Hi everyone,
I’d like to share a use case we’ve been working on that might be of interest to those experimenting with Neo4j beyond the “classic” domains like fraud detection or recommendations.
We have been successful in translating qualitative customer research into a graph model and then using it to answer strategic business questions, such as:
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How can we win more customers?
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How do we retain existing customers more effectively?
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Where can we lower cost while improving the customer experience?
Think of it like a customer-intelligence brain. Just one interview consisted e.g. of 300+ nodes and 10k+ relations between the nodes. This reflects the reality of customers trying to make progress in life.
Our model is based on an ontology (The Wheel of Progress (r) we originally used on paper and digital whiteboards to map customer progress, jobs-to-be-done, pains, gains, and other qualitative insights. For the first time, we’ve digitized this ontology and brought it to life inside Neo4j.
Image: Partial graph of a customer-journey
The result: a Customer-Intelligence Graph that makes qualitative insights and customer journeys queryable and explorable. Instead of buried notes or static reports, decision-makers can navigate connected realities of customer behavior and strategy designers can use Cypher queries to uncover patterns, causality, and opportunities.
I believe this opens a whole new category of applications for graph databases: customer-intelligence as a data asset in its own right.
I’d love to connect with others in the community who:
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Are curious about applying graph thinking to qualitative, human-centered data.
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See potential in building ontologies for strategic business questions.
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Might want to explore this use case further with us.
Has anyone else experimented with similar approaches - turning interview data, customer journeys, or qualitative insights into graphs?
Looking forward to your thoughts and discussions!
Kind regards,
Eckhart Boehme