What Is Meant by ‘Marketing Data Model’?
A data model organises information and standardises the way informational elements interact with each other.
So, a marketing data model means that marketing information elements are organised, and they interact with each other in a standard way. If your marketing data isn’t structured, you won’t be able to make sense of it, and you won’t make the right decisions about campaigns.
Predictive Analytics: Knowing What Your Customers Will Do Next
Your marketing data model can actually tell you what customers may do next. Predictive analytics is the ability to forecast what customers will do in the future, based on what actions they’ve taken in the past. These advanced analytics aren’t a new technology; they’ve been around for over a decade. However, advances in other technologies (such as AI) have made predictive analytics more effective.
Advanced analytics only work if you’re using the right data sources, though. What data sources should you use, and how can you build the best possible marketing data model?
How to Create a Better Marketing Data Model
There are six steps in creating a better marketing data model:
- Define goals
- Understand available data sources
- Ensure data quality
- Start small
- Scale up
- Keep fine-tuning
Define Goals
The first step in developing a better marketing data model is to define your goals. What is it you’re hoping to get out of your marketing data model? Even if you’re looking to gain insight from predictive analytics, that’s still rather vague.
Defining goals is akin to mapping out a road trip; you don’t know if you’ve gotten to where you want to be if you don’t have a destination in mind. Remember that the best goals are specific, measurable, achievable, realistic, and time-bound. For example, increasing a particular audience segment by 25 percent in a quarter would fit the bill.
Understand Available Data Sources
A successful marketing data model is built on internal, external, and third-party information. Internal data is the information you’ve gathered from your customers; it’s sitting within your firm’s data repositories. External information is information that you aren’t already storing, yet it is fairly easy to obtain.
Here are some examples of internal data:
- The device a person used to view marketing messages
- The date and time a person viewed the marketing messages
- A person’s location
- How many times they’ve visited a page
- Which forms they’ve filled out
Here are some examples of external data:
- Weather
- Real estate statistics
- Location
Third-party data is a hybrid of internal and external sources; it comes from external sources that are integrated with internal information architectures. Here are some examples:
- Product orders
- Bookings to talk to salespeople
- IoT information
Ensure Data Quality
A marketing data model will fail without the right information to support it. You need information that’s accurate and up-to-date (those two concepts are related, but aren’t the same thing).
It’s possible you’ll need to invest time and effort into evaluating your data’s quality, and then more time cleaning it up so that it’s usable. This is worth it, though. Without good data, you can’t reach the right customers, and you can’t make the right decisions, either.
Start Small
To build a better marketing data model, start small. If you’re trying to improve your data model, now is not the time to test it out on a major project. You want to experiment with it and see what kind of results you get.
Pick a set of data or a project where there aren’t major consequences if something doesn’t go as planned. Now is the time to learn, tinker, and figure out what works and what doesn’t.
Scale Up
When you’ve finished experimenting, it’s time to scale up. You’re ready to apply the marketing data model to a campaign.
Ideally, the time you’ve invested in perfecting the marketing data model should yield excellent results. You should have advanced analytics, and you should have a deeper insight into what your customers are going to do next, as well as what the best decisions are for your marketing department. However, improving your marketing data model isn’t a one-and-done exercise.
Keep Fine-Tuning
You might realize that even after scaling up, you’re still not always getting the results you want out of your marketing data model. That doesn’t necessarily mean that you’ve failed. Rather, you need to keep fine-tuning your data model.
Even if you’ve scaled up successfully, fine-tuning from time to time is still important. You might have new streams of data coming in. Furthermore, data ages; you need to ensure they’re accurate and up-to-date frequently so that you’re reaching the right people and making the right business decisions.
How often should you recalibrate your marketing data model? That depends on your needs. Ask yourself how often your data changes, and whether you anticipate any new data streams being integrated into your existing model.
Enlighten Designs: Helping You Improve Your Marketing Data Model
Since 1998, Enlighten Designs has created delightful digital experiences for our clients. We’ve successfully leveraged our expertise in data and marketing to implement advanced analytics, so you can better understand your clients and make the right business decisions. To learn more about improving your marketing data model, contact us today.