Data analysis is critical to the success of your business-it tells you whether what you have done so far has been effective and what you can do in the future to achieve your goals.
"It is a capital mistake to theorize before having hard data. You end up distorting facts to fit theories instead of fitting theories to facts."
No, this is not a quote from a marketing book: it says so Sherlock Holmes In one of the stories in which he is featured.
I don't know how much of a connoisseur or fan of this genre you are, the fact remains that the good English detective is exactly right.
Don't you think so, too?
I imagine that you have still committed yourself to something new hoping for a certain outcome. But have you ever wondered what the conditions were for actually achieving it?
Data analysis allows you to do just that: gather useful information to use to make informed-and therefore better-decisions to be more confident that your actions will lead to success.
Not only that, in such a data-rich digital age, Understand how to analyze and extract the true meaning of such information helps you optimize performance.
This is often taken care of by experts in the field (data engineers, analysts, scientists, and developers), but it is important that you, as well as your team, know how to take advantage of this great resource at your disposal.
So let us see why it is important to deal with Data Analysis And what exactly it means.
What is Data Analysis
Data Analysis is the process of collecting and interpreting data about your business to draw useful conclusions.
The goal is to find meaningful information that can guide smarter and more effective strategic and operational decisions.
The entire process includes not only the analysis of various indicators in the narrow sense, but also the organization, archiving, tools and techniques used to deepen and communicate the results.
It is, after all, a real transformation of raw values into statistics and practical explanations.
They can come from any department (sales, marketing, production, etc.) and can be generated from processes, such as transactions with partners, potential leads and customers.
Why data analysis is important
Before talking about methods and types, you need to understand the benefits that data analysis can bring to your business.
As we have already said, it is primarily for making better decisions based on facts and not on mere intuition: it is a guide to show you the way and be sure it is the right one.
In essence, the data tell you where to focus your efforts, reevaluating your priorities-a way also to reduce costs and increase profits.
As far as customers are concerned, data analytics gives you a 360-degree view of all aspects of them. You can understand what channels they use to communicate with you, their demographics, interests, habits, buying behaviors and more.
In the long run, predicting their actions will bring success to your marketing strategies, allow you to identify new potential buyers and avoid wasting resources.
A deeper understanding of your audience's needs enables you to create better business relationships.
Data help you improve specific aspects of your products or services by analyzing trends and increasing customer satisfaction.
You can detect competitors' weaknesses and strengths, discovering a new competitive advantage, as well as increase awareness of risks to implement preventive measures.
In essence, data analysis helps you optimize performance, and if we think about, for example, the web marketing and its tools, is certainly a particularly valuable resource.
The Internet, as you well know, is a crowded space, and the effectiveness of one's actions is the first rule of success.
Types of Data Analysis
Okay, so basically what does the data analysis consist of?
Among the various techniques you can use, there are four basic types:
- Descriptive analysis: is the starting point, answering the question "What happened?"; it describes what has happened in the past, presenting patterns and statistics.
It is often used to monitor key performance indicators (KPIs), revenue, sales leads and more.
It clarifies your understanding of the current context, but it does not tell you why and how certain numbers developed, nor does it allow you to predict future results.
- Diagnostic analysis: answers the question "Why did it happen?"; provides answers to specific questions so you know how to deal with a particular problem.
It is useful, for example, to understand customer behavior or to know which marketing campaigns are most effective.
If you find that your leads have increased, this analysis will tell you what contributed the most-it's a way to link cause and effect.
- Predictive analysis: the previous two types of analysis draw conclusions about the past; the predictive method uses data to make projections about the future, answering the question "What will happen?"
For example, to forecast next year's revenue, data from previous years will be analyzed.
After understanding why a trend or event has occurred, you can predict your customers' actions and develop more effective and competitive initiatives.
- Prescriptive analysis: combines the information found so far and shows an action plan to address the problem or make a decision.
It is the most advanced form of analysis, which considers multiple scenarios, predicts the outcome of each and suggests the best course of action.
It answers the question, "What makes sense to do?"-this is when decisions are actually made based on the data collected.
The steps in the data analysis process
Well, now that you understand what is data analysis and what type it may be, let's see how to proceed in practice. Here are the different steps to follow.
#1 Data analysis skills.
You need to know what kind of data you will need to collect and evaluate; ask yourself questions that have definite answers and decide what to measure and how.
#2 Data analysis process.
Organize them intelligently, using various sources and tools (I recommend you have an effective filing system that doesn't waste too much of your time).
#3 Delete unnecessary data.
Remove any statistics or metrics that do not align with your goals and strategies; check for duplicate, strange, or simply irrelevant values; find and correct errors, to avoid arriving at incorrect or inappropriate conclusions.
#4 Analyze the data collected.
Choose the type of analysis, find relationships, identify trends, sort and filter your data by variables; you can use dedicated tools and software.
#5 View data.
Draw up charts and tables so that it is easier to understand and process; dashboards are a great way to concretize information and make it easier to identify trends and patterns (as I also say in this article, graphic visualization is more understandable and immediate for the brain).
#6 Data analysis help.
Interpret the results of your data analsysis to understand how they answer your initial question and decide how to act accordingly.
So what do you think?
I know, it might all sound a little articulate, but there is one thing you basically need to keep in mind: knowing the numbers and values of your business is critical to avoid wasting time, money and energy on something that really isn't worth it.
There are so many indicators of how your business is performing, and you have endless ways to gather useful information for your marketing strategies: whether the data is quantitative or qualitative, whether it comes from surveys or social media reviews, the important thing is that you know how to use it in the right way to grow.
No doubt, the data analysis and data tracking in the digital world is even more crucial, to understand what went well and what didn't, what the next steps should be, but most importantly where every penny you invested went.
Do you agree?
Well, very soon I will tell you about this in more detail, showing you what indicators you should pay particular attention to in analyzing your digital business.
In the meantime, start asking the right questions and figuring out where you can find the answers.