Data is the most important resource for companies today. The difference is even clearer in marketing. Businesses that use data-driven marketing are able to address their target groups more efficiently – in both B2C and B2B sectors. The main reason for that is optimized dialog with relevant customer segments, based on personalized content. Data-driven marketing can positively influence buying decisions and improve customer loyalty.
What is data-driven marketing?
Web pages, mobile apps, search engines and social media: a lot of data is created during the customer journey. It is not only customers’ actions at numerous digital touchpoints that generate useful information; mentions of specific brands, companies or decision-makers may be illuminating too. Data-driven marketing means letting digital tools and artificial intelligence evaluate this complex information. This facilitates marketing and identification of customer segments, makes the customer journey visible and allows anticipation of buying decisions. Companies can get knowledge about their customers and prospects and use it to focus and exploit content for their company and brand messages in more targeted ways.
Key factors for the success of data-driven marketing
Successful data-driven marketing means understanding the huge potential of this approach compared with traditional marketing communication, in which marketing decisions are based on experience. It also requires a comprehensive understanding of today’s user journeys with their many digital touchpoints. Companies have to be clear about their goals – these could include restructuring a brand, supporting a product launch, expanding target groups or positioning themselves as a “conscious business”. It also requires a knowledge of what data to collect for data-driven marketing and how to collect it.
Eight Best Practices for Data-driven Marketing
There are several tried and tested best practices in data-driven marketing. Companies need to:
- build a skills team covering data analytics, digital strategy, creative/campaign leads and quality;
- start with a smaller project, and feed the resulting learnings into further strategic measures;
- define indices that can be used to measure the success of data-driven marketing measures;
- be aware of the limits of AI in marketing and keep a critical eye on the data at all times;
- continuously monitor data, check the results of AI-generated data and optimize them using regular analysis;
- calibrate results from data tools with data from other sources such as studies and surveys, and supplement and improve the data if necessary;
- adapt the marketing budget in a focused way based on the knowledge acquired;
- communicate the benefits of data-driven marketing to production and sales. These departments can also benefit a lot from ensuring that products and packaging are exactly right for customers and appeal to prospects even more;