Modern digital marketing strategies require learning how to leverage several different types of data models to make marketing decisions based on facts and statistics. But this is no easy task, as we’ll soon see.
What makes data modeling so important? Proper data models help marketers visualize the flow of their campaigns, measuring, analyzing, and identifying potential improvements throughout the entire marketing process, from targeting through conversion.
In short, data models provide essential details and actionable insights that can be used to direct marketing strategy and improve ROI.
Those marketers who have embraced data-centric strategies and learned to develop effective data modeling methods are rewarded with better insights about how to improve their campaigns, leading to more efficient spending and better results.
In this article, MatrixPoint’s data modeling experts explain what data models are, what the most popular data models currently in use are, and why it’s so important that you start integrating them as a core component of your marketing strategy.
As we said at the outset of the post, this is no easy task, but fortunately, MatrixPoint is here to help illuminate modern data modeling best practices for digital marketers.
Let’s start with the basics.
A data model is a conceptual representation of various metrics that you need to track.
A marketing data model organizes elements of data that your campaigns collect to determine how those elements relate to each other, allowing you to spot relationships, glean insights, and determine how to improve results by making changes to your marketing strategy.
In short, data modeling is the process of identifying, analyzing, displaying, and communicating data sets, allowing them to be more easily understood so that you can use them to make marketing decisions.
Below, we’ll discuss how different types of data modeling methods are used by digital marketers.
In the past decade, all sorts of new data points have begun to get captured, primarily in the digital marketing ecosystem.
With more powerful analytics systems and tracking possibilities, marketers have far more data to work with than they did in the past.
And while virtually every brand is collecting data, many haven’t yet quite figured out how to harness it for marketing purposes. That’s where data modeling comes in!
Data modeling lets digital marketers identify insights at each stage of the sales funnel, with advanced data modeling techniques shedding light on ways to improve marketing results.
The best marketing campaigns combine data models with AI-driven solutions and machine learning to predict results based on historical data, generate new insights about how to improve performance and maximize return on investment for each marketing dollar spent.
Data-driven campaigns have proven their effectiveness, and it’s highly unlikely that this important strategy will disappear.
There’s a very good chance that your competitors are already using data modeling solutions to guide their marketing decisions, and the longer you wait to embrace the potential this process unlocks, the more likely your results are to fall behind the competition.
Data modeling helps improve the results of advertising campaigns by informing marketers about:
A customer persona is the fundamental archetype of your ideal, target consumer. By using data modeling techniques, you can construct the ideal customer, determining all the different features they exhibit (from age to marital status, to household income, to interests, etc.), to help determine WHO exactly you should be targeting with your marketing dollars.
Once you’ve developed an accurate buyer’s persona, you’ll have much better insights that let you determine WHERE and WHEN to spend your marketing dollars, as well as HOW to discuss the features and benefits of your company, products, and services since you can cater all these decisions to your target audience.
But to create a customer persona, you’ll need accurate, authentic data derived from a credible source (or series of sources), and you’ll have to know how to coordinate the data points to help build that picture of your ideal customer.
That’s where data modeling comes in! By creating a proper data model, you can predict the target consumer’s needs, desires, and even behaviors, allowing you to design an ideal marketing campaign that produces the best possible results from that audience target.
After determining your target audience, running campaigns against them, and collecting the resulting performance data, you can now leverage data modeling strategies to measure their engagement, understand which parts of your campaign are working (as well as why they’re working), so that you can uncover insights that will lead to better results in the future.
This part of the data modeling process includes reviewing how consumers engage and interact with your ads, website, and content, allowing you to figure out which parts of the marketing process are truly motivating a targeted individual to convert.
With an advanced data model in place to measure engagement, you’ll be reviewing patterns like the timing and placement of your ads, the specific content pieces and calls to action that get the most clicks or generate the most sales, and other important components of the marketing process that are important to improving your ROI.
Once this data model is in place, you’re sure to produce actionable insights that truly move the needle, as the results of this model will help you to devise better processes for exposure, engagement, sales, and retention.
To get the most out of your digital marketing campaigns, you need to leverage data modeling services that provide actionable insights about how to optimize your campaigns.
Marketing optimization is all about leveraging the data you’ve collected to uncover learnings that can help enhance your marketing decisions, and therefore, results.
When your optimization model is in place, you’ll be capable of building data-driven insights to help reduce redundancies, cut inefficiencies, and optimize all of your efforts, from targeting through retention.
This part of the data modeling process is key to generating better results, as it’s the critical component that provides you the insights you need to assess the successes (and failures!) of your previous marketing campaigns and then identify opportunities for adjusting future strategy and tactics.
Data modeling best practices are sure to provide you the information you need to improve the results of your marketing campaigns.
With more accurate, relevant, and insightful data, you can easily analyze historical performance, predict future results, and build campaigns that engage and convert, rather than generate empty impressions.
But how are you to leverage data modeling if you don’t have any experience in the field? That’s where MatrixPoint can help.
Our team of data modeling experts is thoroughly versed in building and utilizing data models for marketing purposes, and specifically, for driving digital marketing strategy.
We’re prepared to review your data architecture, collection, storage, retrieval and analysis methods, and to provide actionable insights on improving your processes to generate better results.
Our data modeling techniques and best practices have proven to generate substantial performance improvements for our clients, and they’re certain to help you too, no matter how much (or how little) progress your team has made in developing your data models.
Ready to schedule a consultation with MatrixPoint’s data modeling experts? Contact us now to learn more about our data modeling consulting services.