A CRM that actually knows your customers.
RFM scoring, persona inference, public-data enrichment, full purchase history. Klaviyo's reach, Outer Signal's depth, and a real RFM tool, on data you already own. Segments that flow back into ad platforms, email, CS macros, and CCEN agents.
The shape of crm in CCEN.
Customers is the record for the people who buy from you. Identity is resolved across channels, email, phone, and address. Every order, every return, every ticket, every browse session attaches. RFM scores recompute nightly. Persona inference runs on the customer profile. Segments are first-class and propagate everywhere segments are used. B2B account hierarchies (parent account, child contacts) are first-class for wholesale-and-DTC merchants.
Identity resolved across the stack.
A customer who buys on Shopify, then on Amazon under a married name, then in retail with a different email, is one person. CCEN resolves identity by exact matches on email and phone, and fuzzy matches on name and address. Every match is reviewable and reversible.
When the resolution is confident, orders fold into a single customer record with a channel breakdown. LTV reflects every channel. Retention curves include retail. The marketing team stops asking which Shopify customer and which Amazon customer are the same person.
Identity rules are tunable. Tighten the threshold for B2B buyers where company-level identity matters more than individual emails. Loosen for DTC where roommates often share a billing address but are different shoppers. B2B hierarchies are first-class: a parent account holds child contacts, contracts, and credit terms.
Recency, frequency, monetary, without an export.
RFM grids recompute nightly across every customer. Each customer lands in a 5x5x5 cell, with a textual segment label drawn from your taxonomy: Champions, Loyal, Potential, At Risk, Can't Lose, New. The segment appears next to the customer name in every other CCEN surface.
Click any cell to open the cohort. From there, push to a Klaviyo flow, a Meta custom audience, a Google customer match list, or a CCEN agent's input. The segment is one record, syndicated everywhere it needs to be.
Cohort retention curves render alongside the RFM grid. Acquisition month on the x axis, retained percentage on the y. Drill into a cohort and the underlying customers list opens. The curve is the customers, not a sample.
Persona inference on the real profile.
Public-data enrichment cross-references your customer table with public sources for household composition, career signals, geo, and lifestyle indicators. Inferences run with explicit confidence scores. Nothing presented as fact is unverified, and the source is always attached.
Personas are merchant-defined. You set the taxonomy: 'mom-entrepreneur', 'gift-buyer', 'collector', 'professional reseller', 'institutional'. The inference engine tags customers against the taxonomy with a confidence band. The marketing team can tune by tag.
Persona feeds back into product recommendations, CS macros, and replenishment forecasts. A 'gift-buyer' Christmas spike is not a frequency anomaly; it is the seasonal pattern of that segment, modeled correctly.
Three things Klaviyo and Outer Signal, running side by side, will not do.
One identity across every channel
Shopify, Amazon, retail POS, all collapse into one customer record. LTV reflects every channel. The marketing team stops guessing which records are the same person.
RFM cells that push to Meta and Klaviyo
Click an RFM cell, the cohort opens, and push it to a Klaviyo flow, a Meta custom audience, or a Google customer match list. The segment is one record, many destinations.
Persona inference with sources attached
Confidence-scored persona tags from your customer table and public sources. Sources attached on every inference. No black-box claims, no surprises in your audit.
Customer intelligence, on real data.
Cross-channel identity
Exact matches on email and phone, fuzzy matches on name and address. Reviewable, reversible.
RFM grid
Nightly recompute. Cell-clickable cohorts. Segments syndicated to Klaviyo, Meta, Google, CS macros.
LTV and cohort retention
Live cohort curves by acquisition month, channel, segment. The curve is the customers, not a sample.
Persona inference
Merchant-defined taxonomy. Confidence-scored. Sources attached. Tunable per segment.
Public-data enrichment
Household, career, geo, lifestyle. Confidence scores. Source attribution. No black-box claims.
Segments anywhere
Push to ad platforms, email tools, CS macros, CCEN agents. One record, many destinations.
Full timeline
Every order, return, ticket, browse session, message. One scrollable timeline per customer.
Lifecycle triggers
First purchase, second purchase, lapse-detection, win-back, churn. Triggers fire downstream actions.
Consent and privacy
GDPR and CCPA primitives. Right-to-erase. Consent flags. Region-specific retention windows.
What a real customer profile looks like in CCEN.
Most CRMs treat a customer as a row in a database. CCEN treats a customer as a record with the rest of ops attached: orders across every channel, RFM segment, persona tags, recent tickets, last touch, last return, and the agents that ran on them. Here is what an account exec sees when they open a profile cold.
Email, ads, support, and the warehouse-side ML.
Customers segments push to your ad platforms and email tool, render in your helpdesk, and export to your warehouse-side ML stack.
“We were running Klaviyo and Recart and Outer Signal and a custom RFM spreadsheet. CCEN replaced all four. The persona tags actually feed back into our merchandising, which they never did before.”
“Persona inference moved from a deck I argued about with my agency to a column on my customer table. The buying team now reads it. The Christmas plan started with the personas, not the volume.”
A real CRM on your data.
Walk through identity resolution, RFM, persona inference, and segments running on a customer table from your own brand.