Know every customer. Act on the signals that matter.
One view of each customer across sales, service, marketing, and billing, plus the signals that say who will buy, leave, or expand.
What gets in the way
Customer data is split across CRM accounts and contacts, billing, support tickets, web analytics, and email tools.
Duplicates and mismatched IDs mean the same customer appears three times with three histories.
Marketing and sales work from different lists, so outreach overlaps and good accounts slip.
The questions that matter most take days to answer, if anyone can answer them at all. Who is about to leave? Who is ready to buy more? Which campaigns brought in customers who stayed?
What we measure: match rate across systems, churn and expansion rates by segment, and time from signal to action.
Signs you need it
Three versions of the truth
Sales, finance, and support each have a different customer count, and nobody trusts any of them.
Churn is a surprise
You learn a customer is leaving when they cancel, not months before.
Lists built by hand
Someone exports spreadsheets every month to decide who gets a call or an offer.
Spend you can't trace
You can't tie marketing spend to customers who renewed or expanded.
Deliverables
Identity resolution: one customer ID across CRM, billing, support, and the web.
A customer 360 table in your lakehouse with history and segments.
Starter models for churn risk, propensity to buy, and lifetime value, tracked in MLflow.
Signals pushed back to CRM fields and lists your team already uses.
A short guide to reading and acting on each signal.
Your customer data, in your lakehouse
We build Customer Intelligence on Databricks when it fits. Lakeflow pipelines load and clean the data. Delta tables keep history. Unity Catalog controls who sees which fields and tracks lineage from source to score. Genie lets managers ask plain-English questions of the customer 360 table, answered from governed data. Already on another platform? The same design works there. We tell you plainly if Databricks is not the right fit. The tables this work reads live in the Knowledge Lakehouse.
Start with the decisions, not the data
We start by naming the three to five decisions you want to make better. Examples: who gets a renewal call this month, which accounts get an upsell offer, where to cut ad spend.
Then we work backward to the data those decisions need. Nothing gets built that doesn't feed a decision.
Your team reviews the match rules and the scores before anything reaches the CRM. If a score can't be explained, it doesn't ship.
Steps
Connect
Pull CRM objects (accounts, contacts, opportunities, activities), billing, and support history.
Match
Resolve identities with clear, reviewable rules.
Model
Build segments and starter scores, tested against past outcomes.
Activate
Write scores and lists back to the CRM and marketing tools.
What runs under the hood
Ingest. Managed connectors or scheduled pulls land CRM, billing, support, and web data in raw bronze tables, unchanged.
Clean and match. Pipelines standardize names, emails, phones, and addresses, then apply deterministic rules first (same email, same billing ID) and scored fuzzy matches second. Every merge is logged and reversible.
Model. A customer 360 table holds one row per customer with history. Churn, propensity, and lifetime value models train on past outcomes and are tracked in MLflow.
Activate. Scores and segments sync back to CRM fields and marketing lists on a schedule, so people see them where they already work.
Same idea, in your lakehouse
Twilio Segment, Amperity, and Salesforce Data Cloud are built around unified customer profiles. We use the same ideas and keep the profile in your lakehouse, so you own it and are not locked into one vendor.
Questions it answers
Real estate
Which tenants show early signs of not renewing?
Hospitality
Which past guests are most likely to rebook direct for the next season?
Fintech
Which accounts are expanding usage and ready for a higher plan?
Logistics
Which shippers are moving volume away, based on lane trends?
A basic first version in 4 to 6 weeks
The range depends on the number of sources and how clean they are.
Connect and match
Sources connected, match rules agreed, first customer 360 table built.
Score
Segments and starter scores, tested against last year's real outcomes.
Activate
Scores in the CRM, a short guide for your team, and a plan for what's next.
Customer data follows your privacy rules and consent choices, with sensitive fields masked by default. Access control, lineage, privacy, and monitoring come standard. A deeper security assessment is available through a trusted local partner.
References
Amy Gallo, The Value of Keeping the Right Customers, Harvard Business Review, October 29, 2014. https://hbr.org/2014/10/the-value-of-keeping-the-right-customers
McKinsey & Company, The value of getting personalization right — or wrong — is multiplying, November 12, 2021. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying
What is Unity Catalog? Databricks documentation. https://docs.databricks.com/aws/en/data-governance/unity-catalog/
Genie. Databricks documentation. https://docs.databricks.com/aws/en/genie/
Bring one customer question.
In a free discovery session we look at your customer data together and name the first decision worth improving. Or try a working example in the Lab.