In a job you sell your time and your energy for money - you will never become rich this way because of the inherent restrictions the laws of nature and of physics place upon us all. Entrepreneurship (eg. business, ideation, innovation, etc.) has been one of the few consistent and reasonably moral paths to both moderate and extreme wealth since the industrial revolution.
Of course, other paths such as crime and political corruption have always existed as paths to wealth for those who were willing to walk on them, but we are only concerned with paths that really add value to humanity and can at least be somewhat considered morally permissible.
No matter how hard you work and no matter how many hours you work, you will be restricted to the number of hours in a day, in a week, in a month, and in a year. With a job, you are selling your time for money. Your time might be worth little or it might be extremely valuable given your human capital, but you still are selling this finite resource for money.
The richest people in your towns and cities are generally not people who have jobs. Yes, someone in your city might earn $100,000 per year or maybe $250,000 per year working as a highly-paid individuals in a big corporation, but there are also plumbers, electricians, small accountants, small lawyers, dentists, doctors, programmers/coders, restaurant owners, website owners, that earn $500,000 or $1 million (or much more) per year through their entrepreneurial ability to use their human capital in a way that is not restricted by time. In effect, these entrepreneurs are able to expand the audience for whom they create value both in time and in scope - they can reach people even when they are not working (eg. website) and they can reach many more people (possibly millions) all by themselves. In this process, the create value for a lot of people and they are themselves able to extract a portion of that value as remuneration from themselves without having to rely on an intermediary in the form of an employer.
An Absurd Example of a Great Job to Bring the Point Home
There are 8760 hours in one year. Let's say you work like a crazy person and are able to work for 1/2 of that time. This means you work for 4380 hours in a year.
That 4380, represents about 84 hours per week without taking a single week of vacation. Clearly, we have an unsustainable situation if the work you're doing is in any way physically or mentally rigorous.
So, you -- a total workaholic per the above -- are making how much money? Well, that depends on your hourly wage. According to the Bureau of Labor Statistics (BLS), the average private sector hourly wage in early 2017 is $26.19. But you're not an average person - you're making a lot more than average in our example.
According to the BLS, the highest mean hourly in the US is for anesthesiologists who make about $130 per hour - this is even higher than surgeons, lawyers, doctors, and chief executives. But even then, let's say you make even more than that.
Let's say you can make $500 per hour consistently for every one of your hours. This is a hard thing to do. Lots of people earn $500 per hour for ad-hoc work - think of a graphic designer who bills for two hours after spending two hours securing a client or a lawyer's billable hours that don't take into account time spent on client interaction or business management. Unlike most, you're able to get paid $500 per hour for your entire 84 working hours every single week of the year.
So, per the above example, you'll make $42,000 per week
This comes out to $2.18 million per year
Clearly $2 million is a very large amount of money to be earning per year, but think of the fact that even with our truly absurd example where you're working like a machine and earning an extremely high hourly wage, you will still only earn about $20 million in 10 years or $100 million in 50 years. Yes, those are a large amount of money, but they are literally nothing when compared to what some top people in business and entrepreneurship make more than $100 million in a single year. Facebook founder and CEO Mark Zuckerberg, for example, has a current net worth that would equate to earning $4 million EVERY DAY OF HIS LIFE!
Clearly, the gains Zuckerberg and other extremely rich individuals have earned are not based on income - it would be impossible to sell their time to earn such gains. Instead, they have earned money selling other things such-such as ideas that are not restricted the same way time is. The highest paid salaried people are always making less than the highest paid entrepreneurs because the world is created in such a way that time is restricted while ideas are not - with ideas you can be earning multiple streams of income every second of every day or you might have windfall gains by creating immense value for millions (or even billions of people). It's far more difficult to do this at a so-called job.
The key takeaway here isn't that it's bad to have a job. The key takeaway should be that you have to lift your head up from the current place you’re at and see things in a broader, holistic, and realistic way. By understanding the inherent restriction, a job places on your ability to earn you might be better able to spot opportunities or even better understand the world.
Entrepreneurship isn't for everyone - many people will do better at a good job in a good firm. Additionally, although the above discussion was about money, money is not the most important thing in work and shouldn't even be the reason anyone forgoes a job to start a business on their own. There must be something else besides money motivating you if you are to have a chance at being successful in any endeavor.
The only thing we're trying to portray here is that business and entrepreneurship allows you to escape from the paradigm of selling your time for money - you can escape this paradigm and go beyond the limitations of time and space on value creation that a job places on you.
Caveats and Exceptions - There are Some Jobs That Will Make You Rich
As with almost anything that's generally true, there are some caveats and exceptions. Here, the main caveat is that there are in fact a handful of people in the world who do become truly wealthy through their jobs. These people include the likes of:
Additionally, one might argue that in the above absurd example it was unfair to bring in the likes of Mark Zuckerberg - there are plenty of entrepreneurs who earn less and even more who fail and don't earn much at all. This is all true, but the point did do a comparison of high paid jobs vs highly paid entrepreneurs. In that comparison what we attempted to illustrate this that in entrepreneurship there is inherently little restrictions on earnings - earnings can be so great that they become absurd (eg. $4 million a day for every day of Zuckerberg's life) while job earnings are restricted simply by the laws of nature and the laws of physics.
Most people in history created their livelihood -- either by creating income or by actually producing the necessities of life with their own hand and toil -- within family or communal units. The idea of working at a job for a larger entity such as a corporation is extremely new in the grand swath of human history. In effect, almost all of the people who ever lived could in effect be classified as small business owners - this is even true today as most US employment comes still from sole proprietorships or small businesses.
Why is it useful to understand the history of work/labor?
This idea is very important to people living in modern societies because we have a view within our minds that is quite different from reality. Many people believe that:
Going beyond the present day and having at least a basic conception of the things our ancestors did to create substance and value in their ancient worlds will assist in opening up your mind to new opportunities, new ways of combining life with work, and new ways of creating value for others.
Hunting and Gathering - The First Sole Proprietorships
For most of our history, we hunted meat and gathered fruits and vegetables to feed our families and our very tight-knit communities. The lifestyle involved simply waking up with the sun, looking for food during the day, and resting in the evening. Bedtime was when it became dark and no hunter-gatherer had to plan very far ahead.
