Statistics are useful in many fields, including business. Especially now, in the era of “big data”, having a good grasp of statistics can help to take your business from average to exceptional.
So, how is statistics used in business? Statistics is used in business for: appraisal of value, consumer surveys, hiring decisions, insurance, manufacturing, online business, real estate investing, rental housing, sales, and stock markets. Data analysis, regression, forecasting, hypothesis testing, and more are used in these fields.
Of course, statistics is a tool that serves several purposes. It can give you insight into business operations, help you examine what went well (or what went wrong), and make predictions about the future.
In this article, we’ll talk about 10 ways that statistics is used in business. We’ll also go into detail about what types of statistical methods are used in each of these examples.
Let’s get started.
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Statistics is used in business, and many different methods are used, including: data analysis (mean, standard deviation, etc.), regression, forecasting projections, confidence intervals, hypothesis testing, and probability distributions.
Here are some specific examples of how we can use statistics in business:
Let’s take a closer look at each of these applications in turn, along with what statistical methods are being used in each case.
An appraisal of value helps to give an estimate of what a house, vehicle, or business would sell for. This estimate is based on current market conditions and the best available information.
In real estate appraisal, one common method of valuing a home is the sales comparison approach. This involves looking at similar homes (for example, other 3-bedroom, 2-bathroom ranch homes), finding out the average price per square foot (for all similar homes), and applying this average value to the home’s area.
When you buy something of value, you will often finance the purchase with a bank loan. However, the bank will only lend to you if you have good credit and income, or if you have collateral.
A house, vehicle, or business has tangible value, since it can be sold on the open market. If you fail to repay your loan, the bank can take possession of the asset and sell it to recover some or all of the defaulted loan amount (plus collection or legal fees).
However, a bank will limit the amount you can borrow, based on the value of the asset. For example, many banks will only provide a mortgage loan of up to 80% of the value of a property.
This means you would need to make at least a 20% down payment. If you want to borrow more than 80% of the value of a property, you would also need to pay for private mortgage insurance (PMI) to protect the bank in the event of your default on the loan.
An accurate appraisal of value prevents the bank from making loans on a property that is worth a lot less than they thought.
A common phrase in investing is “buy low and sell high”. What this comes down to is trying to buy an asset for below the market value and selling it above market value (arbitrage), or else waiting for the value to go up (price appreciation).
Stock market investors will often use metrics such as price, earnings per share, P/E ratio (price per share divided by earnings per share), and debt load to evaluate a business.
A construction company also needs to make a decision about when to invest in new housing, and where to build. The inputs they have to pay for include:
All of these costs can vary, but the price of the finished home is another variable. Getting an accurate price appraisal before starting can help to determine if there is any profit in a construction project or not.
A consumer survey can help you to discover consumer sentiment about a product or company. It can also reveal demand for a certain product or service in a given area.
Consumer surveys can ask both quantitative (based on numbers) and qualitative (based on verbal descriptions) questions. Looking at the data from a sample of the population can give us insights into the market, based on statistics we calculate from the data.
For example, we can look at:
It is a good idea to figure out how you are going to use the data before you conduct a survey. Consumer surveys can be expensive and time consuming!
Statistics can also help companies to make better decisions about hiring and compensation for staff. For example, instead of guessing about what salary to pay, a company can do research to find the average salary for someone in that position.
If they are having trouble hiring because of a tight labor market, they can offer a salary above the average. They can also offer extra non-cash benefits (such as working from home) to attract workers.
A company can also keep track of average employee tenure in a given position. This will help them to predict how often they will need to hire, or encourage the development of a talent pipeline and training program.
Insurance companies must make good use of data and statistics to make profitable decisions. They must also keep close watch on their various lines of business to see problems early.
For example, two statistics that an insurance company can calculate are severity and frequency for claims.
Severity is the average cost of the claim (the units are dollars per claim).
Frequency is the number of claims per time period (the units are claims per time period).
When we multiply frequency and severity, the units of claims cancel, and we get dollars per time period.
