Making the right decisions with a high impact on results is what every Marketing manager wants. For this to be achieved, data analysis techniques must be the most effective, allowing the efficiency of a strategy to be thoroughly ascertained and anticipate opportunities.
Nowadays, Digital Marketing teams have many technologies to carry out their tasks, especially those that are part of the digital transformation, for example:
Big Data: They are a compendium of techniques and processes for analyzing large amounts of information on the network, to determine aspects such as the behavior of buyers and their habits on the Internet.
Artificial Intelligence: is a technology that is allowed to “learn” while it works. It is used to make predictions, based on patterns, identify contexts and improve decision making.
Dashboards: are interfaces that provide key information, in the form of graphs, to analyze the progress and performance of a strategy.
It would not be wise to go to the heart of this article without first mentioning those fundamental skills that favor any team or individual in data analysis. Get to know them below!
Skills needed for data analysis
We will not be very extensive in this part, we will only talk about the competencies that we consider to be the most important and why.
Part of the analysis process requires ordering all the raw data to determine patterns and insights.
Likewise, with the amount of information and tools that are handled in this task, the order is the best ally, even to avoid the tendency to analyze a lot of numbers that do not represent priorities.
Much of the work of a data analyst is to determine, through mathematical and statistical models, standard deviations, regressions, arithmetic mean, and other formulas for decision making.
Objectively analyzing the questions, hypotheses and results obtained is an essential skill in the field. In this way, more relevant data is obtained and can be related to each other.
Identifying opportunities, finding problems, and devising solutions is also the task of the data analyst.
It is crucial that you can find even the most complex problems to quickly think of a way to solve them and take advantage of them.
Now, with this in mind, we can explain what are those most effective data analysis techniques of the moment.
The best techniques for data analysis
Data analysis has improved thanks to the sources that provide this information, where the creative approach of the teams is a fundamental value to take advantage of and develop new ideas to use the data.
Now, this is accompanied by certain techniques that greatly facilitate the process, among the most important are:
1. Marketing Mix Model (MMM)
It is an advanced data analysis technique that involves Big Data to measure the effectiveness of diffusion through a specific channel.
To do this, links are used between the Marketing statistics that are generated with other sales methods.
Typically, variables such as seasonal factors, competitor activities, and promotional campaigns are included to determine interactive effects and changes in segments and individuals.
This mechanism can be used to understand the participation of each communication medium in the generation of new clients within a given period.
For example, suppose that there is a growth in the business opportunities that come to your company from a Landing Page that you recently launched on your blog.
This data can mean that the page has had a good performance and can serve as a successful model for the others, which will allow interpreting that it is worth investing more in this channel.
2. Scope, cost, and quality (RCQ)
The Reach, Cost, and Quality is a way of using data and structured judgments, reducing channels to their components, for example:
the quality of engagement,
number of target customers reached,
and the cost for each touchpoint or conversion.
And, speaking of data analysis techniques, it is used when almost everything else cannot be applied, that is, when the information is limited, when there is a consistent routine throughout the year, or when the marginal effects of the investments are difficult to measure.
3. Predictive models
This is a representation of reality to find the relationship between some variables. The technique requires information technology, part of the digital transformation, Big Data, and management skills.
Thanks to this tool, you can find business opportunities, know the market share, identify segments, in short, a wealth of information to make decisions based on data that generate the highest profitability and dividends for Marketing and Sales strategies.
4. Attribution model
For the execution of Digital Marketing, attribution is part of the newest approach methods.
This model allows the use of algorithms and rules to efficiently manage the available resources to convert and sell. For example, when buying online ads, email campaigns, among others.
In addition, it allows marketers and programming professionals to decide how useful each channel is for conversion success.
With the use of regression techniques, advanced algorithms, and statistical models, greater perspectives of the reality of the strategy are generated and the channels are optimized.
The analogy that becomes clearer to understand this analysis is with soccer.
When the player scores a goal after receiving a spectacular and improbable pass, what percentage of participation is attributed to the scorer and the passer? Depending on the circumstances it will be higher or lower.
The same thing happens in the field of Marketing.
If a customer had his first interaction with the company through social networks, but before becoming a consumer he accessed many pages of the blog, an attribution model must be carried out to understand how efficient those channels were throughout the day of the customer. client.
5. Time series analysis
This sequence of values that are observed for a given period of time and are arranged chronologically is used to, among many other things, estimate future values of a specific variable based on its historical behavior.
