What you should know about data modeling
Gartner says, “Matching the data model to its purpose remains the most-important criterion in selecting CDI products. The model must be able to handle complex relationships and should map to the master customer information requirements of the entire organization, not just selected areas."1
The easiest way to match data is to model the customer as an entity with all related attributes like name, address, bank account, etc. But wait. What if the delivery address should be different? Of course, you can create two or more sets of address attributes, and the same applies to bank accounts and contact persons - but where should this end? At least for B2B customers, several attributes would end in a mess.
The norm is to establish each attribute as entities and relate them to the customer. But what is ‘the customer’ in this case? In B2B you have different parties: sold-to, ship-to, bill-to, and maybe more. Some will be corresponding and some will differ. That’s why you should set up your party-specific data as separate entities with a separate subset of mandatory fields. While the data for the ship-to party may contain details about delivery times, the others need conditions and payment terms. Additionally, if a customer is also a supplier or an employee, it could be a different role as well.
After you have set up your customer, you can start ‘householding’ (linking all family members in a group). However, any group can change e.g. by marriage, divorce, birth and death of a family member, children can move out and set up their own household or house their parents later. So, you should implement processes that can update these relations later.
Focus your marketing efforts on your good customers
Now you know who your customers are, but do you also know (according to the Pareto Principle) that 80% of your sales come from 20% of your customers? To focus your marketing efforts on your ‘good’ customers, you need to calculate the customer value. One method to do this is RFM (Recency, Frequency, Monetary Value). RFM analysis determines your best customers by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary).
For the RFM method, you need to relate the customers with their purchased products. This information can also be useful for your customer service department. Here’s another reason to have good multidomain MDM in place. As for the main challenges in using data to engage target audiences, 61% of respondents of a recent Forbes survey2 cite that it’s breaking down the silos of data between internal departments to ensure the successful flow of information. Over half point to complex technical solutions as an obstacle. Only a mere 14% of respondents have a portfolio of tools for analysis and planning that are established and fully supported by a single platform and best practices.
The ubiquity of big data makes it necessary to locate both customer and product data (as well as other domains) in one single system so that organizations “have the opportunity to build relationships between those customers and those products,” as put by Stibo Systems’ VP of Product Strategy Christophe Marcant.3
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 The data is derived from a survey of 162 U.S.-based senior executives conducted by Forbes Insights in September 2015.