Technical in nature but designed to be simple, Customer Data Models are the relationship definers characterizing customers’ data points in visual manifestations. Hence, we can say, a well-defined Customer Data Model is the dictum through which the 21st-century businesses’ selling processes are defined.
On the other hand, a classic Data Model can be of three different kinds (which are in general use); each solves hardships specific to your business needs — thanks to the several many feedback loops it went through to bring to the desired perfection.
In this blog post, we will discuss one such manifestation of data modeling that helps marketers define customers and guide them through their buying decisions. We will be talking about Customer Data Modeling, its importance, and the techniques that will definitely help you develop new strategies to cater to your customers’ needs. Let’s begin.
Table of Contents
- The Importance of a Customer Data Model
- Strategy That Performs Well When Creating An Ideal Customer Data Model
- How Should an Ideal Customer Data Model Look Like? [Best Practices]
The Importance of a Customer Data Model
Though a bit scientific-sounding, a Customer Data Model works like any other specialized data model aiming to benefit the smooth functioning of business processes. Here, the motto is to define customer data cluttered behind unrecognizable data points in a way that can help organizations sell and marketers collaborate effectively. Here’s how a logically-built Customer Data Model benefits organizations worldwide.
Effective Human Resource & Capital Usage To Achieve Better, Faster Time to Value
When talking about matters related to customers suddenly, the processes become capital intensive; thus, the imperative to cut costs exists. Business leaders use data models to define fundamental business rules to mitigate the risk of excessive resource utilization, resulting in fewer revisions, lesser implementation time, and adequate capital and human resources usage, i.e., better, faster time to value.
Understand & Improve Selling Processes
To better understand your customers, it is essential to know where they are coming from — meaning to uncover the data to get to know new customers and better selling practices. The information decluttered will also help understand custom-built business processes and if any room for improvement exists.
Reduce Customer Communication Complexities
Usually, it’s hard to guess which stage the customer is at; hence it’s common to miscue preferences while drafting new messages. The Customer Data Model helps you define the same. With intuitive diagrams for processes, a Customer Data Model allows you to reduce customer churn, helping the organization, especially the C-suite, gain a unified view.
Improve Team Collaboration
With a significant amount of reduction in customer communication complexities, a Customer Data Model helps reduce team collaboration conflicts, too. In general, data modeling makes it easier to understand high-level business processes faster. That said, with a defined Customer Data Model it becomes easier for the sales staff to work in tandem with the technical staff and vice-versa.
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Strategy That Performs Well When Creating An Ideal Customer Data Model
First, before going into the specificity and the so-called conservative nature of any Data Model strategy, the structure of our thinking process should be well-defined. Here, it’s all about customer acquisition; hence, let’s take a cue from the traditional web analytics model.
Look at the illustration given below:
The illustration shows three tables providing information in this flow:
Page Views > Sessions > Users
- Sessions can or can not always be relevant for your business model or relevant use cases.
- The difference between mobile and web interactions sessions can be different; it’s important to define those.
- It’s important to measure sessions reliably when many target users are involved with various many user journeys.
- Not always, Page Views are the smallest unit of interaction. Here, video engagements and direct searches are not involved.
- It’s important to have SQL skills to understand data better. One must yield different processes so that teams can understand and make data-driven decisions across business functions.
Now, let’s look at what these three tables capture and predict. Hence, through the mandate of the traditional web analytics model, we can term that Page Views will show the number of interactions, which shows how many users engage with the content on the platform. A Session can be defined as interaction over time, thus termed ‘Cycle,’ and the User is an entity involved in the interactions with the platform or your sales team.
In detail, let’s talk about the three defining factors — Interaction, Cycle, and Entity.
Let’s look at the level of the interactions and further break down the captured information as follows:
Dimensions related to the interaction can be as follows: Referrer, timestamps.
Examples of interactions other than the Page Views can include: Screen views, ads, impressions, email opens, bids, transactions, form changes, purchases, searches, logins, and conversions.
With the current example, we have one row per cycle, and if we take sessions as an example cycle, the information can be broken down into the following:
Dimension related to the cycle: Session ID, session index
Dimensions related to the entities: User ID, location
Cycle metrics: Page views, conversions
Other than the current examples of cycles, more examples might include: Course completions, signup funnel, content discoveries, sessions, game level competitions.
In the example mentioned above, we have provided one row per entity, and the information can be broken down into the following parts:
Dimensions related to the entity: User ID, location
Entity metrics: Sessions, page views
Other external entities could include: Products, articles, videos, campaigns, apps, and data pipelines created
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How Should an Ideal Customer Data Model Look Like? [Best Practices]
To better manage customer communications and persuasion strategy, there must exist competent-enough processes in the form of Customer Data Models; keep these three best practices in mind:
Identify the Grain
When defining the design processes, it’s important to define the data grain in fact tables — and the most common error data professionals make. In data warehousing, data graining in the fact table helps identify the level of complexities in the level of measurement of the data stored. In short, the refiner the grains, the more detailed and agile the analysis will be vis-a-vis selling outputs.
For example, imagine a table “order_states.” In this table, the relations can have multiple rows which reflect the stage of the orders placed by the customers as “placed,” “paid,” “canceled,” “delivered,” “refunded,” and so on.
But, what’s the right level of granularity to streamline the process? Here are three cue points:
- Always, make it easy to determine the grain of the relationship.
- Nomenclature of the relationship is a must-have such that the meaning of the grain is clear.
- The column must be in relation to the grain. For example, placing “user_age” in the column defined for “order” is not prescribed.
Also referred to as “caching” in the software engineering world, Materialization is the process of defining relationships as a table or as a view. In fact, both the processes come with their own benefits (depending on what data warehousing technology you choose to go forward with). Here’s how:
- If you choose to create a relationship as a table, the User will experience a much faster query response time.
- And, if you choose to create relation as a view, the User will be getting up-to-date information with a response time on a little slower end.
Data Governance Remains Central
Data security and privacy become paramount when stakes are high. Hence, the best practice remains to become thoroughly aware of the permissions and governance requirements for doing business and to acquire new customer details — which vary from geography to geography. To learn more about the subject, refer to this blog: 2021 Demands Fresh Data Security Paradigm, Will New Cloud Adopter benefit?
In this blog post, we looked through the surface of what it takes to make a competent-enough Customer Data Model, through some basics which align with the goals of defining the first layer of the marketing funnel. The process dictates you first get a hang of the traditional web analytics model and then go further by defining your marketing funnel.
On the other hand, as you collect and manage your data across applications and databases in your business more data models are bound to take shape, now it becomes important to consolidate for agile performance analysis.
However, it’s a time-consuming and resource-intensive task to continuously monitor the Data Connectors. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Incrementally Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics.
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Also, let’s know about your thoughts and building process for your custom-built Customer Data Model in the comments section below.