The first really interesting thing to think about when thinking about how hunter-gatherers provided for themselves is how there were almost never any intermediaries. Besides the possibility of occasional trade within tight-knit communities, hunter-gatherers had what can be considered a two-step method to getting what they wanted. In terms of purity of execution, this was the most basic/fundamental way of obtaining food and water - a hunter gather would literally expend energy in order to obtain the final product he/she sought.
The second interesting thing arises from the first - hunter-gatherers didn't create value for other human beings in order to achieve their goals. Of course, a hunter-gather might want to provide for his family and create value in that pursuit, but that's not what we mean here. What we mean is that hunter-gatherers either went to pick edible growings or killed animals in order to obtain sustenance. In that pursuit they did not serve any other human being in any way - they simply went out into the world and obtained what they needed from it. Contrast that with today's world where we almost exclusively have to earn our livings by creating value for other people, be they your employees or your customers (which are also your employers in a sense). We're not making a normative statement here - we're simply making a descriptive statement.
The third very interesting thing about thinking of the working hunter-gatherers performed is that they had a direct understanding of how their efforts and skills translated into the final product they obtained. Of course, hunter-gatherers likely had some sort of quasi-religious beliefs where they imbued objects, the weather, etc. with spiritualistic aspects and they might have relied on them to provide. However, that doesn't detract from the simple physics of hunting and gathering - every hunter-gatherer must have understood how it was their own physical efforts out in the world that were the proximate cause of their gain. They could have thought the ultimate cause came from the skies or from the tree spirits or elsewhere, but they surely understood that the proximate cause was their own effort - they surely understood that without themselves leaving their cave, picking growing, or killing an animal and dragging it home, their families would not have food to eat. Contrast that with today's modern corporate worker who works in a corporate office or campus and who has
These complex factors can include things such as
Yes, a person's well-being still depends on themselves and everyone must take responsibility for their lives - you must work hard and well so that you're able to do well in your job and in life. However, it is abundantly clear that the level of mental control that a person feels over his or her method of meeting wants/needs should have been far greater in the past than in today's complex and interconnected environment where so much of the economy is not visible or understandable by a single individual.
This understandability of relationship between soil and result could be psychologically beneficial to human beings on many levels. This isn't a psychology website and we're not purporting to have any theoretical or empirical underpinning for these statements, but it does seem to make sense that an individual who has a clear "a leads to b" understanding of the relationship between toil and result -- as opposed of "a to b to c to d to a BLACK BOX to e to f to g" understanding -- would have greater psychological comfort and less psychological stress.
In no way is above supposed to make you envy a hunter-gatherer - we live in a far richer world (both physically and mentally) than our ancestors and anyone who would want to give up today's peace, today's luxury, and today's comfort for a hungry dangerous life of basic subsistence and survival is a quite unusual person.
Agricultural Revolution and Farming
After many centuries of foraging, humans ended up farming. This happened gradually over the course of centuries as well, but the end result was the literal transformation of human life from a nomadic existence to a settled life that would be far more familiar to the modern person.
Although life transformed as well as the approach fro providing for it, humans still operated at a family or communal level - humans still remained in effect small business owners. The business changed, of course humans went from hunting and gathering to
Humans mainly operated as family units after the agricultural revolution according to current historical data with larger family-based communities existing for things that went beyond the family. In effect, each household ran a small farming business that employed the entire household from a relatively young age by today's standards.
Here people had a bit more complexity - their toil no longer immediately translated into value creation (eg. food to eat) but had to go through the intermediate step of waiting for the seeds to grow into plants. The same is true for livestock - farmers and heard had to wait for livestock to grow and spend time and energy on breeding instead of just going out into the wild to kill game.
We can see that from hunting and gathering to farming -- things which make up by far the vast majority of human existence -- we operated in very small-scale communities and were in effect creating our livelihoods within our family units. In effect, all hunter-gatherers and farmers until the Industrial Revolution turned farming into big business can be classified as small business owners in the very broad sense of the world. These individuals worked primarily for themselves and their families. Farmers in certain eras might have had to pay taxes to lords or barons or other elites, but these can be thought of as quasi-taxes. Almost all of humanity did not know the meaning of providing your labor (either in the form of physical or mental exertion) to another individual in return for some sort of payment - this was the case for many reasons, one of which was an economy that was so poor that it could not sustain such interactions in a meaningful way.
Artisans and Craftsmen - Sole Proprietors Throughout History
Beyond farming, there have been at times in history a class or artisans or craftsman. This class developed after the Agricultural Revolution as settled communities were needed in order for this class of people to arise. They mainly operated in larger cities and they ran what can be considered small businesses. The words "artisan" and "craftsman" is too narrow, however, as these individuals operated a large variety of business. These businesses including:
All of the above can also be classified as small businesses. They are more like the small businesses we think of today - instead of directly producing their own livelihoods, these artisans and craftsmen would set up shop and serve their communities. They would very likely have most of their family involved in the business and live either close by or directly above their shops.
The Modern Working World
Although the majority of US jobs still come from small businesses, most people think of work as something you do in a large-scale setting such as a corporation. Most people even aspire to such work.
This work is quite different than operating a small business because it involves providing your labor to a larger entity that you do not control and likely can never fully understand (not even the CEO of a large firm fully understand what's really going on). This creates a sort of "black box" effect where you provide your labor into a "black box" and then some income is given to you. You aren't totally sure about the actual value you're creating for the firm and you don't fully understand how your labor fits into the bigger puzzle.
There are of course many benefits working in jobs - most of these benefits come from a certain stability that is not always present in running a small business. However, there might be some psychological costs that affect a person in the following ways:
Working in a job might make a person blind to other small but very profitable opportunities where their skills might be used. They might not ever consider opening their own business, running their own website, consulting on their own, or providing value on a small scale. This is unfortunate because it is in such small setting where you are able to capture the full value of your efforts (instead of the employer capturing most of the value). This is really how people get rich today - most people will never get rich working for a job and saving a large portion of their income; the vast majority of people in our world get rich in entrepreneurial activities.
