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Marta Miranda
– Branded Content Specialist & Producer –

If you usually invest in Digital Marketing content without knowing why or who your target is, then this article is for you. 

It is understandable that it is relatively easier to follow market trends or preferences based on the experiences one has. But the truth is that an efficient Marketeer, nowadays, must “be familiarized” with the numbers, the analyses, and the technology. In fact, Digital Marketing has in its genesis the possibility of defining KPIs and measuring the results (by testing feedback) to make decisions.

Marta Miranda
– Branded Content Specialist & Producer –

If you usually invest in Digital Marketing content without knowing why or who your target is, then this article is for you. 

It is understandable that it is relatively easier to follow market trends or preferences based on the experiences one has. But the truth is that an efficient Marketeer, nowadays, must “be familiarized” with the numbers, the analyses, and the technology. In fact, Digital Marketing has in its genesis the possibility of defining KPIs and measuring the results (by testing feedback) to make decisions.

Incidentally, we are increasingly experiencing a “trial-error” reality that is massively impacted by data generation, which allow, more precisely, to delineate inbound strategies, improve content production and optimize sales.

It is not surprising, therefore, that the creative and communication strategies of digital agencies are increasingly based on the data collected from different channels of a brand. Data is the protagonist of the current reality of Digital Transformation; it is that which allows, for example, both an analysis of the sales volume of an e-commerce product, or the sending of emails to a contact list. It is the data that allow the analyses of behaviors and trends and, consequently, the promotion of services, products and campaigns with segments of people. In fact, 61% of consumers in 2018 already expected brands to be able to customize the experiences they offer according to their preferences (source: Think with Google study, 2018).

Source: Marketoonist.com

Let’s start with these two concepts

In other words, empirical facts should not overlap with information extracted from mathematical algorithms – or data source – to implement solutions and help a company grow. 

In this universe of Marketing that has as its central axis data orientation there are two concepts to keep in mind: Big Data and Data-Driven, i.e., respectively, analyze and interpret the set of information present in the voluminous databases of servers and companies, turn them into useful information and, based on this processing and knowledge of the data, make decisions, and define strategies to improve outcomes. 

Youtube, the streaming app like Spotify and Wikipedia are examples of Big Data at the online level, now com videos, music, or texts, and databases are available for users’ access. The customer journey or, more specifically, user’s webpages while surfing the Internet are also part of the Big Data dataset

Why use the data?

The Data-Driven culture allows you, roughly, to manage big data in the brand universe. It allows you to monitor data on traffic acquisition, interaction of visitors on the website and apps – as well as in physical environments – and on the patterns of behavior against the different sales acquisition channels, making it clearer which strategies to adopt in the market (do we bet more on promotions or new product launches?). 

With the insights obtained by the integration of this data it is possible: to make market analyses (segmentations, benchmarking); to be able to explore new markets; evaluate the health of marketing actions; diagnose problems and seize opportunities; understand the needs of the consumer and optimize the relevance of its relationship; plan strategies and predict sales. Especially in the face of an accelerated reality of transformations, data reduces uncertainties and gives more security to brands. 

In a way, the data should allow you to transform the information into actions to achieve good results!

We are in what is considered the Golden Age of data, where Intelligence teams can collect abundant data about audiences, through various types of social media platforms and different devices. The data is considered an authentic treasure. And you don’t have to have a large company to make use of the info and have the mindset oriented towards a data-driven culture

Deciding on data implies Data Literacy

However, data-driven management only achieves good results if it is carried out by Marketing Analysts who work with qualified data and specialized tools. Data Literacy is very important in this area of Analytics: knowing how to read and understand algorithms, to be useful information for a business strategy, which can mean trying, also, to challenge your results. The assessment of Artificial Intelligence (AI) – the expanded science of reproducing human capabilities – implicit in a data-driven methodology, depends on human intelligence. 

Data-based marketing then needs a human component with skills to be impartial, objective and guide analysis in relation to the scenario of a specific business. In fact, contrary to what might have been understood so far in this text, data can also misrepresent realities.

There is a controversial example that illustrates the importance of the source of the data that feeds the algorithms: Amazon’s attempt in 2018 to use AI to optimize the selection process of applications through the Human Resources area. However, programmers, without proper development and a critical mindset, placed biased data, including the very specific characteristics of candidates who had been recruited many years ago, such as being male, Caucasian and with an academic degree associated with one of the best universities in the United States.

