Today, we want to start this post in a different way, inviting you to think about your marketing tactics. If you invest in your company's strategies, we want to ask you a question: do you know the importance of data analysis for capturing leads?
If your answer was no, it's time to worry, because when it comes to marketing digital data analysis must go hand in hand with your sales tactics for an effective strategy.
At this point in the post, you must be thinking about all your strategies so far, right? And if the results have not been what you expected, it may be a consequence of a lack of good data collection and analysis.
What you will check in the post:
- What is Data Analysis?
- How important is data collection for marketing strategies?
- After all, how does data analysis work?
- What types of analysis?
- Descriptive analysis;
- Predictive analytics;
- Prescriptive analysis;
- Diagnostic Analysis.
If you want to know more about data analysis, read on and discover the types of analysis and the importance of data collection for digital marketing.
What is Data Analysis?
Every day your company is fed with a lot of data with great speed and you should know that well. But the big question is: what should you do with all this? After all, how to organize this data and use it in the best way in your strategies? If this is one of your questions, we have the answer!
Data Analysis is the process of analyzing raw data, often done through software with a focus on knowing your customer's behavior and identifying patterns. That is, data analysis will interpret information from your target audience and contribute to your brand's next decision-making.
In this analysis, it is important to highlight that there are two methodologies used in the process: the qualitative, based on the responses of each client and discursive information, and the quantitative, focused on numbers and statistics.
But what does Data Analysis have to do with your marketing strategies? If this is one of your questions, the answer is now in the next topic.
How important is data collection for marketing strategies?
When it comes to marketing strategies, all companies have a common challenge: capturing leads. After all, how to attract him and improve his purchase journey so that he becomes a possible customer?
Well, you already know that, with data analysis, you can analyze information about your target audience, right? By following your future customer's journey and obtaining this data, it is possible to understand more about your lead, their concerns and dreams and connect with your potential customer.
With the analysis in hand, you will also be able to review your strategies and identify your main flaws. That way, your marketing team can fine-tune processes and better control operations. In other words, don't make the same mistakes!
It's time to develop a solid relationship between your brand and the lead through a more personalized and assertive communication strategy, you know? All this, of course, based on the data collected so far.
In addition to helping you get to know your customer better and correcting your strategies, data analysis can contribute to smarter business management and assist in your brand's decision making.
Not to mention that, by identifying and focusing your investment on leads that are most likely to convert, your company can still save money. After all, you will be directing your efforts to those who are most likely to become your customers. Wonderful, isn't it?
With data analysis and a broader view of consumer behavior, your company still has the opportunity to invest in innovations and promote a unique experience for the lead.
Ufa! How many advantages, isn't it? If you arrived here wondering what the purpose of data analysis is, in this topic, you have already managed to understand in depth that this process is fundamental when it comes to capturing leads and identifying which of these leads have the greatest potential for conversion. And if you still haven't invested in professionals and software with the necessary skills, it's time to start rethinking that decision.
Okay, but now that you understand the importance of parsing. It's time to find out how this process actually works, shall we?
After all, how does data analysis work?
You already know that Data Analysis, in general, is done through highly modern software and deployed in companies for information organization and analysis. But in practice, how does all this work? In order to do data mining, the analysis usually follows a sequence of eight steps, which we will cover now.
- Data entry: The first step in starting data analysis is defining requirements and collections. This is where you will determine the information you need and how you will be able to gain access to that data.
- Data preparation: are we going to the second stage? It's time to prepare the data to produce insights. It is in this phase that the raw information is filtered and the data structured for the next phase.
- Data Exploration: As the name implies, it is at this stage that there is a research and study of a large set of data. At this point, the focus is on understanding this information in depth, taking into account profiles, sampling, statistical analysis, patterns, among others.
- Data enrichment: After collecting and filtering the information, it's time to enrich the data analysis with additional information. It is at this stage that it is possible to see the process from a new perspective and gain new insights.
- Data science: in this phase, a new method is added to extract deeper and more detailed information. This process encompasses algorithms, Artificial Intelligence (AI) and machine learning.
- Business Intelligence: One of the last steps in data analysis is Business Intelligence. In this phase, it is possible to have access to reports made through the collected results, in addition to obtaining dashboards and studying the business process.
- Report Generation: In the penultimate phase, reports are generated and shared as quickly and effectively as possible.
- Optimization: although this is the last phase and all the data has been analyzed so far, it does not mean that your company has stopped receiving information, on the contrary, new data arrives all the time, supplying the created base, so it is necessary to elaborate variables, improve them and optimize the ones already created.
What types of analysis?
We can say that, at this point, you are already mastering Data Analysis much better than when you started reading this post. Just to recap, you already know what data analysis is, its impact on marketing strategies and how each step of this process works. But now that you've had most of your questions answered, it's time to get to know the four types of analysis. Prepared?
Based on facts, the descriptive analysis model takes real data as a reference. The purpose of this analysis is to guide digital marketing strategies. It is important to emphasize that descriptive analysis is directly linked to descriptive statistics, such as frequency, mean, median and percentages.
By focusing on real data, this data analysis model is widely used, especially in the educational and business sectors. Some examples of descriptive analysis in use are: reports, segmentation, application of metrics, among others.
With predictive analytics, your business will always be one step ahead. Now, you must be asking yourself "how so?". The focus of predictive analytics is to identify patterns and predict situations that may occur.
With its prediction of the future based on data collection, predictive analytics is one of the most popular models among companies. After all, who wouldn't want to predict effects or have a formula to anticipate their results?
analysis If in the predictive analysis the company has the possibility to foresee scenarios, in the prescriptive one, the objective is to define possible decisions and solutions for the foreseen situations.
It is from the possibilities that, in the prescriptive analysis, you will find recommendations and simulations to obtain a better result. In short, prescriptive analysis will show you where you should put your efforts to get around the future situation. In this model, artificial intelligence (AI) is used as machine learning and algorithm. AI can support prescriptive analytics data.
In the process of diagnostic analysis, the investigation is focused on something that has already happened. Far from predicting the future, as in the previous analysis, in this model, the central objective is cause and effect.
That is, through past events and based on probabilities, diagnostic analysis seeks to find relationships and clarify the causes of a situation or behavior that has occurred.
After understanding the types of analysis and everything about data analysis, the question is: what are you going to do with all this knowledge? You can't just stay in theory! If you want real changes in your fundraising, you need to put into practice what you've learned here. Access now our last post: How to retain and attract customers through audiovisual content.