Marketing in the Age of Big Data

Sociodemographic factors prove to be of little help in measurable, digital campaigns: after all, it all too often doesn't matter whether the buyer of a product is a 60-year-old pensioner from the Grisons or a 14-year-old girl from the city of Zurich. What counts is the behavior behind the purchase decision. If you know the background to the buying behavior, you can better identify potential buyers. After all, similar buying behavior signals interest in the same product. The basis of this type of marketing is structured behavioral data. If the marketer has access to this, the first step towards data-driven marketing and thus the opportunity for higher sales figures has already been taken.

According to Internet research, consumers in Europe receive between 500 and 10,000 advertising messages every day. Wherever the exact number lies: There are too many messages trying to get our attention. On the one hand, there are the marketers, who want to maximize the ROI of their advertising budget, and on the other hand, the consumers, who no longer perceive the individual message because of all the advertising.

In a perfect world, consumers would only be shown information that they need at the moment. For example, that his coffee is coming to an end even before he holds the last capsule in his hand. What counts for the consumer is the individual relevance of the advertising message.

Thanks to the Internet of Things and various methods of analyzing large data sets, it is already possible today to play out individualized messages. The automotive industry is a good example of this: In many car models, thousands of sensors collect data continuously. From this data, car manufacturers can tell, for example, when a particular part will soon need to be replaced. In this way, the local car garage can inform the vehicle owner even before a breakdown that a visit is necessary.

Individualized advertising messages and their channel-specific playout at the right time - right up to real time - offer marketing completely new possibilities. If a message relevant to the consumer is played out at the right time via the right channel, the conversion rate increases significantly. Traditional advertising can't even begin to compete at this level. For example, when a child's birthday is coming up, parents are very receptive to relevant messages and offers. A retailer might obtain this data via a customer card that records the entire family. In this way, he knows the child's dates of birth and his preferences in the product range.

The example shows that behavioral data can be used to identify or forecast affinities and buying potential. As a result, fully automated campaigns can be played out (marketing automation), which are triggered on the basis of a trigger (trigger-based marketing). Complex behavior patterns can also serve as triggers: Through data analysis, it is possible, for example, to identify customers' propensity to abandon and to react to this with automated countermeasures (churn prevention).

Data-driven marketing

A part of one-to-one marketing that provides the basis for strategy and measures on the basis of evaluated data. The evaluated data allows individually relevant information to be played out at the right time on the right channel. Data-driven marketing is also the basis for marketing automation.

Marketing Automation

Method of automating data-based marketing processes. A defined trigger triggers individualized customer journeys on one or more channels (omnichannel). These are played out on the basis of more or less complex algorithms. Behavior and reactions during the journey can be measured and incorporated into the algorithm.

Trigger-based marketing, event-based marketing

In these forms of marketing, defined events (birthday, anniversary, behavioral patterns, etc.) lead to messages being played out. The process mostly ensures temporal relevance for the consumer. For example, if someone finds out about travel insurance on a website, the person is likely to be open to a corresponding offer.

Geofencing

A specific form of event-driven marketing. With geofencing, entering a predefined location is taken as the trigger for the playout of measures. Geofencing requires geo-tracking, which can be used in mobile apps or via specialized services.

Programmatic Advertising

A software-based method of booking, playing and optimizing online advertising automatically and in real time. This involves defining who is to receive the advertising, how high the total budget is, and how much the individual playout may cost. Programmatic advertising is usually implemented with "real time bidding", an automated auction process in real time, whereby the advertising media of the highest bidder are played out. There are also "private deals" in which a fixed price and a fixed inventory are negotiated.

Programmatic Creation

Dynamic generation of advertising media. The advertising adapts visually and in terms of content to the profile of the viewer. This requires modular components that combine differently depending on the viewer and the situation.

One-to-one communication

Refers to the individualized, one-on-one approach in marketing. The recipients are known in each case and are addressed individually and personally. Classic channels are direct mailings and e-mails. Thanks to big data and improved tracking and identification methods, one-to-one communication has become one of the most innovative forms of digital marketing.

Internet of Things (IoT)

Collective term for technologies that enable virtual and physical objects to be networked and interact - for example, by the refrigerator warning when the milk is running out.

Descriptive, Predictive, Prescriptive and Diagnostic Analytics

Analytical methods that enable marketers to draw conclusions from big data and make better decisions.

• Descriptive Analytics: Provides insight into the past and answers the question "What happened?".
• Diagnostic Analytics: looks for associated reasons and answers the question "Why might something have happened?"
• Predictive Analytics: Helps to understand the future and answers the question "What could happen?".
• Prescriptive Analytics: Facilitates decisions using predictions and answers the question "What should we do?".