Marketing automation has helped businesses of all sizes leap forward with their marketing, branding, and PR efforts. It helps to streamline many redundant and administrative tasks and even get closer to your target audience.
Enterprise-sized businesses, however, have a very important card up their sleeve that can completely change the game when it comes to marketing automation – they have data.
Lots of data.
Data for days, weeks, and years. With an international presence, hundreds of thousands if not millions of customers, and many years of operation, these large-scale businesses have data pouring out of their ears – and it’s this data that can help take marketing automation to the next level thanks to data modeling.
Before you can jump into the automation process, however, it is imperative that you first make that data work for you. Unstructured and unorganized data is just a mess you need to comb through. If there is data on multiple systems, servers, and apps, and none of it is centralized, then you’ll struggle to get that breakthrough in any type of automation, much less the customer-driven branch that is marketing automation.
That’s why, if you are a heavy-duty enterprise, it’s key that you follow this guide to wrangle together your data with a data modeling approach so that you can then build out an effective automation framework.
Table of Contents
Getting Control Over Your Data
Automation systems need centralized and organized data in order to work efficiently and to the best of their capabilities. Until your data is in a centralized data warehouse or data fabric solution, you simply won’t get the most out of any automation tool.
Now, building a data warehouse isn’t as simple as buying a building and putting servers in it. To get all that information sorted and properly mapped out, you need a data model.
Introducing Data Modeling
What is data modeling? It’s essentially a diagram that maps out how data is to flow from all those external tools, apps, and servers into your centralized data warehouse. It’s the plan that gets the entire project in motion. Not only does it help bring all your information together, but it can also be used to define data sets so that they’re easily sorted.
In short, you need this tool in order to properly plan and implement a big move from disorganized datasets to organized ones. With a comprehensive data model on your side, you can create a simple, easy-to-understand database or data warehouse and, in the process, remove redundancy (duplicate files) to save storage space and speed up processing.
How Effective Data Modeling Can Look in Marketing Automation
There are many ways that a strong data model can help with your marketing efforts. For example:
- You can offer a discount for every person’s birthday or, if you sell children’s clothes, a discount on their child’s birthday. To do this, you need to organize the parents’ information and the child’s information and use relevant details to personalize those messages in the best way.
- You recommend products based on the full catalog of products that a customer has offered, not just the last product (which may be a gift or a one-and-done purchase, etc.).
- Send out reminders based on what the customer needs (for example, a garage might send out regular maintenance emails so their customers know when their car needs a tune-up).
Decide Between a Flat vs. Rich Data Model
The first question you need answered so you can start building an effective data model is whether it needs to be flat or rich.
Flat Model
A flat modelis a list that organizes data into a single table, which is realistically nothing more than an Excel file. This type is useful for very specific types of tasks, but it’s not very adaptive. It can also only work with the last information given (for example, the last item purchased, even if it was a one-done purchase or a gift).
Rich Model
A rich model is a relational approach that sorts and organizes information into a series of tables. This way, you can create a very detailed customer profile. If your goal is to offer personalization as part of your marketing automation efforts, then you will need a rich data model to do it. Of course, these models take more effort and time.
The Best Data Modeling Practices for Marketing Automation
Now that you know a bit more about what data modeling is and how it fits in with your overall data strategy, it’s time to put a few key best practices to use. These methods will help sharpen your data strategy in ways that specifically help with your marketing automation efforts:
Understand the Needs and Outcomes
To build a strong data model that can help you push for effective marketing automation, you first need to clearly outline your business needs and the outcomes that you want from your business. This will help you choose the right tools and, more importantly, help you communicate if you outsource this to a data modeling professional.
Clearly Visualize the Data You Need to be Modeled
Visualization is a very important part of the data model process. This is because standard rows are hard to see and a headache to parse through. By creating graphic illustrations to showcase the data, you actually make sure it’s easier to understand and adapt later on. Drag-and-drop screen interfaces let you and your team easily adapt, connect, or review data tables as needed.
Start Small and Scale Up
It’s important to have a strong foundation and then start adding more information. Doing it this way is more manageable, lets you easily track progress, and, most importantly, limits the amount of discrepancies that can occur in the process.
Add Relevant Supplementary Data
You need to store and split up data into facts (like the number of sales) as well as additional supplementary data (like the specific store or region those sales were purchased from) and then a filter (like a timestamp). This will let you easily extract answers like “Which product sold the best in the summer?”
