulse purchases. They want a click, or a sign-up quick, and might bend the rules, using every psychological trick in the book.

It is all for money right now for these invisible entities, even if that means throwing a 30 day guarantee on it (because most people are too lazy to ask for a refund, but it legitimizes the purchase in their mind). They give everyone a bad name, but it makes sense that this would run rampant because of the clear monetary incentive.

The role of Content MARKETER

As a result of being screwed every which way (myself included), people
hate to be sold to. Sales and marketing is a dirty word. Content
marketing is supposed to be the redemption. Instead of selling, we are educating on the product, they say. Each objection is another opportunity to come to a resolution and educate about the part of the product that overcomes that objection.

Okay, great. But that still sounds a little manipulate-ey to me. What I propose is a little bit different. The role of the content marketer should be to distill the truth of academia into the most easily consumable media. Moreover, they should start with what research led to the product discovery, and then use the credibility inherent in that research to build the social proof.

If you think your job is to come up with great ideas as a marketer, or that the product person should be doing the research you are wrong. The government subsidizes research and development to the tune of billions of dollars, providing it to the private sector at extremely discounted rates. 

Keep it Simple Stupid!

Okay I get it. Academia is ridiculously convoluted and very scared of people with the word market in their job title. But remember that the availability of information in this new connected world means that credibility can be verified. You are your reputation.

Content marketers are increasingly journalists, essentially. Adhere to the truth to build a reputation of educating and offering product based on discovering through research, rather than the other way around.

The redemption is not in the fact that we are educating about the product. The redemption comes from the fact that the research led to the product, and you are reporting on that process. If it is the other way around, run like hell from that product.

Elasticity & Agility on AWS

Value is a Value Prop

Sage Market is about adding value to your business by first applying existing technologies. This article is going to talk about a couple of the value proposition that Amazon Web Service’s technologies offer: agility and elasticity. More importantly it is going to discuss why these value propositions are important. By the time you finish reading this, I hope it will be clear why Sage Market is worth your time.

The plan for Sage Market is to become part of the Amazon Partner Network, among other things. This means Sage Market will be an Amazon affiliate, being knowledgable of the Amazon product catalog and experienced in deploying the solutions. Additionally, it will bring a breadth of knowledge about how to apply technology in other ways to grow and improve the business.

Amazon Products

Amazon Web Services is a suite of tools that allow businesses to take advantage of technology in some important ways. It allows you to serve your customers better because it offers agility and elasticity. Agility means that you can experiment quickly and cheaply. Elasticity means you can scale your business to meet surges in demand that might otherwise cause your system to crash. Let’s talk a little more about what this means, starting with agility.

Agility: Smart R&D

Businesses today face a seemingly insurmountable challenge: innovate or be squashed. You might say to me, “that’s all good and fine, but I’ve been around for 70 years. I have got nothing to worry about. Besides, I couldn’t put people out of work by innovating. That would be heartless.” To this, I shake my head and grimace.
 
Both political parties are growing tired of free trade and globalization, fueled by fear of the masses. They have a secret, though. As populism rises and manufacturing is incentivized in future legislation, state of the art plants will increase production.
 
These plants will, much to the dismay and surprise of the populist movement that hates losing their jobs, use automation and hire a fraction of the workers.

 

There will be new entrants that come into the market and compete with this state of the art capital, even if you don’t. They won’t even have employees that they would lay off with the new technology. They’ll sure have an incentive to automate your manufacturing business cheaply, though.
 
Any incumbent business owner who has benefited from competitive advantage to profit for years should be investigating how the recent tech boom will affect them. If they have even a shade of foresight they will be researching and experimenting with how they can use technological advancement to fend off the future of specialized labor that will be AI.
 
What is the best way to do this? Cheaply and with minimal risk, of course. This is what agility is. It is the ability to experiment with your finger on the pulse of your business, reading and analyzing your data in real time to pivot or embrace whatever it is your are experimenting with.
 
Amazon has tools to optimize this process. Now, agility is important, but how can this be possible? Elasticity and agility are so related it can be confusing to decouple them in the mind.
SAGE logo

Elasticity Universal Truth

Elasticity is being able to react to market activity in real time. It means scaling your activities in direct response to what your customers are doing. This is simply good customer service, but it is also cost effective.

Just as important to the bottom line as increasing sales by being responsive is to minimize costs. It is also great for the environment, so by being responsive to your customer you will save the planet and attract more customers.

Any business savvy individual knows that a dollar saved is a dollar earned. If you are chasing dollars for your business, whether that be for society’s good or to buy a new car to show off to your neighbor, there is no difference between a dollar from cost-cutting or sales-increasing. This is a universal truth.

Get Involved

If you are serious about staying relevant in the changing landscape, it is cheap to get started. I will work with you and create a quote for you and your business for free. If I can’t add value, I would not even think of charging a dime.

Additionally, I am looking for people that are ambitious about applying technology to change the world in a positive way. Sage Market is in it’s infancy, but it has a positive future, just like us. Check out our social media channels, or e-mail us at support@sagemarket.info.

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