There’s no doubt that having a reference data set about organizations, such as OrgRef, at your disposal is a key part of solving a complex customer puzzle. Not only can it help you to standardize your customer data and identify new sales prospects, it can also help you to understand and visualize the relationships and hierarchies between your customers.
You may already have a notion of a customer ‘top level’ – those important institutions holding the purse strings – but do you know who is related to them, and how that financial relationship filters down in real terms? Can you effectively piece together all the parts of the puzzle to see a joined up view of institutions, departments, consortia and their members?
Family tree
The complex multi-level nature of your customer base can involve relationships between individuals and their parent organizations, organizations and their subsidiary parts (such as university faculties and departments), and consortia or multi-site deals and their members. Having a structured and simple 3-level hierarchy in your reference data set can nicely compress this into something that is clearer and more manageable.
Data disambiguation
Your existing customer data may contain inconsistent naming, identifiers originating from different source systems, or no unique identifiers at all. This is where a central ‘backbone’ of organizational reference data is invaluable, for matching and joining customer transactions together within a single record, which can then be viewed in the context of the organization’s wider relationships.
Throw in the ability to define your own hierarchy that accurately reflects the relationship you have with those organizations, and you’ve got yourself a true picture of all your customers and contacts. This is an invaluable tool for:
Prospecting: Whether you’re aiming to increase your circulation and drive up subs, or generate new article submissions, having an understanding of how all your contacts relate to each other is vital. For instance, by linking authors to their institutions, you can look to increase subs from those institutions which aren’t subscribing to journals their related authors are submitting to.
Market penetration analysis: When you review your coverage of your market you may wish to include more than those institutions you are actually selling to. For example, you may also want to consider their sub-units as being ‘sold to’ so you’re not trying to sell something those customers already have. Having a strong hierarchical data model means the concept of inheritance becomes easier to visualize and easier to use, and – by also knowing who not to contact – ultimately ensures good customer relations.
If you are able to easily negotiate your way around your customer hierarchies, you’ll be better equipped to understand and exploit those relationships effectively – and derive the maximum value from them.
The DataSalon Blog 