Data Modeling in Digital Marketing: What is That and How It Works
Any marketer will tell you that without data, your digital marketing strategy will be based on guesswork. Indeed, data-driven marketing concepts that analyze and customize your customer buying behaviors are the core of digital marketing. However, connecting the customer’s data to results can seem daunting.
Even though data modeling is the heavy hitter of digital marketing, your ROI will remain underwater if you don’t know the best marketing metrics. In fact, 87% of marketers say that data is their company’s most under-utilized asset. This means that most companies are still doing mass marketing and not even using data modeling basics.
In this article, we'll walk you through what data models are, why data modeling is so important, what makes a good data model, and how you can simplify your customer data collection, data storage, and usage.
What is Data Modeling
Data modeling is the process of creating a visual representation of data flows to connect the dots between structures and data points. It shows the data types the system contains, the relationship between analyzed data types, the attributes and formats, and the way the data can be organized and grouped. This allows marketers to understand the data flow, analyze customer data for trends and patterns, and gain real-time insights on the best marketing campaigns for their customers.
The Role of Data Modeling in Digital Marketing
Many businesses are beginning to capture and keep different types of data modeling for their digital marketing needs. With the advent of artificial intelligence, more marketing automation tools have seen the light and data annotation has made huge changes in this particular sphere. However, a closer look at the so-called ‘data-driven’ campaigns shows that most businesses are yet to understand what metrics they really need to track. Here’s where data modeling comes into play.
For instance, a lead list generated by a mass marketer mostly includes an overwhelming list of potential customers that doesn’t consider basic attributes like age, location, gender, etc. Data-driven marketers know that the power is not in the numbers. Identifying your highest-value leads involves using data to connect the dots around fit, intent, and engagement.
In short, the primary role of data modeling is that it can be targeted to a specific product or service. This allows you the flexibility to target customers based on the sales funnel, behavioral patterns, past purchase history, and many more attributes.
Key Benefits of Data Modeling
The key benefits of data modeling in digital marketing include:
1. Reach and interact with the right audiences
To use data modeling concepts to learn about your target audiences’ interaction with your ads, implement the following:
- Build look-alike audiences from a seed audience of potential or existing customers: A $1000 ad experiment conducted by Facebook proved that look-alike customer segmentation has a better clickthrough rate and a 13 percent lower cost per action. Data modeling tools like Customer Data Platform help marketers to connect customers with similar interests and traits.
- Create custom audience segments according to unique attributes such as age, gender, interests, location, purchase intent, and marital status. This promotes a personalized marketing experience for your customers.
Reach the right audiences with Yespo
2. Better Customer Experience
Improved customer experience is a key benefit of data modeling techniques. No matter how robust your marketing dollars are, if your data-driven campaigns are not measuring metrics tracking customer experience, poor sales will remain the thing around your business’ neck. Typically, approximately half of the consumers leave a product page to purchase elsewhere due to poor UX.
Data modeling strategies allow businesses, especially ecommerce and B2C businesses, to add personalized product recommendations to their marketing mix. Personalizing the experience builds trust and customer loyalty for brands and has been shown to increase ROI by 5-8x, says McKinsey.
3. Fine-tune campaigns to drive better results
Customer needs are usually dynamic, and so should your marketing campaigns. Your data modeling process should be continuous, giving you room to tweak and fine-tune your strategy as needed. To further optimize your campaigns, consider upselling or cross-selling your products. Always remember your end game — to develop personalized marketing strategies for the highest possible ROI.
4. Create a customer persona
Customer personas are detailed descriptions of the primary characteristics of your target audience. Using data models, you can map the “who” and “what” of most of your customers and determine the motivations behind their purchasing decisions. This is also powered in part by data-driven customer segments from market research, competitor analysis, and customer profiles. We recommend building multiple buyer personas for each customer segment. For example, here’s Shawna Cummings, a buyer persona for a B2B product management company illustrated by Semrush.
5. Measure performance
When you launch a business data modeling campaign, you should have benchmarks and key performance indicators. For instance, ecommerce businesses usually measure mobile traffic, cost per lead, close ratio, social media traffic, bounce rate, conversion funnel rate, and more. Studying these metrics can help you understand what you are doing right, what needs to be improved, and predict the best route for your future campaigns.
Types of Data Models
The different types of data models include a conceptual data model, a logical data model, and a physical data model.
1. Conceptual Data Model
Conceptual data models provide data in its rawest form. It is a high-level, abstract, and more of a summary-level model. It usually defines, organizes, describes, and presents data entities and their relationships without providing technical details such as data types and attributes.
