Large amounts of data, called Big Data, have always been a challenge, both in the marketing field and in other areas of the Web. Data mining has facilitated the time-consuming data processing of these data mountains, which required manual analysis and provided little relevant information. Since then, technical advances have made it possible to analyze these data much more quickly.
A comprehensive, automated real-time analysis is now possible for Big Data. In addition, the multiplication of online contact points with consumers has continually increased the amount of data collected, which is characterized by their relevance. Data-Driven digital marketing uses this progress to interpret this data in a marketing context, to identify opportunities and implement more effective strategies. The databases are thus interpreted with higher speed and in real-time, which allows a high reactivity.
What is Data-Driven Marketing?
Data-Driven marketing refers to all the marketing measures that have been taken from the knowledge gathered via the data of Internet users. This branch of marketing has emerged as a result of various developments within companies.
In the field of online marketing, Data-Driven marketing concerns only Big Data (i.e., very large scale computer data sets, as well as their uses). The objective is to optimize marketing measures according to the target groups and also helps to improve the image of the brand and its popularity by strengthening the dialogue between the company and the customers.
In addition to online marketing, customer relationship management plays a growing role. These marketing disciplines have been developed with the aim to improve knowledge gained from customer data. The process of obtaining the data has been optimized, as well as the planning of resources over the long term (e.g., in the field of sales).
An Innumerable Amount of Data
The digital transformation has contributed to the fact that Internet users always leave traces while surfing the Web. This accumulation of data is often referred to as the new black gold of the 21st century. The companies knew how to collect them and put the Big Data to profit. This is an important aspect of data-driven marketing. The main aspects of data-driven marketing are:
- Demographic data: general information on visitor groups such as age, place of residence, socio-professional category, family status, etc. These criteria help define a target group.
- Behavioural Data: This data is collected through web analysis and translated into KPIs (Key Performance Indicators). These data include, for example, the average length of time a user visits a site, the bounce rate, or the journey of visitors on a site.
- Qualitative surveys: These data are made available to companies via telephone satisfaction surveys or on-line questionnaires.
- Market surveys: The databases thus created make it possible to define the territory and identity of the brand, but also to better understand the highly competitive environment of the company and the sector in which it operates.
The Core: Analysis
The core of Data-Driven Web marketing is based on accurate data analysis. Performance indicators such as user click-through rate are relevant only when a lot of data has been collected. This makes it possible to draw up several schemes and to define algorithms which give them meaning afterwards. Once these data have been analyzed, marketers can determine the future purchasing behaviour of consumers from their visiting itinerary on the site.
It is an opportunity to take the lead in relation to competition: for those who know how to use the data adequately, the expectations of customers and prospects can be better understood and anticipated. Companies that understand the needs, wishes and requirements of their customers can better adapt their product or service offerings.
Information interpretation is the prerequisite for a successful and enriching dialogue with customers, and therefore a key success factor. But the expected results can only be achieved with the implementation of sound planning and through the services of data scientists and industry professionals. The data scientists can, collect valuable information with the appropriate tools and convey the information to the marketing department. These data need to answer the questions below:
- What are the forecasts?
- What data has been used?
- What are the logical links between these datasets?
- What conclusions can be drawn from these analyzes?
- How can they be exploited commercially?
- How are these results compared to the company?
- What strategies can be implemented?
The main task is to control the flow of data and all the different factors that come into play, without omitting information. It should also make accessible information in their presentation and get to the point. This requires excellent analytical skills and mastery of automatic analysis and segmentation tools, for a fluid process.
The Objectives of the Data-Driven Web Marketing
The main objective of Data-Driven Web marketing is to understand the behaviour of users and to anticipate their desires. The resulting studies also make it possible to remain constantly up to date on the sector. Thus, each type of queries carried out by the Internet surfers can be a way of reflection for the actors of the marketing.
Being responsive not only increases customer loyalty and customer relationship, but also increases long-term revenue. By learning from masses of raw data, it is possible to implement effective and targeted marketing strategies. On the one hand, there are the upstream studies that are carried out from the databases, and on the other side downstream, the marketing mix that will define the strategies of the company.
Example 1: Determining the appropriate marketing content
With Data-Driven content marketing, it is possible to optimally adjust its communication and the relevance of messages to the target. When a company wants to attract the attention of its customers, it is necessary to propose content that offers a real added value. An elaborate analysis of the Big Data makes it possible to determine the interests of the different target groups of the company, and facilitates the choice of the type of content to be offered while adapting the tone, the style, the format and the means of communication.
Example 2: Customer loyalty
The problem raised by “lost customers” is common for marketers, especially when potential customers have shown interest, or even already filled a basket but interrupted their purchases after. Which passive customers can still be reconquered? Thanks to the analysis of the contact points, it is possible to determine the quality of the customer relationship. When a prolonged period of inactivity is detected, it is possible to intervene in time and to re-establish a personal dialogue with the client.