Some Examples of Employment Throughout History
Although most people worked for themselves throughout history, there were some interesting examples of employment throughout history. Here are a few:
Data provides intelligence. It doesn't equal intelligence, but through the proper application of analysis, data can be turned into intelligence. Your business has data - whether you use that data now or not to create business intelligence, that data (assuming it is of reasonable quality) is quite valuable.
Data such as:
is crucial to your business. Even if you haven't implemented proper methods for turning that data into business intelligence, you can't afford to let your data go away.
If you're letting the life of your data ride on the functioning of a mechanical hard drive, you are making a big mistake. Big businesses understand the importance of data and invest time and energy in order to preserve it - it's time small and medium sized businesses and managers did the same. It's time that small and medium-sized business owners and managers took a few easy and important steps to add quite a bit of resilience to their business.
The first part of creating a robust data resilience strategy is local storage - you have to have a local backup of your data. For most small businesses, data usually resides on one or a handful of computers. For medium sized businesses, data can reside on multiple computers and mobile devices. Wherever your data resides, you must have a robust local storage system set up to back up your data - although this will take a bit more time and effort for medium sized businesses.
For small businesses, there are two options here:
Either one of the above methods would work and should be looked at in light of:
Now, for medium sized businesses, things can get a bit more tricky. If a medium sized business doesn't thave many computers, they can approach it from the same way we outlined above. If, however, a medium sized business had data stored on multiple computers or mobile devices, a more thought-out strategy will save money and decrease headaches over the long-term. Hiring a consultant to assist with setting up a high-quality backup system might be a good investment here.
Whatever local storage method you choose, it is key to make sure that the physical storage is safe and secure both physically and electronically:
In addition to local backup, cloud backup is key - local storage is exposed to various operational risks such as flooding, fire, theft, misplacement, mechanical failure (for typical spinning hard disks), or electronic failure (for solid state devices). To guard against the risk fo loss of locally stored data, a cloud backup system can be used.
There are many solutions tailored to both small and medium-sized businesses - we won't' go into them here but it's important to focus on a business solution and not on consumer-level solutions here. Additionally, free solutions should likely be avoided - "if you're not the customer, you're the product" is a relevant saying here that should deter you from storing valuable data in a free cloud storage solution where the provider fo the storage has little or no obligation to you or your business.
As with the local storage, you can choose whether you want a continuous backup of the entire system of ad-hoc backups of only relevant files and folders. Again, this depends on the same factors discussed above.
When choosing a provider of cloud storage, a few key things to look at are:
Supplementary Computing Systems
In additional to having your data backed up, you'll want to invest in a supplementary system to both access the data and operate your normal business software (be it MS Excel, MS Word, just accessing the internet, using some sort of CRM software, or using propriety software for your business) - an operational risk even might affect your computers and you'll want to be able to get up and running quickly again.
You'll need to access the level of your and your business's reliance on computing and software in order to determine how much to invest in supplementary computing. A business that jus needs access to the internet and email might be fine with a simple additional laptop or no supplementary system at all. A business that relies on persistent CRM software and propriety software to run the business might need to invest in maintaining a secondary computing system with up to date software and an ability to connect to a network quickly (eg. a wireless chip that can get online without needing a physical connection).
Additionally, supplementary batteries might be useful here depending on the business's reliance on mobile battery operated devices and the risk that a power outage poses.
Resilience is key to succeeding in nature, succeeding in life, and succeeding in business - we'll cover resilience in business here.
The world has gone a bit soft over the last few decades and discussing resilience in business might even seem strange to individuals who aren't used to the term. Small and medium sized business owners bad managers would find it useful to know, however, that the biggest corporations in the world are deeply concerned about understanding and developing resiliency - it's time that small and medium sized businesses caught up.
Now, we'll do the customary dictionary definition of the term to start - resilient means:
So, from the definition we can see that there are a few things to think about when thinking about resilience. Resilience is more than just strength - ti si the ability to recover after suffering some sort of stress. Resilience is the ability to bounce back from misfortune or adjust to it in an easy way.
How can this general definition translate to business? Well, it should be easy to understand that a business will likely suffer all sorts of setback over time. These setbacks can include things such as:
Now, when these occur, the business that is resilient stands a greater chance of surviving these unpleasant events. A business that is capable of withstanding shocks is far more likely to be able to survive the inevitable shocks that life and business send our way from time to time.
Let's dig a bit deeper into what resilience really means for businesses. Specifically, what characteristics of a business allow it to be able to withstand those shocks we discussed above? Business resilience arises from a few factors:
No matter what business you run - whether it be a small ice cream shop or a small bank - resilience planning will provide you with a variety of benefits. The primary benefit is the ability to keep the business running as a going concern in the event of adverse or severely adverse circumstances. In addition to this primary benefit, however, you and your business managers will gain a lot of peace of mind knowing that your business is resilient nad likely able to survive as a going concern in even severely adverse operating conditions.
The Average Transaction (AT) is the fundamental building block to having an understanding of your business - if you don't currently know the Average Transaction (AT) for the business you own or manage, your level of business intelligence is severely lacking.
In today's works of easy record keeping, storage, and plenty of computerized analytic powers, there is no excuse to not be keenly aware of such basic and fundamental metrics such as your business's AT.
Average Transaction (AT) simply represents the mean transaction over a given period of time. Stayed more appropriately to business, AT is the expected transaction - it is the revenue you can "expect" (in the statistical sense of the term) to receive from the next individual or organization that you do business with.
Calculating Your Average Transaction (AT)
Calculating your AT is quite simple - you simply take the arithmetic average of all your transactions:
AT = (sum of transactions)/(# of transactions)
If the formula sounds very simple, it's because it is - you hopefully already know your business's AT. Of course, there are a few important things to keep in mind in order to make sure your AT is accurate and useful.
There are two points to be made regarding timeframe. The first is easy and hopefully obvious - you must use the same timeframe for both parts of the formula. So, if you sum the transaction over 2016, you need to divide by the number of transactions in 2016. Imagine you didn't follow this rule and instead only used 6 months worth of transaction for the top part of the formula (for the sum of the transactions). What would happen? It should be clear that you would significantly understate your AT (it works like be one-half) because you're not taking the full year's worth of transaction into account. If you only had 6 months worth of transaction, you would need to divide by the number of transactions you had in that year in order to calculate your AT properly.
