Data-Driven Marketing: From Big Data To Smart Data

Large amounts of data used to be a challenge not only in marketing. So-called data mining only allowed tedious processing of data from the past, required manual analysis, and provided little valuable insight. In the meantime, technical developments have made it possible to process data faster and faster. A fully automatic analysis in real-time is not a problem in times of big data. Apart from that, more and more digital contact points with consumers mean that more significant amounts of data are being collected, containing profound and valuable information. Data-driven digital marketing uses precisely these developments to correctly interpret data in the marketing context, to identify potential, and to be able to control measures better.

Table of Contents

What Is Data-Driven Marketing?

Translated, data-driven marketing means something like data-driven marketing. The term refers to all measures that use the knowledge gained from data sets to develop and adapt marketing strategies. The marketing discipline arose from the influence of various developments in companies. In addition to online marketing, sales and customer care come into play here. All three fields have already obtained knowledge from databases in the past, based on which processes were optimized and important internal decisions were made.

Optimizing processes and sustainable resource planning, e.g., purchasing, was the focus. With data-driven digital marketing, extensive databases are now used, which are primarily relevant for the perception and image of a brand or a company and less for the operational processes in the company. The aim is to align marketing measures to the target group better and thus achieve a positive perception and long-term customer loyalty.

The Basis: Data, Data, Data

Digital transformation has ensured that information is left behind everywhere, anytime. Companies can collect these and use them for themselves. People talk about data as the new gold in the age of digitization. Collecting customer data -big data- is also an essential part of data-driven marketing. The following are relevant:

Demographic data: general information about groups of people such as age, gender, place of residence, and social characteristics (occupation, marital status, income) that help to record the overall picture of the target group
Behavioral data: derived from the web analysis and output in so-called KPIs (Key Performance Indicators), e.g., length of stay, user path, bounce rate
Qualitative customer statements: are voluntarily provided data, e.g., B. were collected via telephone or online questionnaires.

The Core: Analysis & Evaluation

The core of data-driven digital marketing is accurate data analysis. Only through them can the enormous amounts of data be used in a meaningful way and patterns, e.g., in the user’s click behavior. This is made possible by various data models and algorithms that give the data structure and allow connections to be identified. Marketers use forward-looking analyzes to conclude their future purchasing behavior from the current surfing behavior of the users. This gives you a clear competitive advantage: Those who use data correctly understand their potential customers better.

And if you know their needs, wishes, and expectations, you can better tailor your products or services to them. The structured collection, evaluation, and interpretation of data are ultimately decisive for good customer dialogue and corporate success. The whole thing only works with solid planning and coordination between the so-called data scientists, who use analysis tools to obtain relevant information from the existing data, and the responsible marketing team. Together they must answer all pertinent questions, e.g., e.g.:

What is the starting point, and what data is available?
What connections are we looking for, and what analyzes do we need for this?
What value do the possible outcomes have for the company?
What effort is behind it?
Is the relationship economic enough?

The typical task is then to control the flood of data and visualize all relevant facts user-friendly without any loss of information. Automated analyzes and intelligent segmentation ensure efficient processes.

Goals Of Data-Driven Marketing

The main goal of data-driven digital marketing is to understand customer behavior and stay up to date with all current events. Trends and currents, short- or long-term changes in purchasing behavior, or, in general, changed brand perception can always be kept in view. Those who react promptly not only strengthen customer loyalty and customer relationships but ultimately increase sales. Reading out acute trends and targeted recommendations for action from a confusing mass of raw data makes marketing work much more accessible.

Example: Finding The Right Content

In data-driven content marketing, the right messages are always important. If you want to attract the customer’s attention, you do so with relevant and valuable content. The proper analysis of data-driven marketing finds out the target group’s interests. This makes it much easier to select the correct range and play it out with the ideal target group approach.

Example: Recapturing Lost Customers

Many marketers are familiar with the problem of “lost customers”: potential customers who have shown interest and may have already filled their shopping cart while staying away. But which inactive customers can still be won back? The analysis of contact points provides information about customer relationship quality. During prolonged inactivity via these contact points, you can intervene in good time and reactivate the customer relationship through targeted, personalized addressing.