It becomes evident, that marketing becomes easily data-driven when you look at data from the strategic level. How do you make most out of your data without becoming a data scientist?
Data is a key component in driving decisions when it comes to marketing strategies targeted at consumer behavior. There is a lot that marketers can learn from data collection that can be helpful in creating a concise customer journey and increasing conversions. However, all this data brings some challenges.
In this article, we will discuss three of the biggest (and most common) marketing data challenges organizations face and share insights on how to solve them.
According to a recent survey, 81% of B2C marketers find it extremely complicated to implement data-driven marketing strategies. Finding what to start with in an attempt to optimize your campaign performance and convert new leads appears to be the key challenge for a marketer. In order to overcome it marketers need to analyze their audience and historical campaign performance data.
Start with a goal in mind – what is it that you want to achieve? Is it sourcing new leads and ensuring growth, or optimizing your campaigns to increase customer engagement and manage their retention?
Whichever option you pick, you should use it as a layer which will help you filter out all data noise that you do not need at that time. Having clear focus will help you find those little things that really determine the key drivers behind your customer behavior. Only when you are equipped with these insights, you will be able to make data-driven decisions that will optimize your campaigns.
Your 1st party data might be limited. Even if you are able to track and combine all the website interactions, campaign engagement and capture loads of demographic data, you might be struggling to draw actionable conclusions out of it.
At first, better data visualization seemed to be an obvious answer to such a challenge. Fancy graphs and dashboards have been replacing tables and sheets in an attempt to understand the generated data better. But marketers have already realized that this is just not enough.
The point is that marketers, who rely only on their 1st party data, are not able to come up with any out of the box solutions. Therefore, they need to capture more details about their audiences.
We are talking here about enriching your CRM datasets with hundreds, if not thousands, of new data points about each customer. Getting 2nd party data or purchasing 3rd party data became really important in today’s marketing world for everyone hoping to stay ahead of the competition by ensuring advanced content personalization and campaign as well as audience optimization.
Consumer behavior is always driven by factors that define their social and economic status. Their purchasing decision-making and frequency will depend on their level of income, which might be further defined and understood by knowing their home value, presence of children and hundreds of other details, which marketers usually do not get as their 1st party data. That is why data enrichment is so important these days.
It sounds easy: find a reliable customer data platform, purchase every data point you can find about your customers and draw conclusions out of it that will help you optimize your campaigns. However, the “easy” part stops as soon as you merge all the data you get into a single directory and attempt to understand it. If you are stubborn enough, it is a matter of time when you will start looking for online courses in data science, but is this really the path you want to take?
Technology is already advanced enough to prevent you from changing your career. Artificial Intelligence is being invoked to comprehend huge data sets and generate insights out of them. All the industry-standard acronyms, such as CDP and DMP, have begun giving way to next-generation solutions, such as CMP, or Customer Modeling Platform.
The reason is simple. Marketers are not data scientists. They need tools to solve all their data challenges quickly so that they could make informed decisions and squeeze more juice out of their campaigns. AI-enabled CMPs are there to do the heavy lifting.
With a Customer Modeling Platform, marketers are able to use AI data models to analyze their data and draw goal-specific insights. More importantly, such platforms enable them to deploy those insights momentarily to their CRMs or campaign management platforms for instant use.
It becomes evident that marketing becomes easily data-driven when you look at data from the strategic rather than the tactical level. Define your goals, make sure you have enough data to achieve them and invoke AI capabilities to take your marketing to the next level.