CustomerLinks Introduces Graph Visualisation: A New Foundation for Accurate Customer Understanding
Product Update
Customer data is rarely simple. Names repeat, addresses overlap, online behaviour shifts, and systems capture information in different ways. Until now, CustomerLinks at Lumilinks stitched this together using structured comparisons across rows of data. It worked well, but it didn’t always show why records were linked or how an entity held together.
That changes today.
We’ve introduced graph-database capability into CustomerLinks, giving Snowflake users a clearer and more intuitive way to understand their customers.
What this update adds right now
The first visible change is the new graph view. Instead of reading through rows of records, you can now see each entity as a network of connected nodes.
This helps you:
- Understand what links multiple records together
- Spot outliers that don’t belong in an entity
- Identify critical nodes that hold everything together
- See your golden record forming in real time
For many users, this alone makes entity resolution far easier to interpret.
Why this matters
With the graph structure in place, CustomerLinks can begin making more intelligent decisions about identity and customer relationships. It’s not just about whether name, postcode and address match anymore. It’s about how pieces of data relate across the customer journey.
This release lays the groundwork for that.
How it looks for business roles
For the CFO:
Imagine sitting in the quarterly board meeting and instead of seeing 12 pages of spreadsheets, you’re looking at one map that shows:
Which customer accounts are actually duplicated, how many),
Which high-value segments might be fragmented across systems,
Where the bleed in billing or revenue recognition is likely coming from.
That means fewer surprises. Better forecasting. And lower risk from data chaos.
For the CMO:
You’re launching a campaign aimed at upselling to existing customers. But you don’t know who exactly has multiple identities or is fragmented across platforms. With the graph view, you can visualise the networks: which “customers” are actually the same person, which clusters are engaged, and where the outreach can hit harder. It’s smarter activation and cleaner measurement.
For the Head of Data & Analytics:
You’re responsible for data integrity, golden records, and feeding the AI/ML models. Instead of manual reconciliation and ambiguous matching rules, you’ve got structure. You can see the entities, challenge them, refine them. The graph becomes the basis for smarter matching, cleaner models, and better downstream analytics.
For the Head of CRM:
Your CRM is meant to reflect the “single customer view” but in reality it doesn’t. Now you get an entity-map that shows which records link, where duplicates exist, how the account fits into a network (household, business division, shared device). That clarity lets your team execute with confidence – segmentation, nurture flows, personalised journeys all become sharper.
What’s coming next
Over the next several weeks, we’re expanding on this foundation with updates designed to meaningfully improve accuracy and control.
1. Precision modelling
We’re updating how CustomerLinks matches records behind the scenes. By combining the graph structure with a smarter model, you’ll see more accurate entity groupings and cleaner golden records without manual corrections.
2. User-editable graphs
You’ll be able to interact directly with your graph. Remove nodes, split entities, tidy up outliers – whatever is needed to fine-tune your golden record using your domain expertise.
3. Behaviour-based matching (future roadmap)
This is where the graph database really shines. Imagine matching customers based not just on fields, but on relationships and behaviours:
- Accounts that regularly log in from the same IP
- Households that share addresses or viewing patterns
- Multiple identities that behave like the same person
This unlocks far more sophisticated identity resolution, especially for industries where behaviour tells a richer story than static fields.
4. Additional upcoming analytics features
Alongside the graph work, CustomerLinks is preparing to deliver:
- Lifetime value and churn modelling
- Segmentation improvements
- Broader business entity support
All aligned with helping Snowflake customers use their customer data more intelligently.
Why this matters for Snowflake users
If you're using Snowflake to centralise customer data, CustomerLinks now gives you a visual, intuitive, and increasingly smart layer that does the heavy lifting of entity resolution. The result is a cleaner, more accurate customer foundation for analytics, activation, AI/ML, and more.
Here’s the scenario:
You’re a company with tens of millions of customer records across CRM, billing, product usage, digital behaviour. You store it all in Snowflake. You know your data could deliver more: better segmentations, cleaner campaigns, smarter churn predictions. But the first step is messy identity resolution. With CustomerLinks’ graph view you see the mess. Then you fix the mess. Then you scale the insight.
This is just the first step. With Snowflake business intelligence and events on the horizon, you’ll see these features evolve quickly as we strengthen CustomerLinks into the most transparent and intelligent identity layer built for Snowflake.
Stay tuned—more updates are already in motion.
Practical makes powerful.
Lumilinks for Snowflake.