The second point on the timeframe is that it's better to use a full year of data instead of just a few months. A full year of data (if your business is in a stable state) will allow the kinks and gyrations caused by changing seasons, holidays, etc. to be evened out - a full year of data will allow the full spectrum of things that occur in a year to be captured within your data.
Focus on Transaction, NOT Customers
A key part of calculation your Average Transaction (AT) is to make sure you're using transaction and not customers - it's called Average TRANSACTION after all. The distinction is key because focusing on a transaction will allow granularizing your metrics down the line - you'll be able to not just calculate AT, but you'll be able to calculate multiple ATs for different types of transactions (eg. those arising from Google, those arising from referrals, etc.). More on this is covered below, but let's look at an example to really understand the difference between using transactions instead of customers.
Imagine you have a customer that comes in once every month for a year. You'll want to count each of the 12 transactions separately instead of counting the customer as a whole. So, you'll sum each transaction and divide by 12. If you have 10 such customers, you'll sum each transaction and divide by 120 (10 x 12) because there are 120 total transactions for the year.
The benefit of doing this for transactions instead of customers can be explained by doing a thought experiment. Which would you rather have when a customer comes into your business to execute a transaction:
Clearly, the first one allows you to predict what will happen in the immediate future and put things to a close. The second one, however, only allows you to make a prediction about the overall general future - you really won't know what's going to happen today. What this example illustrates is that it's quite useful to be able to predict what's going to happen today instead of-of having today only fit into a larger long-term prediction.
You can break down your AT even further to determine you AT for various types of transactions - transactions arising from Google, from Facebook, from referrals, etc.
This gradual AT is useful because it will allow you to understand where your most valuable transactions come from so that you can channel more money into those pipelines and away from less-profitable transactions.
Referrals Per Customer (RPC) is the correlated rate of referrals per customers over a given period of time. Stated more simply, Referrals Per Customer tells you "how much of an additional customer" each customer brings in.
Understanding that both the above definitions still might be a bit opaque and obscure to business owners and managers, let's go a bit deeper with an example. An Referrals Per Customer (RPC) rate of 0.25 means that for each unique customer over a given period of time, 0.25 (or one-quarter) of an additional customer is going to come into your business - which means that for every 4 customers, you can expect one additional customer to come in via a referral.
We measure the Referrals Per Customer (RPC) rate in terms of a single customer because it will be easier to use downstream - although it might be easier to say "you get 1 referral for every 4 customers" saying instead that "each customer brings in an additional 1/4 of a customer" is the best ay to approach and to understand RPC because it will allow you to apply an understanding of RPC to each customer and because it will be easier to use the RPC concept downstream in the calculation of things such as the Lifetime Customer Value (LCV).
To calculate your business's Referrals Per Customer (RPC) metric you simply need two numbers:
Using these two numbers, you can simply divide the number of referrals by the number of non-referrals to get your RPC metric. For example, if in 2016 you had 800 non-referral customers and 200 referrals, you would simply divide 200 by 800 to get 1/4 OR 0.25 - your RPC would be 0.25, meaning it's as if each customer brings in an additional one-quarter of a customer with him/her every time they come in.
Now that we've given you a brief overview, we'll discuss why this is an important thing to know, then we'll dive into some important conceptual pieces of Referrals Per Customer (RPC) and then follow up with an example of how to implement this new and valuable understanding.
Why should you care about RPC?
Any small or medium size business owner or manager worth anything will understand the importance of referrals. From antiquity to the most modern businesses around the world today, referrals are a critical part of growing any businesses sales base - this understanding is so fundamental that it almost needs no explanation.
Humans, being social creatures, value the opinions of other humans they trust and respect. Humans intuitively understand that a referral from a respected individual is a valuable thing because it both
If referrals are so important to businesses, and if most businesses understand this, why is so little effort put into properly understanding referrals by small and medium-sized business owners and managers? In conversations with small and medium sized business owner sand managers, this usually occurs because a misconception that it is either costly or difficult to go beyond the basic "please refer us" statement to understand the nature of particular business's referrals.
If the nature of referrals can be properly understood, however, various benefits will immediately flow to the business owner or manager. These benefits include:
Correlation vs. Causation
Now that we've covered the basics, we'll dive deeper into RPC in order to flush out some of the important details and get a good understand fo the concepts and it's potential weaknesses. First, we'll note that the way we calculate RPC is a bit flawed - RPC looks at how referrals are correlated with overall customer volume and NOT at the actual amount of referrals that a certain number of customers bring.
What this flaw means is better illustrated via a generic expamle using the same numbers we used in the brief example above. Let's say you have an ice cream shop and 800 new customer visit in 2016 with 200 referrals. Per our RPC calculation, you would look at be looking only at numbers in 2016. That means a referral could have come in on the very morning of January 1, 2016, but you would still count it as part of your RPC. This doesn't make sense because clearly, no customer in 2016 referred that customer - it was almost surely someone in 2015. So, you're not really looking at the causes of the referrals, but only at how your referrals are correlated with (eg. compare with) your non-referrals.
This is a flaw, but it should remain a minor flaw for the vast majority of businesses. You should be aware of it, but that is all - you can safely assume the flaw away because the error that will be introduced will be very small and due to the fact that the greatest error occurs in the first year. In subsequent years, although the very small error will persist within each year, the error will be normalized away via a comparison of years with each other - 2016 and 2017 could be compared with each other and both will have that error in it.
Count Customers, NOT Transactions
It is important when calculating your RPC metric, as stated above, to use customers and not transactions - customers might engage in multiple transactions but you only want to track the individual customers in order to accurately calculate RPC.
It's easy to see why we want to focus on customers and not transactions. Imagine a customer who refers one friend but comes to your coffee shop every single day for a year. Intuitively, how do we understand the relationship between the customer and the referrals that come from him/her? We clearly would say that the one customer refers one person - we wouldn't say 365 customers refer one person. If we count transactions, we would in effect be saying that it takes 365 of this customer to get a preferred customer - a meaningless and inaccurate statement. Clearly, we can see that it only took us one customer to get that referral - the more accurate approach is to count only customers.
