The pandemic has encouraged many businesses to start their digital marketing journeys. For many who are still new to digital marketing, backing strategies with digital data is a learning curve. Contrary to common belief, there are still millions of businesses that have failed to nurture an online presence until the start of the pandemic. While some already had a website or even social media profiles, onsite interactions made up the majority of their revenues. As such, the rediscovery of the digital platform and its potential is the cause for most of the recent data marketing mistakes. 

 

 

#1. Not segmenting your data enough

 

Collecting customer data is the starting point of every digital marketing campaign. However, it’s important to collect in-depth data to ensure that your activities are relevant to your customers’ needs. A business that focuses only on high-level data is likely to create spammy campaigns that will not match their audience’s interest. You want to make sure you can create individual audience segments to target the right contacts with the most relevant information. Ultimately, if you get in touch with useless information, your customers will lose interest in the brand. 

 

#2. Not updating your data

 

Where did you live 5 years ago? 

What was your household budget then?

And more important, how does it compare to your situation now? Most people change their address, jobs, finances, family situations, and priorities over the years. Therefore if you can’t maintain your CRM up-to-date, you are losing money. When manual update doesn’t make sense, businesses need to invest in an intelligent system that can do the hard work for them. The game-changer solution in data marketing is revenue intelligence, learn more about this AI-driven, data-centric princess that tracks and updates data changes. 

 

#3. Confusing big data with deep data

 

Capturing all the available data about your customers is not always the best approach. Excessive data volume is counterproductive. It is a horizontal process that gathers vast quantities for analyzing, without considering which data is going to be relevant. On the other hand, deep data is about diving at a granular level to gain a vertical overview of your audience and market. This provides a rich stream of information that can be repurposed strategically. 

 

#4. Forgetting the right to be forgotten

 

The GDPR in the EU allows users to get in touch with a company or an institution and demand to be removed from the database. The right to be forgotten means that businesses need to be prepared to remove data from their system at any time when it is legally meaningful to do so. In the long term, we can expect similar regulations to appear all around the world. Businesses that fail to prepare for such eventuality will face consequences for the way they use their data. 

 

#5. Assuming rather than analyzing

 

Data marketing is a process that seeks to get rid of the assumptions a business makes and replace these with data-driven hypotheses. Unfortunately, most people make assumptions daily. As assumptions turn into personal convictions, they tend to replace data knowledge. In the long term, this can lead to chaotic marketing strategies. 

 

If you collect data as part of your business processes, you need to establish a productive and meaningful approach to utilizing those data. The process is not about defining areas of improvement for your marketing budget, but truly identifying the areas where you are unintentionally misusing data.