Customer data is like Lego, the colorful plastic bricks with which entire worlds are created: I must know beforehand what I want to build. I can't build a petrol station with a kit for a zoo or a farm. I need the plan to put together the right bricks: Only then can a concrete Lego project be completed. It is similar with marketing data: At the beginning there is the question of the goal. What do I want to achieve? Win new customers? Increase the value of existing customers? Improve the performance of my measures? From this, the appropriate campaigns are derived, which in turn determine the necessary customer data.
Marketing needs a clear, long-term data strategy - otherwise it gets lost in aimless collecting. This is not only an uneconomical effort and difficult to communicate to consumers. It also easily creates a situation that we all know from Lego: essential building blocks are missing, the customer experience suffers, and the campaign performance suffers as well.
In the beginning is the data strategy
You must put the horse before the cart to develop a data strategy. That means you first look at goals and measures. What is relatively easy with Lego bricks is much more complicated with marketing data. We also describe this challenge in the blog post "The Post-Cookie Era: How Marketers Can Raise the Treasure Trove of Data in the Company"
At diconium, our Customer Data Framework provides a structured approach to building the data strategy, i.e., a plan for collecting, organising and using customer data in the organisation. This plan describes step-by-step how to build a Customer Data Record, i.e. the data set of an individual customer, and how to create data-driven campaigns using the Four A's approach (Acquisition - Authentication - Enrichment - Activation). It also includes practical white-label solutions for different industries and use cases.
Defining a target system for marketing data
From the data, the strategy determines the customer data and information that companies require to establish and execute their marketing campaigns in a personalized, data-driven, and automated manner. To illustrate using our Lego analogy: The target customer data set serves as an essential component of the blueprint, indicating which bricks are necessary to construct – in this case, the petrol station – and which are not. Drawing a comparison with my sorting box – the data stock – reveals two key insights:
... the customer information already available from various sources
and
... the data that is still missing to create data profiles suitable for marketing activation.
Based on this foundation, one assesses the available customer data to determine potential merges, identifies missing data points, and identifies areas where data qualification and enrichment may be necessary.
On the way to the "golden" customer data record
A customer data record comprises fields that summarize specific data types and attributes. Determining the data relevant to a company is a highly individual and strategic choice. This 'ideal' target data record is often referred to as the 'golden' customer data record. It consolidates data from various users, customers, and sources into a unified system, allowing for clustering based on various criteria and seamless utilization in marketing efforts. However, prior to collecting new data, it's crucial to capture the existing customer information and consolidate it under unique user IDs. The creation of a customer data record involves two initial steps: inventory and sorting.
Sorting and analyzing customer data
Taking a systematic approach is crucial. To attain a comprehensive 360-degree view of the customer, we at diconium recommend categorizing the information into the following four data fields:
- Consumer Data: This category encompasses master data, including personal information such as name, address, age, gender, family status, and more.
- Consumer Behavior: What are the product preferences of consumers? Which websites do they visit? What triggers do they respond to? This information resides within this data field.
- Customer Stories: How does a customer's behavior evolve over an extended period? This data field includes metrics such as shopping frequency, shopping basket composition, customer lifetime value, preferred payment methods, and more.
- Customer-Cluster: To which persona/lifestyle do the customers belong? Customers can be classified based on factors like price sensitivity, voucher affinity, or loyalty index.
These data fields provide the organizational framework for sorting customer data – much like a sorting box for Lego bricks. The process begins with the company's existing consumer information, focusing on personal details that can be linked. In the subsequent step, anonymized customer data is transformed into actionable insights about consumer behavior. The third phase involves consolidating all purchase and customer history information. The foundation for categorizing customers into distinct clusters is then established, utilizing these three data fields: consumer data, consumer behavior, and purchase history. This marks the fourth and final step in the process.
The customer data record serves as the starting point for marketing campaigns
Clients need to determine how these data fields are tailored and structured according to their unique data scenario and campaign strategy. This is precisely where diconium's consulting services offer valuable assistance. This specific customer data record then serves as the foundation for automated marketing campaigns. In essence, effective customer communication through automated and personalized marketing campaigns hinges on the utilization of the relevant customer data record.