Does the same apply to referrals? Do we count transactions or customers when counting the number of referrals? Clearly, we also count the number of customers - counting transactions would possibly overstate a number of referrals and thereby overstate the RPC metric incorrectly. Again, image one customer refers another and that referred customer comes into your coffees spot every day for a year. Would it be more appropriate to say that one customer was referred or would we say that 365 customers were referred? Clearly, it is more meaningful and correct to note that one customer refers another, not that one customer referred 365 customers.
Digging Deeper into the Calculation
We touched on the actual calculation above, but let's dig a bit deeper into it in order to really flush out the details. As we said above, there are only two things you'll need in order to calculate Referrals Per Customer (RPC):
Then you simply divided:
(# referrals)/(# non-referrals) = RPC
Make sure to keep in mind that you're dividng referrals by non-referrals (not the other way around). Additionally, it is key that the two numbers your dividing are for the same time period - if you use different time periods for counting the number of referrals and non-referrals, your RPC will inaccurate and incorrect.
Hopefully, you already have the data to be able to get the above numbers. However, if you don't, you'll have to set up a system for collecting customer data and wait a bit (at least 3 months) before you do the calculation. You'll want to wait so that the data is sufficiently representative of what's going on and so the short-term kinks and gyrations are evened out. One year is an even better timeframe - if you start at a shorter timeframe, move to a longer one as more data becomes available. One year is particularly excellent because for most businesses it will allow for a full yearly business cycle (eg. holidays, special sales, varying weather, etc.) to be represented within the dataset you are using.
It's not difficult to start collecting the necessary data to calculate RPC if you currently don't have it - you really only need to tag each customer with whether or not they are a referral and be able to separate out customers from transctions. Separating referrals vs. non-referrals is relatively easy - you or an associate can simply ask at the time of purchase verbally or via a registration form if your business uses them. Making sure transction are separate from unique customers will be a bit more complicated, but is still relatively simple - you'll need to someone keep track of your customers (eg. an MS Excel file) and be able to search within your customer list (eg. Ctrl-F within MS Excel) for the customer when a new transaction occurs. This MS Excel - Ctrl-F is the most basic and primitive approach - far more sophisticated and elegant approaches are possible using both MS Excel or a piece of Customer Relationship Management (CRM) software.
Benefits of Knowing Your RPC
Once you know your businesses RPC, you'll have a far better picture of how referrals factor into your business. You will literally be able to understand what percentage of customers are referrals and, thereby, understand what each customer (on average) brings into your business in terms of referrals.
You'll effectively be able to both understand and quantify the additional benefit that is derived from each customer above just the transaction - you'll knw that the transaction amount is only one part of the gain your business receives from each customer. By knowing this, you'll be able to better evaluate marketing - both towards new customers and to existing customers. You'll also be able to better evaluate different approaches to growing sales and revenue - a common dilemma many business owners and managers face is whether to market towards new customers or to focus on getting more referrals.
Additionally, by knowing your RPC, you'll now be able to track your RPC over time - this is incredibly valuable and will allow you to monitor the performance of different strategies and tactics. For example, if you implement a referral bonus where customers get a certain discount for each referral, you'll actually know how effective that program was. You might think that you would already know how effective that program was without knowing you RPC - wouldn't tracking sales and revenue be sufficient? The answer is NO - revenue clearly depends on many thigns (eg. season, tastes, unemployment rate, economic growth, randomness, better salespeople, etc.). By knowing your RPC, you'll be easily able to measure one time period's RPC against another and really know how a new strategy affected the level of referrals derived from each customer.
Most importantly, you'll be able to use your RPC metric in important downstream uses that will further create business intelligence for you - critically useful metrics such as Lifetime Customer Value (LCV) rely on the RCP metric as an input.
Lifetime Customer Value (LCV) is the present value of all gains derived from a customer relationship. LCV is a key metric that big businesses understand (for the most part) and attempt to use in their decision-making process. However, too many small and medium-sized business owners and managers fail to either understand this concept or implement it in their decision making.
Here we'll briefly take you through what Lifetime Customer Value (LCV) means and then walk you through a basic step-by-step guide on how the metric is derived so that you can fully understand how deeply profound and eye-opening the concept can be.
As stated above, Lifetime Customer Value is the present value fo all gains derived from a customer relationship. This sounds simple, but it's not quite as simple as you might think. To really understand what LCV mean's let's break the definition down into its sub-components:
1. Gains: Gains are almost always monetary in final terms, but we say gains instead of money because a lot of the time the final monetary gain comes after multiple non-monetary steps. A simple example would be customer referrals - the referral is a non-monetary gain but a real gain nonetheless because it will end up bringing revenue into your business.
However, often times gains aren't as easy as intermediary steps leading to revenue. Sometimes gains occur due to cost reductions or complex non-monetary benefits. For example, if you're attempting to get your local city government to allow you to put a certain piece of signage up, having more customers come to your business might put some sort of political pressure to get this done. Another example might be the economies of scale that can be achieved by having more customers. All of these complex non-monetary gains must at some point translate into real monetary gains or else they shouldn't be included. Even things such as goodwill, reputation, lax regulatory frameworks, etc. allow for monetary gains down the road.
Now, these complex non-monetary gains are hard to understand and even more difficult to value in terms of dollars. For the vast majority of businesses, it is better to not include them. They will almost surely represent a small portion of your LCV and attempting to introduce them into the LCV calculation will only waste precious resources and potentially cause more harm (in terms of errors) than benefits (in terms of increased accuracy).
So, why would we even mention them if we're not advising including them in the LCV calculus? We mention them because the importance of LCV is beyond the actual number - the profundity of understanding LCV is that you will have a better conception of the nature of your business and your outlook will expand into the longer term. By understand the more nuanced benefits that might accrue to your business (whatever they may be) your overall view of your business - even your underlying emotional and philosophical approach to it - will benefit. Understanding LCV after not knowing it at all is like lifting your head up - while before you were looking at the ground immediate in front of your feet, now you see the entire boulevard ahead of you.
