Digital Analytics | What is it and how can you improve it?

A world interconnected through the Internet cannot be analysed only in the traditional way, according to what we're used to see. 

Haven't you noticed how communication between people or companies is increasing every day thanks to digital media?

The digital relationships in our society are a FACT and the best part is that everything is registered, established and recorded, making it easier to analyse the information that reaches our hands. 

Because of this type of relationship, companies and entrepreneurs can now understand their customers better and shape their decision making in a better way.

And how do they achieve this? Through digital analytics, an essential consulting and advisory tool, especially for marketing teams, which can study the impact of any project and predict future actions (in the short or long term).

If you want to know everything about this term, you don't have to worry, because here we try to help you with an analysis that is as simple as possible. 

When you complete your decision making with analytics, you will see impactful results both in productivity and in the relationship with your customers and leads; so let's get started!

What is digital analytics? 

Digital analytics is a research process that concentrates and analyses qualitative and quantitative data from a large number of digital sources.

This process focuses decision-making on the synthesis and analysis of a large amount of data. This facilitates decision making by focusing on only the essential data.

Conceptually it sounds very easy; however, it aims to understand or illustrate user behaviour and the influence of actions and opinions through digital media, i. e. websites (on computers or mobiles), apps, social media and others...  

Differences between digital analytics and digital marketing

Many people tend to confuse digital marketing with analytics, but there is a distinct difference between the two. 

Digital marketing is an innovation of traditional marketing, which promotes products and services (or brands) using digital marketing techniques. 

That is, it focuses its service on formulating strategies in order to sell through technology (computers, mobile devices) and the internet.

Digital analytics, on the other hand, is a consulting or advisory tool, essential for developing digital marketing strategies.

That is to say, the first concept uses the second to collect data and transform it into results that make it possible to predict various factors, such as:

• Behaviour of users or leads (the audience in general).

• Defining tastes, hobbies, among other aspects.

• Verify the position of a product, service or brand compared to the competition.

• Validate the presence in search engines.

The 3 most important objectives of digital analytics

The fundamental goal of digital analytics is decision making, as any online media owner will understand and know how their customers or leads interact with all online interaction tools. 

Knowing this digital data, the necessary adjustments can be made to help improve the user experience (UX) and the performance of the online business in all aspects.

However, in order to make the best decision, it's necessary to pursue 3 specific objectives that will complement the data and its understanding.

1. Explaining the process

The first stage of analytics is the description of a phenomenon or a particular project in the online world, defined over a given period of time. 

Once the phenomenon is known, the factors that directly influence it are detailed, establishing its causes and possible effects in the future. 

As an example, we can mention a hypothetical case:

1. Comparative description of online sales month by month.

2. One key factor will be the impact of a special social media advertising campaign on online sales.

3. Another factor influencing online sales would be to verify the difference of users and visitors. In this case, users with high conversion rates are studied and compared with the rest.

2. Prediction or future estimate

Once the phenomenon and its relationship with the factors that influence it are known, it's time to make a future prediction of its behaviour (predictive analysis).

This requires:

• That the coincidences found between the phenomenon and the related factors can be fulfilled in time.

• That the methodology applied for the study, relationship and results of the data is firm and comes from analyses of realistic environments. Nothing can be based on hypothetical studies. 

3. Decision making

The phenomenon to be studied has been explained step by step and the most relevant data have been taken into account to make behavioural predictions. It's time to make decisions!

Decision-making comes from estimations; this objective aims to determine actions based on the results obtained and the estimated predictions.

The 5 most important steps for successful digital analytics

More than steps, digital analytics has a methodological feel to it.

The correct application of a digital analytics plan follows a series of pre-analysis, analysis and post-analysis steps, each with a particular methodology.

So, below we review what the fundamental steps are and the results that will be obtained with each one of them. 

Step 1: Defining the objectives of the phenomenon

When performing digital analytics for a particular process or phenomenon, it's necessary to start by defining the primary objective of the process or phenomenon at the analytical level.

In this case, you look for ways on how to measure it and at the same time understand what is the problem or set of problems to be solved (specific objectives).

