Data-Driven Marketing – With Data to Success

In the context of Big Data, there is a myriad of information across all channels that can be used to improve performance. The collection and use of data on different platforms is not a new process and is already used in many ways. However, data-driven marketing goes a step further, as it involves the holistic aggregation and interpretation of all data. The following article discusses how data-driven marketing works and the challenges it faces.

What’s behind Data-Driven Marketing?

Data-driven marketing (DDM) involves collecting, analyzing, and interpreting data so that it can then be used in marketing. The corresponding information is collected along the customer journey at the various touchpoints.

The use of data should provide new insights into the behavior of users. In the next step, this will allow better decisions to be made when designing new campaigns or optimizing existing ones. DDM can also be used to run automated campaigns that are just as targeted and personalized. The overall aim is to improve the customer experience and, consequently, to strengthen customer loyalty.

How does data-driven marketing work?

Data-driven marketing follows about four steps: First, the required data is collected and, in the next step, combined and analyzed. The insights gained can be used to implement strategies, the success of which is then measured.

1. collect data

As described above, data is collected using various tools and solutions at the touchpoints of the customer journey. Data from your own website can be analyzed using web analytics tools such as Google Analytics. Here, key performance indicators (KPIs) such as page views, dwell time, and general traffic can be collected. There are also similar systems for social media channels. These analyze numerous KPIs, but additionally provide information about different trends and developments.
If marketing automation tools are in use, this data can also be very helpful. Last but not least, information from a customer relationship management system is also highly relevant.

2. merge and analyze data

The next step is to integrate and unify all data. In order to perform this step more efficiently, the use of machine learning is a good option. A resulting overall view is crucial for success in data-driven marketing.

3. implement strategy

After gaining an overview of all data, the third step is to implement the strategy. Important KPIs should be known and the goals pursued must also be clear to everyone. Here it is now necessary to apply the appropriate forms of marketing according to the data. This can be email marketing, influencer marketing, offline advertising, search engine marketing (SEM) or similar.

4. measure success

In a way, the fourth and last step ties back to the first one and thus closes a kind of cycle. Because this step is again about decisive KPIs. Depending on the particular goal selected, the KPIs to be examined vary. For example, it may be the number of newsletter sign-ups or downloads, but impressions can also be important. The corresponding data must be compared with the baseline data to determine whether goals have been achieved. With these results, the process can then be started all over again to optimize corresponding campaigns.

Challenges in Data-Driven Marketing

Even though data-driven marketing offers enormous potential for success, there are some challenges that should not be underestimated:

Obtaining the data alone can become a critical issue, as data protection and privacy must be observed at all times. Customers want transparency, want to know what data is being collected, and want to be able to modify it. This creates some obstacles for companies. In addition, the nature of the data, i.e. its quality, is also a decisive factor. Because only with really valuable and truthful data, success can be achieved in Data-Driven Marketing.

Any data that can be collected must be integrated, correlated and, last but not least, interpreted. Since this data is thereby collected on different platforms, in different campaigns and with diverse tools, this step can be extremely difficult. Even online and offline have to be linked. Accordingly, successful data-driven marketing requires appropriate tools and also employees. In order to always have up-to-date data available and also to be able to adapt it, a dynamic data model should also be used.

In the use of data, the actual connection to people can quickly be lost and processes become automatic. Since marketing is supposed to try to get people excited about a product or service with emotions, this mindset can quickly become a downfall. The responsible employees must therefore constantly remind themselves of the connection between the data and the real customers so that they do not lose sight of this perspective.

Conclusion

Accurate data can be used to identify new developments and trends. Through them, improvement of ongoing campaigns is possible and target groups can be addressed in a targeted manner. If the challenges posed by data-driven marketing are taken into account, customer loyalty can be optimally strengthened and the enormous potential realized.