Owing to its simplicity, data analysts use conceptual data models to communicate with business stakeholders when defining “WHAT” a technical business concept will contain.
Here’s a good conceptual data model example.
- There are three data elements associated with the concept — order, customer, and shipment.
- The elements are related to one another. For example, there must be a customer to create an order and an order to create a shipment. A customer can be associated with zero or more orders, while an order may or may not lead to any shipment.
2. Logical Data Model
In addition to entities and their relationships, the logical data model shows a high-level data representation of attributes, data types, keys, and concepts that support the goals and requirements of target businesses. However, it doesn’t describe how data in a specific database management system (DBMS) can be accessed, stored, or manipulated.
Similar to the conceptual data model, the logical data model also uses simple business language (not technical terms) and is not connected to a particular technology platform.
3. Physical Data Model
A physical data model is a form of logical model that contains extra information about indexing and partitioning. It is a low-level representation that specifies how data can be accessed, stored, and manipulated in a specific DBMS. It’s important to note that a physical data model is specific to each database system and can vary based on technical limitations and specifications.
5 Data Modeling Techniques to Try
To better understand data modeling techniques, picture a data model as an architect’s building plan. Just like the building plan, data modeling provides the skeletal framework upon which the essential data is built. While data modeling techniques explain how the different modeling process works, it also clarifies how essential data is arranged.
1. Relational Data Modeling
The relational data modeling technique groups data in a “rows and columns” table format. The data have the following terminologies: tables are called relations, rows are called tuples, and columns are called attributes. The rows contain data entities, while the table and column names indicate what the values are. This model type is commonly used for data that can be easily structured into categories.
2. Entity-Relationship Data Modeling
Other names are E-R data models and E-R schema, so called because they are represented by diagrams. This data modeling technique is a modified version of the relational model that describes data elements and relationships for a specific system. Because E-R modeling is great at capturing data and updating processes, they are best suited for applications used for processing transactions.
3. Dimensional Data Modeling
The dimensional data modeling (DDM) technique utilizes facts and dimensions to store data entities in a “data warehouse” effectively. A data warehouse is a data management system that integrates data from multiple sources into one database. In the practical sense, it optimizes large volumes of data for quick retrieval. Because of its specified structure and data organization, it's also easier to generate performance reports.
4. Object-Oriented Data Modeling (OODM)
The object-oriented data modeling technique is similar to the E-R model in the way they represent relationships, attributes, and data. However, as the name implies, this model is stored as objects and can be used to represent real-life entities such as audio, pictures, videos, etc. Of note, the OODM model works with the Object-Oriented Database Management System.
5. Graph Data Modeling
This data modeling technique is also known as the “whiteboard-friendly” model because you can draw data on a whiteboard and simply link it to other data to show the relationship between the entities. Graph data modeling strategy is a relatively modern offshoot of hierarchical and network models.
Graph databases are built on the graph model and are frequently used to define data segments containing complex entities. It comes in handy when analyzing social media apps, fraud detection apps, and product recommendation engines.
How CDPs Can Help to Organize Your Customer Data
CDPs or Customer Data Platforms is a data management software that combines and analyzes first-party, second-party, and third-party data from multiple channels to personalize the user experience. If your digital marketing strategy doesn’t include data-driven omnichannel campaigns, you are simply paying lip service to your poor sales problems.
Launching CDPs such as Yespo’s customer data modeling software allows you to integrate, segment, and consolidate omnichannel data, spot bad data hurting your marketing strategies, and make the most of your customer data. We have a long list of case studies to back this up!
Using Yespo’s CDP features, we increased Modoza’s subscription conversion by 5X. Modoza is the biggest online brand in Ukraine, dealing with Italian products. Even though they offer high-grade clothes and shoes for over 600 manufacturers, they had a poor conversion rate of 2.5%. We solved this pain by segmenting their customer contacts and launching event-triggered workflows. After sign-up, customers automatically receive promo codes and discounts. This increased Modoza’s subscription conversions to 14%.
If you are itching to grab a piece of the marketing pie and get a good ROI from your advertising spend, then sign up for free. We’ll get in touch, analyze your customer data, and come up with a data-driven omnichannel campaign that your customers cannot ignore.
Your business data modeling doesn’t have to be too complex to drive results. However, you must know that if your data modeling steps are not targeting the right metrics, it won’t scratch the itch of your marketing needs. It doesn’t matter how much data you integrate into your business’ mass marketing data model. Always remember that if you don’t have any data to justify your marketing campaigns, you are not only leaving money on the table but are also giving your brand a bad online reputation.