Finally, the gains must be monetary in their final form because we will need to discount them in order to have an accurate LCV. It's almost impossible to discount non-monetary benefits.
2. Customer Relationship: The gains have to come from a customer relationship - meaning someone who has given your business money in exchange for the products or services your business provides.
Of course gains can and likely will come from non-customers - people might refer your business without being a customer because your business isn't selling what they need (eg. a man telling his girlfriends about a new hair salon he's heard or a woman telling her pregnant friend about a store that sells clothes for expecting mothers). However, it is too difficult to capture enough data to be able to effectively understand how much value such people bring. Additionally, the majority of customer value is derived from the actual transactions that take place - that's the heart of LCV and that should form the base of your LCV conclusion.
By using the terms Customer Relationship we are also implying that it's not the immediate interact that is of sole importance - the overall long-term relationship with a customer is key. The main thing to think about here is repeat customers - most businesses have customers coming back two, three, or multiple times. All of these interactions subsequent to the first transaction are clearly part of the value your customer brings your business and should be included in LCV. Of course, subsequent interactions should be discounted (this is addressed below) because money tomorrow is not worth the same as money today (a fundamental principle of economics and finance).
3. Present Value: We've hinted at this above, but it's key to discount your gains in order to properly understand your LCV. For example, if you use your data to see that repeat customers come in every 5 months for repurchases, the payment 30 months from now (the sixth purchase) shouldn't count as much as a payment today dollar for dollar. As in finance and economics, future payments (future gains) must be discounted by the appropriate discount rate in order to come up with the present value of the gain.
Of course, different payments at different times need to be discounted differently - a payment in 6 mo needs to be discounted separately and at a different rate than a payment 6 years from now.
Now, discounting and calculating Net Present Value (NPV) is beyond the scope of this article, but it should be noted that you must discount at the appropriate discount rate - a rate that reflects the inherent riskiness/uncertainty of the future cash flows and the current risk-free rate for that time interval. For example, if a payment is to occur one year from now, you should probably use a discount rate higher than current savings accounts are paying (because that's a rate of a very low-risk cash flow) but something lower than an extremely risky loan (because you're more sure based on your data that the cash flow will come in). This is more complicated than this discussion, however, and any business owner or manager would serve himself/herself as well as the enterprise they are running or managing by taking some time to understand the basics of discounting.
So, we have our recipe for LCV per the above:
- we take all of the monetary gains that will arise from a customer (except those that are complex and difficult to monetize)
- we discount those gains at the apportion discount rate(s)
Going a bit deeper, the most important gain (besides actual money from transactions) is refferals - this accounts for the vast majority of non-transaction gains that most businesses will receive from customers. So, we can simply our formula (while still understanding the broader context from which we are simplifying) to just include referrals - we can literally ignore almost everything else and still come up with a fairly accurate (albeit conservative) LCV. This LCV will be conservative because we're excluding certain gains - it's better to err on the side of conservatism here rather than optimism.
So, we have:
Gains= 1st Transaction + Subsequent Transactions + Refferal Value
But, what exactly does referral value mean? It's not immediately easy to calculate referral value because:
- not every customer will refer people
- not every referral will become a customer
- each customer will usually have a different transaction amount
So, you'll need a bit of customer data to get your Referral Value. You'll need to track how many of your customers are referrals - something many businesses do already. If you don't do this, start it - it's a fundamental part of understanding your business. But just knowing which customers are referrals isn't enough - you need to know how much referrals spend.
You can assume that referrals spend the same amount as the rest of your customers and simply imply onto them the Average Transaction Value (ATV) of your business overall. This is not ideal and can be improved upon with just a little bit of effort. You'll want also get the transaction value of referrals - this is simply done by just recording one other piece of data.
So, now you'll have the number of referrals and the average transaction of refferals. You'll want to use recent data but you'll want to make sure it's a large enough data set - maybe 6 months worth of data at aa minimum. Using that data, you can see how many referrals come in a period of time (let's use a year as an example) and then understand how your referrals relate to your overall customer volume.
Using our 1-year example, say this is what your data shows:
- 1000 customer for an entire year
- 200 referrals
That means 800 (1000 - 200) customers were on-referral. So, 800 on-referrals correlate to 200 referrals for your business. That means:
- every 4 customers is correlated to 1 federal, OR
- every customer is correlated to 1/4 of a referral
Now, you can simply at 0.25*(Average Referral Transaction) to each customer - you now know that a person coming in brings in his money for his purchase PLUS 1/4 of another referral that brings in the amount you calculated for the average referral transaction.
Putting it all together we have,
Gain = Initial Transaction + Subsequent Transactions + Referral Value
To get your LCV, we simply discount this appropriately - a more complicated discussion left for another time.
If you own or run a small or medium sized business and you don't currently track customer, lead, or inquiry data, starting to do so could be magic for your business.
Keeping quality data on customers (and on leads or inquiries - people who contact your business but are yet to take the next step) could prove incredibly powerful in terms of gaining insights about your business and future marketing efforts.
I've spoken to many small business owners both in a professional and social context and I often ask them this question:
"How would you feel if you had the email of every single one of your previous customers available to you right now?"
They usually respond by saying that it would be amazing to have that - the longer they've been in business the more amazing it is. Once small business owner in California that's been around for over a decade remarked that having that sort of data would be like gold - it would allow for excellent marketing opportunities since tens of thousands of happy high-transaction customers have walked through the business's doors over the course of more than 10 years.
Beyond just emails, knowing very simple things like the Zip codes of your customers would add a level of intelligence to your marketing and advertising efforts without which business owners are using primitive methods devoid of any sort of business intelligence that is so easy to acquire in today's environment. Without knowing anything a business owner is shooting in the dark in terms of marketing and advertising. By knowing just the Zip codes of previous customers, marketing strategies could be more finely tuned in order to get better returns on advertising spend. Google, Facebook, and even print ads are all advertising mediums that easily lend to the use of such information.