The Algorithm of the AI model began to reject, therefor all other great resumés that did not fit the profile. They naturally aborted the model as soon as the error was discovered. 

Out of curiosity, the search for Analytics Professionals or “Data Scientists” is increasingly evident and a study by Marketing Week Career and salary survey even revealed that it is the Career of the Future (source: Website Marketing Week). 

So how do you collect and analyze data?

Data Marketing requires you to purchase tools and software for data analysis. There are several types of data that can be collected and with a lot of potential to be exploited. Primary data – also known as first party data – are typically differentiated from secondary data for market analysis. 

The primary data are those that are collected for the first time by the means of the company itself; the secondary have been searched by other organizations and are accessible for consultation. Secondary data turn out to be those with less interest for those who want to apply a differentiated Marketing strategy, because they relate to general characteristics and behaviors of the population (and not the audience of a brand), do not respond specifically to a problem of research of a brand and hinder autonomy in monitoring data over time (control of the publication of data is,  after all, in the hands of third parties).

These secondary data are basically those that are consulted by desk research – through the press, advertising agencies (Think with Google, LinkedIn Business, Socialbakers, McKinsey, etc.), government organizations, academic papers, etc. 

Let’s focus on first party data

There are several primary data collection methods, from the more traditional to the most technologically advanced tools. The best-known primary research methods are, for example, interviews, questionnaires, focus groups, or participant observation (face-to-face or online). 

When it comes to online tools and software for data collection and analysis, there are options that allow for very interesting insights (including some of them free as Google Analytics):

  • Web analytics: the collection of user data on websites takes place through cookies – Google Analytics, Google Trends, SEMrush (SEO analysis), among others;
  • Heat maps and screen recordings: tools like Hotjar that record user behavior on websites (which areas do they access the most?);
  • Social analytics: relate to the data of users of social networks, extracted for example by Hootsuite Analytics, Facebook Adds, Instagram Insights;
  • CRM:  Customer Relationship Management software that allows the automation of processes of the relationship with the customer;
  • Mention monitoring or Social Listening: how brands are mentioned on the web and on social networks, analyzed for example by Buzzmonitor and Scup;
  • Business Intelligence (BI) tools using AI: new AI approaches such as Machine Learning with platforms that can structure information and automation of marketing email, social networking, ads, analytics tools, marketing automation software and chatbots – Google Data Studio, Microsoft Power BI, Tableau, SAS Intelligence Business, Adobe Analytics, Buzzmonitor (Social Business Intelligence), etc. 

It should be noted that Machine Learning takes advantage of the great data of the web, hence being closely related to the universe of Digital Marketing.  They are software with Machine Learning that allow, for example, to recommend (autonomously without human interference) suggestions to users of Netflix or Spotify (uses AI to recognize patterns and understand the preferences of users within the analyzed data), direct emails to spam, impact on Instagram with ads that seem to have read our mind, personalize communication with each client, among others. In Machine Learning there is a much superior interpretation capability than ours. 

In general, all data (whether primary or secondary) must be well organized to be properly evaluated and this is crucial from the moment of its extraction that arises from various sources and turns out to be uninterrupted. The objectives of data collection and analysis should be understood; for example, if a brand wants to boost your brand awareness, then you should prioritize the analysis of reach metrics, evaluate your position in Google results, etc.

The duty to protect data 

The opportunities that arose with Digital Transformation for the processing of so much data have also made room for the misuse of these by large companies. Therefore, a law has emerged to regulate and protect data worldwide (in Portugal, the General Data Protection Regulation – GDPR, since May 2018), so that they can only be used with consent and can also be private, forcing companies to be more transparent and act responsibly. 

This will lead to the marketing strategies end up being more directed to those who are really interested, since it only authorizes the consensual use of personal information, of whom is really interested.

The cookie controversy 

Cookies, as stated earlier, are the codes that a website collects when receiving a user’s visit. But they’re not all “the same.” First-party cookies may be those related to language preferences, payment methods, cart products, among others; it is users who give this data to the company or may be inferred by users’ behaviors.

