Customer 360 & CRM Analytics
Bring customer, sales, service and revenue data into one trusted view
Datazeb helps organisations connect CRM, finance, support, ecommerce, marketing, and operational data into a clearer picture of customer performance and activity.
How it works
From fragmented customer records to one governed view
A useful Customer 360 view depends on identity resolution and hierarchy design. The same organisation, branch, buyer, contact, or account should not be represented differently across every source.
- 01Customer Sources
- 02Match & Map
- 03Customer Hierarchy
- 04Trusted Customer Model
- 05Power BI / Action
Common customer-data problems
- The same customer exists under multiple IDs or names
- CRM accounts do not reconcile with finance customers
- Sales activity and actual revenue are reported separately
- Support history is disconnected from commercial reporting
- Parent and subsidiary relationships are unclear
- Customer segments differ by department
- Account owners change without clean history
- Customer retention or inactivity is difficult to measure
What Datazeb can deliver
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Customer 360 Reporting
Bring approved customer activity, revenue, orders, pipeline, support, and engagement into one management view.
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CRM Analytics
Improve visibility across accounts, opportunities, contacts, activities, stages, owners, territories, and pipeline quality.
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Customer Master Data
Reconcile customer IDs, names, account hierarchies, parent-child relationships, and duplicate records.
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Account Performance
Analyse revenue, growth, margin where available, pipeline, support activity, products, services, and account trends.
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Retention & Activity Analytics
Identify active, inactive, returning, declining, or at-risk customer patterns where data supports the analysis.
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Segmentation
Create practical customer segments based on commercial, behavioural, geographic, service, or lifecycle criteria.
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Customer-Service Analytics
Connect tickets, issues, response, service activity, and customer context where approved and useful.
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Managed Customer Analytics
Maintain mappings, reports, CRM logic, new sources, and customer-data quality over time.
Unified customer view
Every system that touches a customer, in one view
Customer identities are matched across systems first, so every measure attaches to the same customer.
Sources
- CRM
- Sales and orders
- Support
- Finance and billing
- Marketing
- E-commerce
Match and standardise
- Customer identity matching
- Account hierarchies
- Shared definitions
Unified customer view
- Value and profitability
- Engagement and service history
- Retention and churn risk
Who uses it
- Sales and account teams
- Service
- Leadership
In detail
Explore the detail
CRM analytics
CRM analytics should expose data-quality and process issues instead of treating every field as reliable simply because it exists in the CRM.
- Accounts
- Contacts
- Opportunities
- Activities
- Pipeline stage
- Expected close
- Account owner
- Territory
- Lead source
- Opportunity ageing
Customer identity and duplicate resolutionCreate a controlled way to decide which records belong together
- Exact customer IDs
- Normalised names
- Email / phone where appropriate
- Address attributes
- Tax / registration identifiers where approved
- Parent / child relationships
- Fuzzy matching as a review signal
Parent-child and account hierarchy
Hierarchy design helps management understand the total relationship while preserving the detail needed by local account teams and finance.
- Group / parent account
- Subsidiaries
- Branches
- Locations
- Business units
- Billing entities
- Operating entities
Revenue and customer performanceConnect customer relationships to actual commercial outcomes
The commercial definitions should align with finance and sales so CRM activity is not confused with booked or recognised revenue.
- Revenue by customer
- Growth / decline
- Margin where finance data supports it
- Order frequency
- Average order / deal value
- Product / service mix
- Revenue concentration
- Account-owner performance
Retention and customer activity
Retention logic should follow the business model. A valid 'inactive' threshold can differ significantly between ecommerce, consulting, healthcare, wholesale, and subscription businesses.
- New customers
- Returning customers
- Inactive customers
- Declining accounts
- Repeat purchase
- Recency
- Frequency
- Time since last activity
Customer segmentationUse segmentation to support decisions, not just create labels
Customer segments may be based on: Segments should be explainable and connected to a real sales, service, marketing, or management decision.
- Revenue
- Margin
- Product mix
- Geography
- Lifecycle
- Frequency
- Account type
- Service intensity
- Channel
Customer-service analytics
Service KPIs should be defined around the client's actual support model and should not imply universal benchmark targets.
- Ticket volume
- Open vs closed
- Response / resolution time
- Issue category
- Repeat issues
- Customer-linked service history
- Escalations
- Service trend by account
Sales and customer alignmentConnect pipeline, activity and customer outcomes
- Pipeline by account
- Open opportunities
- Sales activity
- Won / lost opportunities
- Actual revenue
- Cross-sell / service mix
- Account growth
FAQ
Frequently asked questions
Can you combine CRM and finance customer data?
Yes, where customer identifiers, mappings, or matching rules can be established reliably.
Can you help remove duplicate CRM accounts?
Yes. Datazeb can profile duplicate candidates and build matching logic, with human review where uncertain merges could cause problems.
Can you build parent-child customer hierarchies?
Yes. Group, subsidiary, branch, location, or other customer hierarchies can be designed where the business relationships are understood.
Can you measure retention or inactive customers?
Yes. The definition should first be agreed for the specific business model so the result is meaningful.
Can customer data support AI assistants?
Yes, where the data is curated, permissioned, and suitable for the intended use case.
Create one clearer view of the customer relationship
If customer information is spread across CRM, finance, support, ecommerce, spreadsheets, and operational systems, tell us where the gaps appear today. Datazeb can help reconcile customer identities, connect commercial and service activity, and build a reporting model that gives teams a more complete and trusted view.