As the leader of your Marketing department, the decisions you make about your team’s investment of time, money and resources for pipeline and revenue generating campaigns will be the difference between hitting your goals or missing them. That’s an awful lot of pressure. With the rest of the company already questioning what Marketing contributes to company success, it’s important to get these decisions right so that you and your team can be rewarded with praise and bigger marketing budgets.
Unfortunately, too many CMOs and Heads of Marketing don’t have the right systems in place to make data driven decisions. So instead, they trust their gut to make many of these decisions, and then get it wrong, really wrong.
They’ve put themselves in this position because right measurement foundation hasn’t been built. The data’s there. It’s just not easy to access, it’s siloed, and it’s not clear how to identify signals from the noise.
Once your team is able to remove the siloes, the raw data alone is often difficult to interpret and act upon. That’s where data modeling comes in—by transforming raw data into meaningful insights, data modeling helps your team turn data into actionable insights and to make informed decisions that drive business success.
Whether it’s predicting future outcomes, optimizing marketing campaigns, or delivering personalized experiences to customers, data modeling is a crucial exercise for CMOs and Heads of Marketing looking to crush it.
In this article, we’ll discuss the importance of data modeling for marketers and the benefits it provides. Then we’ll outline how to implement a data model and how Interrupt Media can help with this process.
What is a Data Model?
If you’re newer to the fascinating world of marketing analytics, a data model is a graphical visualization of data that is used to define the structure of data and the relationships between different data entities. It is an essential component of data management and hygiene, as it helps to ensure that datasets are properly organized, structured, and stored in a way that supports the specific needs and objectives of an organization.
Why Data Models Are Important For Marketing Teams
Data modeling is an important part of data management for marketers, as it helps to ensure that the marketing data collected and analyzed is accurate and relevant so that it can be used in predictive modeling that supports revenue generation.
With the increasing amount of data being generated, it is crucial to have a structured approach to data management to ensure that data is used effectively to drive business decisions.
This is especially important for marketers who use data modeling to ensure that data is properly integrated with existing systems and processes to make it easier to retrieve, analyze, and use the data to support a targeted digital marketing strategy.
Effective data modeling can support cross-sell opportunities, metrics and KPI reporting, and provide key targeting insights to attract more new customers.
How Do Marketers Use Data Models?
Let’s take a closer look at a few of the data modeling techniques marketing teams use to drive data-driven marketing strategies.
Marketers use data models to forecast different outcomes of certain investments in brand, marketing campaigns, pipeline and revenue goals. The plan that you and are team implement is most certainly not going to go the way you expect it. So having data models in place that can help you understand how different scenarios may play out for those daily micro decisions, in real time, are going to be critical to ensuring that you hit your goals.
Marketers use data models to segment their customers into different groups based on demographic, psychographic, and behavioral characteristics. This helps to target marketing efforts more effectively and to better understand customer needs and preferences.
Marketing Campaign Optimization
Data models are used by marketers to analyze the effectiveness of different marketing campaigns, including which channels and messages are most effective. This information is then used to optimize future campaigns for better results.
Data models can be used for predictive analytics, which involves using data to forecast future outcomes and to identify potential opportunities and risks. For example, a marketer may use a data model to predict which customers are most likely to purchase a new product or to respond to a new marketing campaign.
Customer Lifetime Value
Marketers use data models to estimate the lifetime value of a customer, which is an estimate of the total value a customer will bring to the business over their lifetime. This information is used to prioritize customer acquisition and retention efforts and to allocate marketing budgets effectively.
Data models are used by marketers to create personalized marketing experiences for customers. By analyzing customer behavior and preferences, marketers can use data models to recommend products, send targeted messages, and provide personalized experiences that are tailored to each customer’s needs and preferences.
How to Implement a Data Model
Now that you know why data models are important and how they can be used, let’s move on to how you can start implementing one in your organization.
Identify the Specific Business Objectives and Use Cases for the Data Model
The first step in implementing a data model is to identify the specific business objectives and use cases for the data model. This will help to ensure that the data model is designed in a way that supports the specific needs and objectives of the organization.
For marketers, the business objectives may include improving data analysis and insights, increasing efficiency in data management and utilization, and improving the accuracy and relevance of data.
Determine the Data That Will Be Included in the Model
The next step is to determine the data that will be included in the model. This will help to ensure that the data model is comprehensive and includes all the data that is needed to support the specific business objectives and use cases.
For marketers, the types of data that may be included in the model include customer data, product data, and marketing campaign data.
Design the Data Model, Including the Relationships Between Different Data Entities
Once the types of data have been determined, the next step is to design the data model, including the relationships between different data entities. This is an important step in ensuring that the data is properly organized and structured to support the specific needs and objectives of the organization.
During this step, the data model is created by defining the relationships between different data entities, including entities such as customers, products, marketing campaigns, etc. This helps to ensure that the data is properly connected and can be easily analyzed to support decision making.
A quick note about tools. The most common BI tools that we see our clients use are spreadsheets, Lookr (formerly Google Data Studio), Sisense, and Tableau. We’re big fans of using tools but if spreadsheets is things are for your team today, then consider migration to one of the above. It’ll save you a ton of time and hassle in the long run.
Test and Refine the Data Model as Needed
Once the marketing data model has been designed, it is important to test and refine it as needed. This may involve testing the data model to ensure that it accurately represents the relationships between data entities and that it supports the specific business objectives and use cases.
Refinements may be made based on the results to improve the accuracy and effectiveness of the data model.
How Interrupt Media Can Help with Data Modeling
Our marketing operations and sales operations strategists can help you identify your business objectives and design an effective data model that supports your go to market campaigns.
We will work with you to understand your specific needs, the types of data models you need, and design one that fits your objectives.
Besides consulting services, Interrupt Media also provides training and support for using whatever we help you build. Our team will help you understand how to use the data model effectively, including how to retrieve, analyze, and use data to support decision making. If you don’t have the staff to make sense of the data, you can also partner with us to help you uncover the actionable insights that you should focus on.
We also provide ongoing maintenance and optimization of your CRM or customer data platform to ensure that the data model continues to support your growing needs and objectives.
If you’re ready to level up your marketing analytics, speak with a strategist today.