They have materialized very useful information for marketing professionals, since they allow the activity of users on the site to be understood (for example, the number of clicks, pages visited and conversions) and thus improve the experiences.

Source: Google Images

There are also zero party-cookies, which are the data declared with explicit information and intentionally provided by users. 

However, third-party cookies (or third-party cookies) are created by a domain other than the one the user visits, being widely used for advertising and marketing purposes. It is this data that supports marketing automation tools: it allows you to monitor user navigation between different sites (cross-site tracking), target retargeting ads (based on previous behavior patterns) and communicate ads on different sites optimally (ad serving).

And it is the cookies of third parties that are being controversial for questioning the right of users’ privacy, leading the world’s leading authorities to follow new protocols.  For this reason,  the biggest players on the web, such as Google and Apple, are already announcing updates to offer more privacy to the consumer, which means “killing” browser cookies as we know them in the digital marketing ecosystem. Safari and Firefox have already blocked them. Apple has already begun to move away from the universe of third-party cookies with the release of iOS 14, which already requires permission to track the data. 

The end of the era of third-party cookies thus means that marketing strategies start to be more transparent and bet more on first-party data – in creating their own channels (e-mail marketing, content  such as e-books, social media surveys, organic traffic, SEO) – to better understand their audiences and encourage their confidence to “contribute” with data. 

It is true that online advertising seems to be compromised, from the moment the investment of brands in ads can be questioned, whose data becomes less specific. But it’s also an opportunity for brands to stay close to their customers, without compromising the protection of their data, and building better content experiences! 

 Big Data Insights Turn into Big Ideas

With today’s technology and precisely in big data times, it’s easier for brands to gather enriching insights from first-party data to analyze and drive content strategies. All they need is creativity and quality at the Content Marketing level to impact a lead, or a potential consumer who has already shown interest in the brand. In other words, Data Marketing guides Content Marketing to be more efficient: planning (such as the definition of the target pubic), the production and communication (such as the choice of communication channels) of content is based on the information analyzed by the data. 

Are we producing content that responds to market trends and people’s questions or problems? Which are the best channels for our brand to communicate – blog, Instagram, Pinterest, email marketing? What impact has the content we have communicated? What have been the competitor’s strategies? 

With the orientation of the data, the probabilities of the content will be increased in the interest of the target audience. It’s really important to create the right content formats to leverage engagement at every step of the funnel along the consumer journey.

The engagement is then achieved through the delivery of impactful, engaging and relevant experiences to consumers, taking into account the stage of the journey in which they are and the communication channel where they are. Content guided by data-driven culture follows persona-oriented strategies focused  on achieving results. The icing on the cake is effectively achieving the goals of Inbound Marketing and leading to conversion.

Let’s look at practical examples of how Data can inspire Content Marketing?

Cosmetic #KillerSkin Olay campaign with actress Sarah Michelle Geller for the Super Bowl
Source: filmow.com

 

If you were a cosmetic brand and had to create an ad for skin care, would you think and invest in the celebrated Super Bowl event to communicate it? But the Olay brand wanted to invest in this event, after analyzing the data of its target audience and finding that in addition to having an interest in skin care, they also liked football and horror movies. This research resulted insight into the “Killer Skin”
campaign with a horror storytelling -protagonized by the actress known for the horror films in which she participated, Sarah Michelle Gellar – to be communicated at the Super Bowl.

Another example of the insights big data can give content marketing is called data storytelling.  That is nothing more than using user data as being the communication content itself, not only for personalized online communication for each client, but also for the one that is done in traditional media. One of the best-known examples of Data Storytelling is based on Spotify’s “Wrapped” campaign (which began in 2016), which communicates the consumption patterns it has identified: it features the most listened to artists, songs and podcasts of the year.

In conclusion, I think it is obvious that digital ecosystem only has to gain from the extraction and analysis of data, since it “risks” getting it right with the objectives of a brand (acquiring more traffic and retaining customers), having a differential and competitive positioning in the market and being able to identify more opportunities.

The digital age will always continue to advance, data regardless of sources will continue to be generated and, above all, people will always be the main protagonists, whether acting in the Backoffice of a business ( such as Data Marketing Analysts or Content Marketing Professionals), or as the persona of a brand.

 
Spotify streaming app ads based on Data Storytelling 
Source images: Ads.spotify.com