What is a data-driven marketing strategy and for what is it used?
A data-driven marketing strategy is based on profound data analytics.
This includes analysis of data you already have as well as data that is accessible to you as a marketer. By analyzing various data parts, you can start making data-driven decisions and forming the baseline of your data-driven marketing strategy.
Most often, the application of a data-driven marketing strategy helps companies and entrepreneurs gain insights into future numbers (i.e. forecasts/predictive analytics), but also helps to personalize the user experience for existing and future customers. It allows companies to better understand customer needs and preferences.
And lastly, when it comes to the marketing budget, a data-driven marketing strategy helps to identify and predict which marketing efforts have the highest and most profitable impact or return on investment. A data-driven decision making helps companies identify marketing channels that work the best for your business.
What are the benefits of implementing data-driven decisions in marketing?
With the help of data-driven marketing decisions, you can minimize marketing expenses by knowing which channel and/or tactic works the best for reaching a goal by analysing and comparing the various relevant KPIs. Your decision won’t be based on gut feeling anymore but on effective profound numbers and their statistical.
More precise/Better user personas.
Thanks to continuous data analytics, you are able to develop a deeper knowledge of your buyer personas and keep them up-to-date in order to understand the real needs of your customers. This again helps marketers to create more personalized content for their target audience and get a better return on investment at the end of the day.
With the implementation of artificial intelligence data analytics processes will become more and more precise over time.
Smart planning with predictive analytics.
As a result of a data-driven marketing strategy, implemented with the right technology, digital marketers are able to predict certain patterns and evolutions in their marketing activities. With predictive analytics, marketers can identify possible future behaviours of their campaigns and customers.
Especially useful for marketers is predicting future campaign success or customer purchase values using predictive analytics tools.
As an example: with predictive analytics, based on location, age, employment, and purchase history, possible future purchase values can be identified as well as the potential customer lifetime value (CLV). With this information, marketers are able to target specific users with ads, content and/or offers that attract those customers the most.
Find relationships with correlations.
Finding relations and causalities in your data is hard. By applying, marketers are able to see which metric is driving which other metric. By correlating important and relevant KPIs with each other, marketers are able to clearly see and understand the output of their marketing efforts and get a better holistic view of their metrics and the relationship between them.
What should you take into account before implementing a data-driven marketing strategy?
It is, of course, almost impossible to gather and track all the data from all various marketing tools manually. This would require so much time that there would not be time left for analysing the data properly or actually doing any other marketing activities. Therefore, finding the right technology that can do this manual work for marketing teams comes as the next task.
Digital Marketers usually use 10-15 marketing tools simultaneously, such as Mailing, Social Media, Customer Relationship Management, and Website Traffic Analytics tools just to name a few. Various data can be found across all these tools, but an overview of the overall marketing performance is missing since most of the time these tools are not synchronized.
There are already a few marketing intelligence platforms available that will do that work for you.
It is clear that modern digital marketers must be data-savvy, but more importantly, they must work hand-in-hand with both analysts and data scientists. The collaboration between analysts, marketers, and data scientists brings efficiency and accuracy into decision-making processes in marketing departments. Effective data-driven marketing requires collaboration between all the departments.
Having good technology and a qualified team will not produce good results without high-quality data. This means that you need to be careful when collecting data to ensure that it is precise and correct. What data and data-provider are related to that varies, depending on your business and your digital marketing channels.
Therefore, understanding what type of data is important for your business will bring a lot of clarity into your data-driven decision making process and support your data-driven marketing strategy.
Selecting your Key Performance Indicators for your business is very important for a successful data-driven decision culture and strategy. Therefore, it is important to take some time and find the most important metrics for your business in general or for your next marketing campaign.
As an example, we have listed a few common KPIs that play an important role in a digital marketing campaign or strategy and which we often see used by our customers:
- Historical customer data
After gathering and analysing all the relevant data, you have the chance to actually use technology to monitor your KPIs as well as other relevant numbers. When doing so, you will be notified if surprising positive or negative anomalies have happened and what might have caused those anomalies. This helps to identify possible failures in the processes and give you more time to focus on your running campaigns rather than continuous monitoring of thousands of metrics.
Today we addressed such an important topic as data-driven decision making in marketing. We are happy to share with you more relevant tips for improving your marketing performance!
Be proactive and learn how to create an AI model and how to make your data ready for AI with simple steps in our next articles!