Any small or medium sized business owner or manager who currently keeps poor data has the ability to transform things today with the use of a computer and a bit of time. The great thing is that it's very easy to start. Although most business owners and managers who haven't started this already have likely procrastinated because they thinks it's too difficult or too complicated for them or their business to implement, that's just false - it's incredibly easy to start for anyone with a computer and a licensed copy of Microsoft Excel.
We recommend using MS Excel (not Google Docs or Apple's Numbers) because Excel is at once easy to use and powerful. Excel is robust enough to handle large sets of data and will allow for various sorts of analysis and manipulation in the future - it's basically the gold standard in terms of entry-level data suites and going for a more simple or easy-to-use suite might be a mistake here.
Here is an example we created to demonstrate how easy it is to start collecting useful data (see image below). We created various simple fields that will be applicable to all businesses:
Date: So you know when the record was taken
Privacy and Compliance
Always make sure to ask for data - don't just input it yourself. Additionally, let the customer know that you're storing it and that you intend to keep it secure and private. Make sure you actually do so by using robust passwords and access protection methods, storing hard drives in safes or secure/locked locations, and refrain from transmitting the data via unsecured connections or to parties who are unaffiliated with your business.
The best rule of thumb is to treat the data as if it was your own or your families' information.
Example MS Excel sheet for collecting data that can be set up in 30 min. or less
If you're running any sort of business, you know that customers (in whatever form they may come for you) are basically the lifeblood of your organization. Big businesses understand this and act on this understanding - most large businesses in the developed world spend a fair amount of money and energy obtaining valuable lead and customer data. Small and medium-sized businesses, however, too often forgot the important task of collecting high-quality customer and lead data.
Whether you're running a mid-size organization or are self-employed running a business from your home, if you're dealing with customers or interacting with potential customers, then you need to keep good track of related data.
Why do so many businesses fail to do this?
Many small business owners and mid-size business owners and managers don't prioritize customer and lead tracking for a variety of reasons. These reasons may include:
- literally not knowing how important it is - they simply might not understand the long-term value that high-quality tracking of customer and lead data can bring down the pipeline in terms of increased sales (from both repeat customers and referrals), increased understanding of the business, or improved processes/services
- they may not think they are capable of it - they might be intimated and think that special knowledge, special staff, special/complex processes, or special/costly software would necessary to properly carry out high-quality customer/lead tracking
What does good customer/lead tracking and data obtainment look like?
Good customer/lead tracking and data obtainment means that you obtain data from the various touchpoints leads and customers have with your business. The words may sound a bit complicated, but the message is very simple - keep track of the important stuff related to your interactions with people in your business. Businesses interact with customers in many ways and at many stages of the sales process. These can include the following:
- website clicks
- emails/phone calls/messaging online
- customer service issues
- repeat visits
Good customer/lead data tracking means:
1. Capturing relevant, high quality, and accurate data at each one of these touchpoints
By relevant, we mean things that are actually important to your business. This shouldn't be hard for a business owner or a knowledgeable business manager to accomplish. As a very basic example, if you're running an ice cream shop you might want to know things like the age of the customer, their zip code, and their email, but you probably wouldn't care about their height or their hair color because those wouldn't assist in you making useful predictions for the future or assist you better understanding your customers from a business-related perspective. Knowing their zip code will improve your advertising methods and strategies, but knowing their hair color will be pretty much irrelevant in anything you might want to do related to the business.
By high quality we mean that the data should be easily understandable (eg. simple descriptive terms instead of irrelevant and complicated numbers/letters), sufficiently descriptive so as to not mix it up (eg. if you're selling things to students, tracking actual schools is better than just tracking whether it's elementary or middle or high school or college which is better than tracking whether the student is in K-12 or college - the increased granularity will be beneficial but can still allow for aggregation if needed - eg. combining all elementary schools to see how many customers come from elementary schools)
By accurate we literally mean accurate - they key is to make sure the data your recording isn't garage - it's "garbage in, garbage out" with data. It's better to have no data at all than to have inaccurate data. To obtain accurate data you must build rapport with customers so that they give you real information, not random information just to get you off their back.
2. Storing the customer/lead data in easy to access ways
Storing it on a purpose-built system is better than in a nice and clean Excel file which is still better than having data scattered in many different files and formats which still better than having a ton of data on paper forms in a filing cabinet. You want to quickly transfer data to digital formats because that's really the way to back it up, monitor access to protect customer privacy and business knowledge and to be able to manipulate and analyze it. Remember to always be mindful of customer privacy.
3. Using this data in light of the business knowledge to go from simple data to actual business intelligence - data itself is useless but data combined with insight and analysis can create intelligence
Why is it important to track customer/lead data?
By obtaining good quality data (and turning that data into high-quality intelligence), you can learn so much about your business. You can learn things like:
- where most of your customers come from in terms of location (eg. which zip code do they come from, which city do they come from, etc.)
- how much your customers spend on average
- how many times your customers return
- lifetime customer value (LCV)
- the cost of customer acquisition (CCA)
- general market sentiment pertaining to your business
- revenue drivers for your business
- how you get your customers (eg. Google, Yelp, Facebook, referrals, etc.)
- conversion rates
- return rates
- things that can improve your overall processes/systems
Now, it's easy to argue for the importance of tracking data - but it's a bit difficult to do ti properly. the key is to set up a consistent and quality data obtainment system soon - don't wait until everything is perfect. A lot of business owners and managers procrastinate on this - they feel they don't have the time, the size, the money, etc. to do this now and they postpone it into the future until some "right time" comes along. This is ludicrous on many levels.
It's totally inappropriate to postpone the implementation of data obtainments systems even if you know that you'll have to update and improve them over time. The main reason it's not acceptable is that you're wasting valuable data. Data is food for your business knowledge - without it, you can't have a lot of business intelligence. If you're interacting with leads, potential customers, or actual customers, you are capable of immediate obtaining at least some data. This data can be used relatively quickly (once you've gathered enough) provide you and your business with some insights. You can always improve the entire process later, but the data you gather today (as long as it meets the 3 criteria above) will be able to still be used in the future. For example, if you only gather emails and zip codes today but implement a much better data gathering process in the future that includes addresses, customer acquisition methods, and transactions, you can still use the data you have - you can use it now and you can use it to add to your larger/better future data. In essence, this is not something you have to get right on the first try - an iterative approach that adds and builds works very well as long as you make sure to follow the general guidelines we discussed above.
