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Customer 360, propensity, churn, and fraud — built on a record that actually resolves.
The classic mid-sized machine-learning setup: customer-by-customer scores that drive retention, marketing, and risk decisions. The model isn't the hard part — it's making each customer show up as one clean record underneath, the pipeline that feeds the model, and the way you keep the scores accurate after launch.
What this means for you
If your sales, billing, and support systems each tell a different story about the same customer, this is about building one trustworthy view of every customer first — then turning it into scores that tell you who's likely to buy, leave, or pose a risk.
A customer score is only as trustworthy as the customer record under it.
Most likelihood-to-buy, customer-churn, and fraud projects we get pulled into don't fail at the model. They fail because three systems disagree about who the customer is, and nobody noticed until the campaign list was already out the door.
Customer 360 is two layers stacked. The first makes the same person or account show up as one clean record across your CRM, billing, support, product, and web systems. The second turns that clean view into scores the business can act on — likelihood to buy, likelihood to leave, fraud risk, and lifetime value. We build both, with a shared, reusable library of the data your models rely on (a Feature Store) sitting between them, so the same trusted data feeds training, bulk scoring, and real-time scoring. The model itself is almost the easy part.
What separates a notebook score from a system the business runs on.
These are the pieces that have to exist for customer-by-customer scores to be trustworthy in everyday use.
Making each customer one clean record
Matching the same customer across CRM, billing, support, product usage, and outside sources — both exact matches and close ones. Clear, controlled rules for which details win when records conflict. One customer ID that your data system, your model, and your downstream tools all agree on.
A shared library of model data, not a folder of notebooks
The inputs your models use are defined once, in one place, and reused for both training and scoring (in bulk and in real time). This removes a common, silent failure: a model fed slightly different data in testing than it gets in real use — the thing that quietly wrecks churn and fraud models that looked fine in testing.
Keep track of every version — of the data, the inputs, and the model
Every score can be traced back to the exact training data, input definitions, and version of the model that produced it. MLflow or a Snowflake equivalent. Being able to audit and undo isn't optional for scores that drive marketing spend or fraud decisions.
Getting the scores into the tools your teams use
Scores aren't useful sitting in a database table — they have to land in the tools your teams use day to day: Salesforce, Braze, the call-center routing system, or the fraud-review queue. We build the pipelines that push them there, plus the reliability guarantees that come with them.
A real pipeline of actual outcomes
What actually happened — who left or stayed, who bought or didn't, which alerts were real fraud or false alarms — flowing back into your data on a set schedule. Without those real outcomes, you can't measure how well the model is doing and you can't retrain it. Many 'modeling projects' turn out to be 'first capture the real outcomes' projects in disguise.
Governance, consent, and a plain-English record for each score
Marketing consent and do-not-contact flags enforced before any score is used. A short written record for each score covering who it was trained on, fairness checks, and what it's meant for. An audit trail whenever a score drove a decision that affected a customer.
The customer-level scores we ship most often.
Patterns we've shipped repeatedly. Nothing exotic — the discipline is what makes them work.
Churn and retention scoring
Scoring which subscriptions, accounts, or contracts are likely to leave. The score lands in your CRM and marketing tools, skips accounts you're already working, and feeds the save-team's results back into the next round of training.
Likelihood-to-buy and next-best-action
Scoring who's likely to buy, upgrade, or respond. Used to prioritize sales reps' call lists, target campaigns, and route incoming leads. Tied tightly to the shared data library so marketing and sales always see the same version of the score.
Fraud and anomaly scoring
Real-time scoring (under a tenth of a second) for transactions, account takeovers, and chargeback risk. Live data feeding the model, safety cut-offs if something breaks, and a loop that feeds human reviewers' decisions back into the next round of training.
Customer lifetime value (LTV) and segmentation
Predicted lifetime-value tiers that feed ad-spending limits, customer segments, and where to focus retention spend. Refreshed on a schedule that matches your business cycle, not an arbitrary default.
Signals you're not ready to ship customer-level scores.
Candid red flags. If two or more are true, the right work is foundation work first.
Three systems disagree about who your customers are
If CRM, billing, and support each carry a different version of the same customer, every score you build inherits that confusion — and pushing it into your other tools spreads it further. Making each customer one clean record is the project that comes before the modeling project.
There's no pipeline of actual outcomes
If what actually happened — who left, who converted, which alerts were real fraud — doesn't make it back into your data on a known schedule, you can't measure how well the model is doing and you can't retrain it. That pipeline has to come first.
No owner for the score after launch
Scores quietly get less accurate over time as the underlying data shifts. If no analyst or engineer is responsible for watching the dashboard, the model starts decaying the day after launch — and the marketing team finds out in a board meeting.
The downstream tool can't accept the score
If Salesforce, the call-center system, or the fraud queue needs a person to type the score in by hand, automating the model saves nothing. Fix that connection first — or pick a different first use case.
Three reversible phases.
Old scoring (or the manual rules it's replacing) stays live until the new system matches or beats it on the business metric.
See the full engagement modelCustomer 360 readiness assessment
Quoted by scopeWe take stock of your source systems, check how well each customer resolves to one record, review whether real outcomes flow back in, and give a go / fix-this-first / wait call for each use case. You leave with an estimate for any groundwork and a staged plan.
Clean-record layer & shared data library
Quoted by scopeBuilding the clean single record per customer, one agreed customer ID, the shared, reusable library of model data (on Snowflake or Databricks), the pipeline of real outcomes, and the test set that checks the model is right. Proven end-to-end with one trial score first.
Model build, activation & enablement
Quoted by scopeThe first scoring model trained, set up to run (in bulk or real time), pushed into the tool your team actually uses, and monitored for getting less accurate over time. Working side by side with your team on the written guides. Post-launch support is quoted based on your needs.
The questions we get most often.
Do we need a CDP first?
Almost never. ('CDP' means a customer data platform — a separate product for unifying customer data.) Most mid-sized teams who buy one for this end up with an expensive pipe and still have to do the real work: making each customer one clean record and building the shared data library. We build the same thing inside the data system you already own — Snowflake or Databricks — and push the results into your day-to-day tools. Cheaper, easier to govern, and your data stays put.
Snowflake or Databricks for customer-level scoring?
If your data and security already live in Snowflake and the work is mostly bulk scoring, Snowpark ML and Cortex are usually the right answer — the clean-record layer, the model inputs, training, and pushing scores out all stay in one place. If the work includes live streaming, real-time fraud scoring, or models further-trained on your data, Databricks (the shared data library, live model serving, and MLflow) typically wins. The readiness check recommends one based on your actual work, not on any vendor relationship.
Real-time fraud — what latency can we hit?
Most fraud and account-takeover cases need an answer in under a tenth of a second. We hit that with a live version of the shared data library (Databricks Feature Serving, or DynamoDB / Redis in front of Snowflake) and a live model setup with reserved capacity and safety cut-offs. The exact design depends on how many requests per second you expect and how fresh the data has to be.
How do you handle bias, fairness, and regulatory exposure?
A short written record for every live score, fairness checks against protected groups where it matters (lending, insurance, employment), do-not-contact rules enforced at the point of use rather than buried in the model, and an audit trail wherever a score drove a decision that affected a customer. For regulated work we partner with your compliance team on the documentation rather than handing you a generic slide deck.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.