In the past, large data volumes weren’t just a challenge for marketers to deal with. While data mining made it possible to process older data, it required manual analysis and was only able to offer limited insights on the gathered information. Recent technical developments, however, have made it possible to process data at increasingly faster rates, so automatically analysing data is no longer a problem in the age of Big Data. In addition to these trends, more and more consumers are coming into contact with a growing number of digital contact points (i.e. Like buttons, tweets, etc.), which produce even larger sums of data and contain valuable information. Data-driven online marketing exploits these developments, allowing data to be interpreted within different contexts; it also helps marketers recognise their chances among potential customers or client bases and find out how corresponding marketing efforts should be adjusted to reach these individuals.
Data-driven marketing essentially refers to any marketing effort that employs strategies developed from information gathered from consumer data sets. The marketing discipline came about through the influence of different business developments. In addition to online marketing, both sales as well as customer care also present important components of the strategy. In the past, these three disciplines were also dedicated to gaining insights from data sets to optimise business operations. Now, with data-driven digital marketing, the difference is that data sets are used primarily to help companies or brands understand how their image is perceived by customers, rather than to use this data to optimise day-to-day operations. The goal here is to better reach target groups, carving out a more positive reputation and obtaining a long-term connection in the process.
Digital transformation has helped create a world in which users leave behind trail of information wherever they go. Companies are able to gather this information and use it for themselves. Following this, it’s easy to understand why more and more marketers are beginning to refer to data as the new currency in the digital age. Collecting customer data—Big Data—is also a fundamental aspect of data-driven marketing. Some of the most relevant data includes:
Data analyses make up the core of data-driven online marketing. These help make sense of enormous stacks of data and help recognise patterns, such as a user’s click behaviour. Data analyses help facilitate the use of different data models and algorithms, giving structure to the data and recognising correlation.
Analyses help marketers make predictions on the future purchasing behaviuor of users based on their current search behaviour. Doing this helps create a clear advantage, as correctly using data allows companies and businesses to better understand their customers. Knowing the needs, wishes, and expectations of customers also generates better-matched products and services. The structured gathering, evaluation, and interpretation of data is crucial for a business to succeed and reach out to its customers.
The whole process depends on solid planning and cooperation between data scientists, who extract relevant information from available data with the help of analysis tools, and the campaign’s corresponding marketing team. Together, these two groups have to answer relevant questions, such as:
The shared tasks between the data scientists and the marketing team is to evaluate the flood of incoming data and to visualise facts gathered from this information in a user-friendly way; it’s important to make sure that the most important details remain present in these depictions.
The main goal of data-driven marketing is to better understand customer behaviouir and to remain informed of all current events. Trends, short or long-term changes in purchasing behaviour, or a general change in a brand’s perception can be monitored with the help of this marketing method. Those who are able to make quick use of this information and change their approaches do more than just increase customer loyalty; ultimately they also increase turnover. By sifting through raw data and filtering out the latest trends and specific plans for action, marketing teams are able to spare themselves a considerable amount of work.
Many marketers are familiar with losing potential customers that may have shown interest in the past, for example by visiting a site or even filling up a shopping cart, but fail to return. But which inactive customers can be won back? Analysing contact points helps marketers obtain information about their company’s relationship to their customers. When properly timed, contact points that have been neglected for longer periods of time can be reactivated by implementing measures to personally address lost customers, potentially rekindling the customer relationship in the process.
When we shop online, book holidays, and search for gift ideas, we hardly give a second thought to the fact that each search entry leaves behind a trail of our identity. Busy web bots are never far behind and sweep up this information. The result of all of this is Big Data: massive volumes of data that is analysed and used for a variety of reasons. But is there reason to be wary of leaving behind...
The total amount of data generated worldwide increases by 40% every year. Many companies seek to make use of the constantly increasing mountain of data in order to increase their e-commerce business. But making use of such Big Data alone doesn’t add any value — enter data mining. Below you can find a discussion of the various analysis approaches involved in data mining, to give you an idea of how...
Data analysis is a purely theoretical matter for most people. But in order to provide the customer with information, it’s important to be able to visualise sets of data. With Google Data Studio, you can summarise data from different sources into one handy report. Our guide gives you an overview of Google’s latest offering.
In the digital age, it’s normal for data from small and medium-sized companies to grow to unmanageable amounts. Data mining tools are used to extract desired information from data sets. These extract recurring patterns from the data and make them available to marketers and statisticians. We have compared the most important data mining software and show where their strengths and weaknesses lie.
Sentiment analysis is a process that involves automatic opinion detection. The method analyses text to find out what people think about a certain product or brand. Using the insights gained from sentiment analysis, marketing campaigns can be steered more systematically. Sentiment analysis also uses text mining tools to analyse product reviews and comments on social media.
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