We live in a world of massive amounts of data. You've likely heard the term "Big Data" many times before, but it's far beyond this and you probably don't have a full grasp of how amazing our modern and connected world (mainly the developed 1st world) is today.
In the year 248 AD, Rome celebrated its 1000th anniversary - it had been 1000 years since the founding of Rome. More data is created in one year today than was created in those 1000 years of the Roman Republic and Empire. This is an astonishing fact that should bring a sense of awe to every intelligent and curious person - humanity is creating absolutely vast amounts of all kinds of data today.
What kind of data?
Here are just a few examples of the kinds of data creation that take place every day - that takes place very second every second:
The list above is meant to be broad in order to demonstrate the broad swath of things from which data is created today. Data can be created by governments or big corporations, but data can just as easily be created by small businesses and individuals during their everyday tasks and processes.
The above list is just a tiny example - almost anything remotely automated or electronic creates some sort of data today.
A Key Question
If we have an exponentially larger amount of data today than in the past, why aren't we exponentially smarter today as a society? Sure, the size of our economy as measured by GDP or GNP is much larger than at any point in history, but we can still see that we haven't moved that far way from past societies and civilizations in terms of the things that are most important to humanity.
Going further, why aren't businesses incredibly smart if we have so many data available? So many small and medium sized businesses today still operate under the same paradigms as businesses of the past. The problem is that even though there are tremendous amounts of data (and easy ways to collect more), the data isn't being productively used. The data is just sitting there. It's easy to collect data - it's hard to use it effectively.
What you really need isn't data - it's intelligence. You don't need a data dump on your hard drive or a stream of data flowing in at many GBs a second - you need to know how to turn whatever data you do have (hopefully it's quality data) into intelligence. This is what the human mind does - it turns raw data from sensory inputs into intelligence via the brain.
To better illustrate the importance of intelligence and the inadequacy fo data alone, let's imagine a fictional scenario. Imagine giving an ancient hunter-gatherer tribe all of the data available today on a giant supercomputer. Of course, they won't be able to access that data, but let's ignore that for a second and imagine that SOMEHOW that ancient tribe could in fact access all of this vast data. Do you think that things would really change for that tribe? It is likely that the tribe would be incapable of utilizing the data in any way and creating any actionable intelligence from it - they wouldn't have either the mathematical/statistical sophistication to extract much meaning from it and they wouldn't have the background landscape required to absorb and process the data in appropriate and meaningful contexts.
The Definition of Data
Merriam-Webster's dictionary defines data as follows:
1. factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation <the data is plentiful and easily available — H. A. Gleason, Jr.> <comprehensive data on economic growth have been published — N. H. Jacoby>
2. information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful
3. information in numerical form that can be digitally transmitted or processed
We define data in simpler terms:
Data are discreet units of information that provide some evidence of something in the real world
Data isn't something complicated. Although we might take a technological slant in our mind when thinking about data today, data can come in many forms. Data can be written on a stone tablet, on a piece of papyrus, on a piece of paper, or by tying knots using a string to keep track of things. Data can come in magnetic form as on credit cards. Data can come from CDs and DVDs or data can be stored on a flash drive. Data isn't technological - data is just information but technology has helped us gather and store vast amounts of it.
One of the key features of data is that it gives us some sort of information about the real world. This is due to the fact that data arises from the real world. The only way data can be created is by somehow recording some aspect of the outside world in some sort of storage mechanism. That mechanism might be robust or it might be fragile, it might be high advanced or primitive, but it has to (at least for a time) store some sort of information that is somehow derived from the real world.
Data that has no basis in or relationship to the real world is utterly useless for the purposes of using it to create value and making more effective decisions. Imagine a set of data that is just made up randomly - a random list of customer data that includes totally made up random numbers for purchase amounts, transaction IDs, customer contact information, items or services purchased, customer acquisition methods, discounts applied, and satisfaction surveys. How could a business use this made up data in any meaningful and purposeful way? They couldn't. This data would be of use to no one because no amount of technical knowledge or manipulation would yield anything positive - you cannot derive anything from it. In effect, it's "garbage in, garbage out" with data.
Intelligence, in the sense we're discussing here, is the use of data in effective ways to achieve valuable (whatever that means) goals and objectives in the real world.
What sort of goals are we talking about? They can be any goal that is worthwhile:
Most worthwhile goals are achieved through a combination of effort and intelligence - effort alone is not always enough because you need to put your effort int he right direction. Of course, intelligence alone is useless without the effort to use it also - but intelligence s the seed from which our goals can be productively and effectively achieved.
Intelligence is what sets humans apart in some ways from the other beings that inhabit the world we find ourselves in. Although lots of animals are intelligent in some ways, they're not as intelligent as us. We can use complex models of the world to make decisions - this is why we are the dominant species.
Intelligence is the stuff that builds bridges, building, and apps. Intelligence is what wins battles in war and battles in the boardroom. Intelligence is what allows you to outperform in life and in business - it's what can set you apart in the battlefield of business and make that customer come through your doors or visit your website or download your app instead of your opponents'.
Data vs. Intelligence
Data and intelligence are two different but interrelated things. Data is used in order to obtain intelligence. Or, stated another way, you need data if you're going to have some sort of intelligence.
Intelligence doesn't just arise out of nowhere. The kind of intelligence that is useful (the productive kind of intelligence that helps with making effective decisions int the real world) is based on data. Therefore, intelligence and data are not two different but similar things, they are two very different things with one being required fro the other. It's like water and oxygen - you need oxygen atoms to make water, but water and oxygen are far from the same thing. Just as with oxygen and water, you need data to have intelligence, but intelligence is far more than just data - it's using data to create an understanding of the world.
Intelligence can exist in many forms. It can mean knowing your:
Intelligence can also mean knowing things there aren't specific numbers, but are more comparative in nature - things such as:
Intelligence can also be binary - it can include things like:
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