Although it seems easy, this pre-analytical step of defining objectives is considered the most difficult by experts, as it generates more difficulties than those it seeks to solve.

Once the objectives of the process or phenomenon have been defined, it's much easier to move on to the next step: the search for or "mining" of data.

Step 2: Doing data mining

Another pre-analytical step associated with finding the best information.

When we do data mining we mean that, in a digital research environment, data comes from a wide variety of online sources entirely (blogs, forums, websites, social networks, mobile apps and more).

For this, it's necessary to extract, process and model the data according to a query that fits the objectives of the phenomenon to be studied. These steps must also be complemented by a correct choice of available sources.

As a side note, many people dedicate themselves exclusively to this small part of online analytics: collecting and archiving digital data.

Step 3: Data transformation

A digital analysis will result in a large volume of data, and not all of it can be taken as true. 

The transformation of this data comes from applying appropriate methodologies that focus the study of the phenomenon on the most relevant or related information.

In order to do this, it's possible to apply one of the most widely used techniques prior to its final phase: visual thinking. It allows you to prepare the information by synthesising the results and making them easier to interpret.

Step 4: Data interpretation

We've reached the core of this tool, the previous points can be said to be preliminary preparation tasks. 

Once the information is selected, cleaned and transformed, we proceed to the interpretative visualisation of the results. 

This step is also known as "information exploitation" and certainly takes up almost the entire concept. 

And why? Because its results are used to implement corrective or specific actions. From here, data is transformed into learning and experience, with concrete actions that facilitate decision-making. 

In this analysis, patterns and trends are contrasted, the influence of figures is studied, all done in a strategic way, covering the objectives of each projected phenomenon. 

Step 5: Keeping data up to date

What does updated data do? This post-analytical step is based on the dynamism of time; therefore, data taken into account for actions in the past may not be valid or adapted to present requirements.

It's therefore necessary that the data for the study of a phenomenon is kept up to date and validated, so that predictions and decision making are accurate at all times.

Benefits of digital analytics

Digital analytics of data from online communication strategies has significant benefits when it comes to improving strategies that offer users an authentic and personalised experience.

That's why we can name among the most important, the following benefits:

• Increased productivity throughout the operations chain.

• The organisation (in any field) improves intensively, thanks to the fact that it becomes more flexible to the needs of the market and to users and their demands.

• Increased extension of the multi-channel environment, to offer a personalised experience.

• Better understanding of needs translates into a better user experience, as well as higher lead conversion.

• It uncovers the best trends and studies their application through marketing strategies, thus optimising the advertising investment: more results with less investment.

• It increases the secure environment, mitigating fraud in all areas, from the organisational aspect to the relationship with the customer. 

Areas of focus of digital analytics

This process can be applied in a wide variety of digital areas, as the new times are full of technological innovations as well as the massification of information through technology.

Digital analytics provides a complete view of the entire audience. Therefore, it can be applied in the following areas, related to any business or project:

• User analytics: which allows you to obtain the data of visitors and the behaviour that is most repeated, all this in a given period of time. It also analyses other related indicators such as bounce rate, conversions, among others. Here, the important thing is to have a good understanding of the customer's journey.

• Website:  this is an area where there's a significant contribution of data, such as traffic, quality of links, on page factors (of the page itself). Web analytics is an essential part of taking action on strategies such as SEO.

• Social media: another source of data for analytics and where its results can also be applied. It is social media that allows better measurement and tracking of the performance of all digital contact channels.

• E-commerce: digital analytics can gather information and track ecommerce-related information, such as conversion rate and campaign response, among other data.

Already started your own digital analytics? Endnotes

With all this material, you will see digital analytics more as a support strategy than as a simple data analysis. 

Moreover, digital marketing is grateful for its results, because without them, its strategies and problem solving could not be realised. In other words, marketing could not exist without analytics.

And your customers will be grateful, because the ultimate purpose of digital analytics is to make them feel understood by better understanding what they want or need. 

We want to know what you think about digital analytics! Are you as passionate about this topic as we are?