If a line of business has an increase in the total claims dollars over a given time period, they can investigate why:
By keeping close tabs on their data and statistics, insurance companies can make smart decisions about whom to insure, and at what rates.
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Statistics can be used in manufacturing to track whether the average size (weight, height, length, etc.) of a part or product is correct. Statistics can also help us to determine variability (usually by calculating standard deviation).
In manufacturing, there is a certain tolerance for defects, but the closer we can get to the specified dimensions, the better. For projects that require low variance (small parts with precise specifications), it is crucial to keep standard deviation as low as possible.
There is a tradeoff for of time or cost in exchange for quality. A machine (or machinist) that can do more precise work will take more time or cost more money (or both).
For example, let’s say we have a manufacturing process with a mean width of 0.5 inches and a standard deviation of 0.01 inches. Let’s also assume the size of the manufactured items follows a normal distribution.
Then we would expect:
If a customer specifies that 95% of the items must be within 0.02 inches of the specified 0.5 inch size, then we will be able to fulfill the order.
If a customer specified that 99% of the items must be within 0.03 inches of the specified 0.5 inch size, then we will also be able to fulfill that order.
However, if a customer specifies that 90% of the items must be within 0.01 inches of the specified 0.5 inch size, then we will not be able to fulfill the order with our current capabilities.
Online businesses, such as e-commerce stores and affiliate marketing websites, are very concerned with conversion rates. The conversion rate for an offer is the percentage of people who click through to take an action (sign up for an email newsletter) or make a purchase (buy a physical product, eBook, or course).
Statistics can help online businesses to make decisions that better serve their customers and result in more profit. For example, customers will only buy a product if the benefits are clear to them.
A website owner can use split testing (A/B testing) to find out which version of a sales page encourages more people to sign up for a newsletter or buy a product.
Knowing your audience well can help with this process, but testing is the only way to know for sure what will work best.
We can calculate the statistic (conversion rate) for both days to find:
It looks like version 2 converts slightly better than version 1 (2.5% versus 2%). This may not seem like much, but it is a change of +0.5% in conversion rate.
If your website grows to 400 visitors per day (146,000 visitors per year), an increase of 0.5% in the conversion rate means an extra 730 people will buy your book.
At a cost of $15 per book, that is an extra $1045 per year, just for conducting a small split test!
Statistics can also help real estate investors to make better decisions about where to spend their limited time and capital. You may need to use multiple statistics to make the best decisions.
For example, the price alone is not enough to make an informed decision about where to invest. Let’s see why.
Let’s say there are two investment properties available. Both are 3-bedroom, 2-bathroom single family ranch homes, and both are the same price: $350,000.
Based on these statistics, it seems that House 1 will appreciate faster, at 8.8% per year (versus 5.8% per year for House 2). This could make a big difference over a 10-year investment window:
This is a difference of about $200,000 over 10 years ($20,000 per year), just for spending a few minutes to do a little extra homework on Zillow.
To learn more about which properties and neighborhoods are desirable and on the rise (and what nearby amenities can make a property appreciate faster), check out Zillow Talk.
Statistics can also be useful in rental housing. For example, you can compare the average vacancy rates in your apartment building to the industry average for your geographical area.
If your vacancy rates are lower than average, it may mean a few things:
On the other hand, if your vacancy rates are higher than average, it may mean a few things:
Statistics can be used in sales to make predictions for weekly, monthly, quarterly, or annual revenue. This can help the business to decide how much to spend and whether to hire or not.
Forecasting can also be used to predict growth rates in various markets. This can help the company to decide which market to enter or which audience to target.
A stock market investor can use various statistics to decide whether a stock or bond is a good investment. Some common statistics include:
For example, let’s say that a company has a P/E ratio of 30, compared to a market average of 25. The company seems overvalued in relation to the rest of the stock market as a whole.
However, if the company’s historical P/E ratio ranges from 32 to 40, then buying in at a P/E ratio of 30 may be a discount for this particular company – regardless of the rest of the market.
Now you know how you can use statistics to understand the data in your business and to make better decisions. Maybe this will give you some ideas on how to improve your operations.
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