Importance of analyzing data in Business - Basic introduction
Let us imagine we are designing a marketing campaign. Of course, for it to be successful, we want to properly target our audience.
Consequently, to improve the outcome of marketing campaigns without investing unnecessary resources, it is fundamental to understand how data analysis can assist us. So let us start by defining our main goal:
?Identify which group of clients is more likely to buy your products.
1 - Understand the data
Understanding and identifying our main group of potential clients, is fundamental for a successful marketing campaign. And, in order to do this, the first thing we need to do is to get acquainted with our company's data, and understand how it is structured.
To simplify, let us imagine a 2-sided table, on which on one side we have the product details (ID, Price, Stock...). And, on the other side, the client's information (if they do decide to give us their data).
Of course, not all client information will be complete, as not all clients buy all products, nor are all clients willing to give several details. Additionally, if we are speaking in terms of corporations, in their industry, some products simply do not make sense to buy, nor do they want to identify a point of contact or more details.
2 - Understand the variables
After becoming acquainted with the data, we need to dig deeper into the variables and identify ways to measure their respective types. ID, location, price... we must organize data as categorical or numerical. One example might be, for instance, the industry in which the company operates.
Would you quantify the industry in which a company operates? For instance, does it make sense to say the company operates in a 2.25 average real estate market? No. The bottom line line is that the industry is a categorical variable. And, as such, it is a qualitative or nominal variable.
?What about time?
The age of a company, person, or event is quite interesting. The easiest rule to understand quantitative variables is to determine if there is a true zero. I.e., at the moment a product is sold, it has been sold for 0 minutes/days/years. As such, we have our true zero. Our age, too. When we are born, that moment is the zero point.
What is intriguing about these types of variables is whether they are discrete, or continuous. Can you discretely define time? I.e., can you quantify the exact moment you sold a certain product? What about your age? Can you blow the candles on your birthday cake at the exact time you were born? Interestingly, the answer is both yes and no.
How?
You can define time infinitely. That is, you can say you were born at 10:30am and 0.122857264758(…) seconds. Would it be useful to say out loud the exact instance of the time you were born, though?
As such, time can either be treated as a discrete variable (I'm 24 years old) or as a continuous variable (I'm 24 years old, and 10.0180927081(…) minutes).
Although it is possible to define time discretely, in our day to-day, some things are better off just being simplified. So, we might as well just say you sold the product on the 16th of June to a 25 year old man, or that you were born on August 13th at 3:31pm.
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?Why is understanding variables important in the corporate world?
Cash flows, gross profit margin, assets, salaries, long-term or short-term debts. Prices, fees, or any other cash-related terms are central in the corporate world. Besides all those terms, there are also other types of variables: pay-dates, employee or product names or ID's, employee turnover, ages of customers, locations, stores, and much much more. Therefore, understanding the data and variables we are working with is absolutely fundamental.
?What about transforming variables? Is it possible?
Absolutely! Let's say, for example, you define the following categories of seniority for the employees in your company:
Junior (0–2 years' experience); Junior-Mid (2-4 years' experience); Mid-Senior (4-6 years' experience); Senior (6-8 years' experience) and Executive (8+ years' experience).
Instead of categories, we can say that the personnel belong to categories 1 to 5.
Which in this case, would now allow you to quantify the mean of seniority! Analyzing this logically, you can have the most employees in your company at category 2.5, which means most of the employees in your company are halfway into mid-senior level!
?As such, understanding how to organize and label data is of utmost importance in our analysis!
Why does it really matter to understand how categorize variables? Does it really affect the approach I take with my company/marketing campaign?
YES!
Would it make sense to target a baby formula advertisement at a 6-year-old child? No... why would a child want to buy baby formula? We would only be wasting resources on an unprofitable campaign.
In this case, it would make more sense to determine at what age range our buyers are in and design our marketing campaign specifically for them.
What if your company specializes in manufacturing energy drinks designed for professional athletes? Would it make sense to direct our marketing campaigns towards children? No, but can it be profitable to design our campaign to also try and captivate people that are gym aficionados? It can make sense, but... the answer will depend on what the data analysis of our product tells us!
Finally, what if... have a business on a global scale where about 96% of the corporate's profit comes from a group of 10 countries. Does it make sense to invest heavily in marketing in these countries or the countries that make up for 4% of the revenue?
That might also depend on the data analysis, as there are many factors that might be affecting our sales. For instance, intense competition, population culture, average population age, and much, much more. In this case, we must test hypotheses and do future analysis.
3 - Analyze the data and take conclusions!
We started by understanding the data and the variables and getting acquainted with both. Second, we defined and understood both the goal and the data that would be required to achieve our ultimate goal. The final step is to understand how to process the data and draw conclusions based on the information that we have!
Understanding how to organize, process, and analyze data is an extremely vast and fascinating world. And, after reading all this text and understanding the basic correlations of how data ties in together, aren't you curious to learn more about how to draw statistical conclusions? ??