Have you ever stopped to meta method think that making decisions is the last step in a broad and complex process?
And in the corporate world, you may often be responsible for taking care of this process. Therefore, we created this article to help you better manage business decision-making, making your decisions more assertive and safe.
The META method
The META method, created by Data b2b email list Management, is an acronym for: Measurement, Extraction, Treatment and Analysis, which helps to make better decisions, because it has a structured process that will serve as the basis for your final decision.
This method has 4 major steps that we will explore now:
Measurement
Measurement within the scope of Growth 5 examples of easter email campaigns Ops is the basic starting point in any data-informed decision-making process, because without reliable data, the decision is likely to be wrong. Therefore, let’s highlight the most important points that, in our opinion, cannot be missing from your project:
> Website : within websites, there must be a complete Web analytics job, to ensure that all tracking tags are properly installed and firing correctly. This is one of the most important processes because practically all of your acquisition efforts drive traffic to the website pages, where there are conversion points for new leads for your company.
Based on this, it is necessary to ao lists ensure that everything is being measured as planned.
> CRM : in CRM, it is very important to have structured measurement, because after conversion on the website, the purchasing journey continues and numerous actions can occur that need to be measured to generate valuable inputs for various teams, such as BI, Marketing and Sales.
> Campaigns : Acquisition and conversion campaigns are essential for the growth of your company. And when we talk about measuring campaigns, we mean looking at results from email marketing, organic traffic, paid traffic, social media and more.
And your team needs to ensure that all these efforts are measured correctly.
Extraction
Once you’ve ensured that everything is being measured correctly, it’s time to look at the extraction process. This process involves mapping all the locations that generate data and are being measured.
Below are some precautions for extracting data:
> Ensure that all measured data is being extracted, especially when it is not in the same place (example: part of the data is in Google Analytics, part in HubSpot CRM, part in paid media, and so on);
> Ensure that data extraction is being performed in a standardized manner, i.e., all data is being extracted in the same format. The most common format, for example, is for extractions to be in CSV, XLS or XLSX files.
> Avoid extracting data that cannot be processed later in the processing, such as PDF, images, etc.
> Logical and intuitive compilation of data, preferably in the same database/spreadsheet so that you have easy access to the data, facilitating its processing.
Treatment
Treatment is the part where we prepare the data for analysis. This process is essential, as incorrect data treatment can lead to incorrect data structures, compromising the effectiveness of the analysis.
The processing includes, after unifying all exported data into a single database:
> Adequacy of data formatting so that formulas and visualizations work (even if care has been taken in extracting data, it is important to double check to ensure that no numbers, text or dates, for example, are outside the standard)
> Standardization of the units treated (date formats, months, years, monetary units, texts, decimal places, percentages, etc.)
> Building essentialist formulas and logical processes so that complexity is simplified. It may not seem like it, but an essentialist data processing structure can save time and make it easier for other people on the team to handle.
> With the data processing process well defined, the reasoning used can be documented, ensuring understanding by any database user and other parties involved in the decision-making process, especially those approving the decision.
Analysis
Data analysis is the final step in the decision-making process. For this reason, a logical and well-defined analysis helps in making the best choice at the time.
In short, all data analysis consists of six steps:
> Clear definition of the objective of the analysis, that is, the problem;
> Definition of the criteria used in the analysis, such as metrics, time periods, and indicators
> Formulation of the initial hypothesis, based on the primary analysis
> Comparison of the initial hypothesis with the performance of an in-depth analysis, seeking to find flaws in the initial hypothesis;
>When flaws are found, the initial hypothesis undergoes a modification and the previous step is carried out again. Until the initial hypothesis becomes the final hypothesis found in the data analysis;
Finally, with the entire analysis process carried out. The decision maker will have all the inputs very well prepared, verified and explored to make the best decision.
There are some articles we have created that also talk about data analysis for decision making. Where we confront two views of analysis: data-driven analysis and data-informed analysis. Learn more by clicking here .
There are also other ways to do analyses, such as horizontal and vertical analyses. Which we have already covered in full on our blog. See here .
Conclusion
Decision making is the end result that still involves many human factors, in addition to the entire process described above.
It is important to emphasize that human factors are still essential for optimal decision-making. As we have the power of contextualization. Something that machines and algorithms are not yet capable of doing.
