Big Data marketing: how to integrate it into your content strategy

The digitalization phenomenon is generating new advertising practices, including data marketing, which is based on the collection and analysis of customer data. Exploiting big data allows the deployment of more targeted marketing campaigns that improve the customer experience. Delivering the right message to the right target is now a challenge for all brands wishing to optimize the effectiveness of their campaigns.

What is big data marketing?

Data marketing is the art of collecting and using data to build your marketing strategy. It consists of understanding the reasons for consumer behavior and action patterns in order to implement rational and intelligent marketing decisions.

How does it work?

The collection, extraction, analysis and interpretation of consumer data are the foundations of any data marketing strategy.

Data collection must be of high quality, because quantity alone will not make the difference. It will be necessary to make databases more reliable and to remove elements that are of no interest, harmful or worthless.

The customer data collected must be qualified according to demographic, socio-cultural or behavioral criteria to enable the development of effective strategies. Subsequently, the information collected and characterized can serve as a basis for the personalization and automation of marketing communications.

However, the use of personal data is not trivial. On the one hand, it is necessary to comply with the GDPR, which came into force in 2018 and includes a set of rules related to the protection of consumer privacy. On the other hand, Google has indicated in its communications the deletion of third-party cookies by the end of 2024, which will complicate the collection of data for companies.

The challenges

Big data is the opportunity to know in detail the customers of a brand. Once done, the company can personalize its customer experience, the products it offers and the content it publishes. Similarly, collecting and analyzing data makes it possible to optimize user journeys by refining targeted audiences.

The detailed knowledge of a brand's audiences allows the personalization of communications according to the identity of its target, its needs, its expectations, its habits and its history.

The development of digital has also generated a multiplication of communication channels available to companies. They can select the optimal channel for the transmission of their messages.

In addition, data makes it easier to calculate the ROI of marketing campaigns. The data collected may indeed concern the number of new customers as well as their satisfaction. Data marketing will therefore make it possible to best allocate the sums allocated to campaigns, to do more with the same means.

Finally, the analysis of customer feedback and behavior will push the brand along the path of continuous improvement of its products and services.

How to implement a big data marketing strategy?

Set goals

It is essential to set objectives for data collection. These objectives will be used to know what data is necessary to collect to achieve them. They must therefore be defined, qualified and prioritized both in the short and the long term. They will also identify how the information collected will be used.

Define buyer personas

To deploy an effective marketing campaign, a brand must start by defining its targets. A company's buyer personas are the ideal customers it wants to reach. Their definition pursues the same goals as that of the objectives: to identify the useful data to be collected and to know how to use them.

Collect data

An essential step in the process is setting up the collection of information that will be used later. In this area, it is necessary to favor quality over quantity. The data to be obtained are of three kinds:

• Data related to defined buyer personas.
• Those about competition.
• Those that will be used to analyze the success of data marketing campaigns.

To collect them, the company may use several methods depending on the level of access to each category of data:

• The available data is to be retrieved within the various departments of the company.
• The data to be collected requires defining an appropriate collection method.

Use the right tool

The collection and processing of data require the use of high-performance technological tools that can be used by all of the company's departments. Indeed, data marketing impacts different trades such as sales, after-sales or marketing.

The choice of tool, made difficult by the profusion of options available, must be made according to the data to be managed, the objectives to be achieved and the campaigns envisaged.

Create targeted content

The content strategy to be put in place is based on a four-part premise: publishing the right content, to the right consumer, at the right time and on the optimum channel. To do this, the most favorable method is not to accumulate a mass of different content, but to publish quality content. It is also essential to vary the edited formats to reach its target in its preferences.

By creating curative content, the brand first wishes to attract and seduce consumers, even before pleasing search engines or optimizing its natural referencing. It will therefore have to publish accessible and engaging articles, with attractive visuals that will generate consumer attachment or allow them to identify with the brand.

Test, measure, analyze and repeat

The data is used at all stages of the marketing campaign, from identifying the target to analyzing its performance, including defining the content to publish. To know if the target and the objectives have been achieved, it is necessary to carry out tests such as AB testing, to analyze them and then to repeat the operation.

Defining relevant KPIs will make reviewing the success of the chosen scenarios simpler and more explicit. The possibilities of choosing KPIs are multiple and must be correlated with the objectives chosen for the data marketing campaign.

Big Data marketing works in a loop, strategies must be defined, implemented, analyzed, improved and repeated with the objective of continuous improvement of the process.

Example of data-driven marketing campaigns: Spotify and its content contextualization

Spotify leveraged data from its own customers to create a personalized content campaign. For each geographical area, the content was based on the existing and geolocated audience. The campaign here uses internal data collected in the various departments of the company on the brand's customers. Spotify was thus able to offer its content in 14 countries while differentiating it according to the target audience.