This type of technique is widely used, as a mathematical prediction model, by Marketing and Sales teams to foresee trends, take advantage of all kinds of opportunities and adjust strategies, for example:
- Number of visits to a blog
- Sales numbers
- Downloads of rich materials, among others.
The values that result from the analysis of time series are not exact, but following a regularity in the series and the correct formulas, it is possible to model and, with it, predict the results.
This model is very interesting for managing a blog, for example. When defining the growth goals of this channel in the coming months, its behavior in the previous ones must be taken as a reference.
In high season months, many professionals usually go on vacation, therefore the volume of hits on your blog may be lower. This variable and others must be taken into account so that the analysis is not contaminated.
Now, it is important to clarify that, although collecting information is a fundamental part of the process of data analysis techniques, what to do with it is the other pillar. If you want to know more, keep reading!
What to do with the information generated with the techniques?
Good data analysis — the most critical thing is that you understand what it means and what kind of information you have behind all the numbers and behaviors.
Knowing how to interpret data is one of the most crucial challenges that Marketing teams face in the quest to make accurate and relevant decisions, allowing each member to achieve more autonomy.
A Marketing professional must collect the data from all the tools they use, record them well in the form of documents and combine the variables that show the level of interpretation, which is the prelude to decision making or, at least, a shower of ideas of actions to take.
How to do it efficiently? For example, in the case of blog performance analysis, tools like Google Analytics and SEMRush are preferred by marketers. We tell you why.
This Google tool allows any team or person to analyze the receptivity of their audience to the materials they offer and, of course, the effectiveness in terms of attraction techniques.
Among its many features you can:
- Know the number of unique users
- Track traffic
- Know the bounce rate and time spent on the page
- Track visitors and learn their locations
- Be aware of the devices preferred by the audience to navigate
- Know the preferred operating systems and browsers
- Monitor traffic in real-time, among others.
In addition to all this, it does so through an intuitive and very practical interface. Regardless of your level of knowledge about it, you will be able to master it in no time.
It is a popular tool used to monitor keywords, process data analysis for SEO and SEM, as well as provide general optimization for Content Marketing strategies.
It can also be used as part of the interpretation of the data, in aspects such as:
- Backlink tracking
- Traffic analysis for organic and paid searches
- Keyword analysis for ads
- Social media post-performance
- Blog content performance
- Creation and management of the editorial calendar
- Analysis of competitors, among others.
With these tools, you will be able to achieve a much deeper observation of your company’s environment and deliver better analysis and predictions to generate the results your team expects.
Major difficulties you may experience when analyzing data
Collecting data only makes sense if there is a trained team capable of analyzing it through processes and tools to generate business opportunities and gain authority in the line of business.
Next, we will show you those mistakes in which you should avoid falling.
Disregard the data
In many companies, there are years of data that accumulate for no purpose, for example, sales history or profit and loss analysis.
Proposing a new organizational culture where decisions are based on data is the best first step to get away from this mistake.
Not knowing why the data is collected
Without clear objectives and goals, it is impossible to understand what data will serve. No marketing tool, software, or equipment can elevate a company that doesn’t know what it wants.
Not investing in data analysis
Many managers and owners are scared by the costs of data management and analysis platforms. It is essential to understand that making efforts to train the team, buy software, install devices, and have digital security are obligations for those who want to stand out.
Separate the departments
Integrating the different processes of the company to generate a fluidity of data and tasks is a step that many do not take. The unification of information is a way for each department to generate greater benefits to market requirements.
For this, cloud-based architectures, business intelligence, and data centers can be adopted.
With everything that we have explained in this material, you already have enough material to understand how data analysis techniques are used today.
To conclude, we want to give you some final recommendations with some questions that you should ask yourself at each stage.
Are you using an adequate sample size?
Many times, prestigious companies make the mistake of giving “study” results without guaranteeing that they get a real response from the public. When you are analyzing data, make sure that the sample can give you a genuine vision of reality.
How much do you depend on chance?
If your research and collected data cannot give you at least 90% confidence that you are correct, then you should not believe it. A loose plan can lead to disaster. If necessary, hire trained personnel to give you truthful information.
Are you stuck in analysis?
Big Data is wonderful, but you must not forget your instinct and common sense. Using information obtained by the aforementioned techniques is highly recommended, but using your logic and experience is also necessary to start taking action.
We hope this content has been educational and to your liking! We want to give you something before you leave.
This time it is our ebook Marketing and Sales Metrics and Goals , it is a very important material for those who want to accompany these departments towards the same goal. Avoid mistakes and better plan your strategies!