# New Use Case: Customer-Intelligence Graphs for Strategic Decision-Making

**URL:** <https://community.neo4j.com/t/new-use-case-customer-intelligence-graphs-for-strategic-decision-making/75380>\
**Category:** General\
**Created:** [September 10, 2025, 6:27am UTC](https://community.neo4j.com/t/new-use-case-customer-intelligence-graphs-for-strategic-decision-making/75380 "2025-09-10T06:27:37Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![eckhart.boehme](https://sea1.discourse-cdn.com/flex021/user_avatar/community.neo4j.com/eckhart.boehme/32/36012_2.png) [@eckhart.boehme](https://community.neo4j.com/u/eckhart.boehme)\
**Post date:** [September 10, 2025, 6:27am UTC](https://community.neo4j.com/t/new-use-case-customer-intelligence-graphs-for-strategic-decision-making/75380/1 "2025-09-10T06:27:37Z")

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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:

- _How can we win more customers?_

- _How do we retain existing customers more effectively?_

- _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.

 ![4 Phases of the Customer Journey4](https://us1.discourse-cdn.com/flex021/uploads/neo4jcommunity/original/3X/5/8/582ff2dbd243d27389ba963d08526fdffa21f9f1.png)

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:

- Are curious about applying graph thinking to qualitative, human-centered data.

- See potential in building ontologies for strategic business questions.

- 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

> **[Home - unipro solutions](https://unipro-solutions.com)**
>
> Book a 30 min Demo Turn unstructured qualitative customer data into a strategic digital asset.Power genuinely customer-centered marketing, sales, and product decisions with a machine-readable knowledge base - your single source of truth. What Your...

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<div class="post-metadata">

**Author:** ![eckhart.boehme](https://sea1.discourse-cdn.com/flex021/user_avatar/community.neo4j.com/eckhart.boehme/32/36012_2.png) [@eckhart.boehme](https://community.neo4j.com/u/eckhart.boehme)\
**Post date:** [September 25, 2025, 10:09am UTC](https://community.neo4j.com/t/new-use-case-customer-intelligence-graphs-for-strategic-decision-making/75380/2 "2025-09-25T10:09:09Z")

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> **[\#knowledgegraph | Eckhart Boehme](https://www.linkedin.com/posts/eckhartboehme_knowledgegraph-activity-7376909908736229376-94kS?utm_source=share&utm_medium=member_desktop&rcm=ACoAAABfwvYBXyRHN4ONZd1E56RswYmYU5aq_bM)**
>
> 10 Problems Leading to 99% of Qualitative Customer Data Being Underutilized
> 
> Most companies don’t realize it, but they are sitting on a goldmine they can’t access. 99% of qualitative customer knowledge - interviews, focus groups, customer...

Here are some insights about the problem with unstructured qualitative customer data and why a #knowledgegraph is the answer.