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.

We reconcile customer identities, standardise account hierarchies, connect interactions to revenue and service outcomes, and build Power BI reporting that helps teams understand who their customers are, how relationships are changing, and where attention is needed.

Discuss Your Customer Data Review Your CRM Reporting

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
  • Ecommerce, retail, B2B, and service channels maintain separate customer views

What Datazeb can deliver

Customer 360 Reporting

Bring approved customer activity, revenue, orders, pipeline, support, and engagement into one management view.

CRM Analytics

Improve visibility across accounts, opportunities, contacts, activities, stages, owners, territories, and pipeline quality.

Customer Master Data

Reconcile customer IDs, names, account hierarchies, parent-child relationships, and duplicate records.

Account Performance

Analyse revenue, growth, margin where available, pipeline, support activity, products, services, and account trends.

Retention & Activity Analytics

Identify active, inactive, returning, declining, or at-risk customer patterns where data supports the analysis.

Segmentation

Create practical customer segments based on commercial, behavioural, geographic, service, or lifecycle criteria.

Customer-Service Analytics

Connect tickets, issues, response, service activity, and customer context where approved and useful.

Managed Customer Analytics

Maintain mappings, reports, CRM logic, new sources, and customer-data quality over time.

From fragmented customer records to one governed view

  1. Customer Sources
  2. Match & Map
  3. Customer Hierarchy
  4. Trusted Customer Model
  5. Power BI / Action

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.

CRM analytics

  • Accounts
  • Contacts
  • Opportunities
  • Activities
  • Pipeline stage
  • Expected close
  • Account owner
  • Territory
  • Lead source
  • Opportunity ageing

CRM analytics should expose data-quality and process issues instead of treating every field as reliable simply because it exists in the CRM.

Customer identity and duplicate resolution

Create 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

  • Group / parent account
  • Subsidiaries
  • Branches
  • Locations
  • Business units
  • Billing entities
  • Operating entities

Hierarchy design helps management understand the total relationship while preserving the detail needed by local account teams and finance.

Revenue and customer performance

Connect customer relationships to actual commercial outcomes

  • 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

The commercial definitions should align with finance and sales so CRM activity is not confused with booked or recognised revenue.

Retention and customer activity

  • New customers
  • Returning customers
  • Inactive customers
  • Declining accounts
  • Repeat purchase
  • Recency
  • Frequency
  • Time since last activity

Retention logic should follow the business model. A valid ‘inactive’ threshold can differ significantly between ecommerce, consulting, healthcare, wholesale, and subscription businesses.

Customer segmentation

Use segmentation to support decisions, not just create labels

Customer segments may be based on:

  • Revenue
  • Margin
  • Product mix
  • Geography
  • Lifecycle
  • Frequency
  • Account type
  • Service intensity
  • Channel

Segments should be explainable and connected to a real sales, service, marketing, or management decision.

Customer-service analytics

  • Ticket volume
  • Open vs closed
  • Response / resolution time
  • Issue category
  • Repeat issues
  • Customer-linked service history
  • Escalations
  • Service trend by account

Service KPIs should be defined around the client’s actual support model and should not imply universal benchmark targets.

Sales and customer alignment

Connect pipeline, activity and customer outcomes

  • Pipeline by account
  • Open opportunities
  • Sales activity
  • Won / lost opportunities
  • Actual revenue
  • Cross-sell / service mix
  • Account growth

Explore Sales Analytics

Customer and finance alignment

Where approved data is available, customer reporting can connect:

  • Invoices
  • Receivables
  • Revenue
  • Margin
  • Credit / payment context where appropriate
  • Refunds / adjustments

Accounting treatment remains under finance governance. Datazeb provides the reporting and data integration layer.

Multi-channel customer view

  • CRM
  • Ecommerce
  • Retail / POS
  • Marketplaces
  • Support
  • Billing / finance
  • Projects / service delivery

A common customer layer helps preserve channel-specific detail while creating a more consistent enterprise view.

Customer data quality

  • Duplicate customers
  • Missing owner
  • Invalid or stale contacts
  • Unmapped accounts
  • Broken parent-child relationships
  • Missing segment
  • CRM / finance mismatches
  • Unassigned activity

Customer analytics is often one of the fastest ways to expose where CRM discipline and master-data processes need improvement.

Customer 360 for AI

AI becomes more useful when customer context is trusted

A governed customer model can support selected use cases such as:

  • Account briefing assistants
  • Customer-support context retrieval
  • Natural-language questions over approved customer metrics
  • Customer / product knowledge assistants
  • AI-assisted exception summaries

AI should use approved curated data and should not expose restricted customer information outside authorised access.

Existing CRM reporting review

Datazeb can review:

  • CRM dashboards
  • Power BI reports
  • Sales spreadsheets
  • Customer mapping files
  • Finance-customer mappings
  • Account hierarchies
  • Current KPIs
  • Known data-quality issues

The result may be better reporting, customer master-data work, CRM clean-up, source integration, or a phased Customer 360 model.

Why Datazeb for Customer 360

  • Strong CRM, API, SQL, Python, Power BI, and data-engineering capability
  • Customer-data quality and master-data thinking built into the analytics work
  • Ability to connect CRM activity to actual finance and operational outcomes
  • Practical account hierarchy and identity-resolution design
  • AI and automation extensions where appropriate
  • Flexible project and managed-support models
  • Senior-led global delivery

Engagement options

  • CRM reporting assessment
  • Customer 360 analytics project
  • Customer master-data mapping
  • CRM / finance integration
  • Account hierarchy design
  • Retention / customer activity analytics
  • Customer-service reporting
  • Managed CRM analytics support

How we work

  1. Understand
    Clarify customer processes, CRM usage, source systems, account structures, users, commercial questions, and data-quality pain points.
  2. Map
    Document customer IDs, account hierarchy, CRM fields, finance customers, support sources, channels, and ownership.
  3. Reconcile
    Align customer identities, parent-child relationships, commercial definitions, and authoritative sources.
  4. Build
    Develop integrations, governed customer models, Power BI reporting, validation, and selected alerts or automation.
  5. Validate & Improve
    Reconcile outputs with sales, finance, service, and management stakeholders and refine the model as customer data changes.

FAQ

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.

Do you provide ongoing support?

Yes. Managed support can cover CRM changes, new reports, customer mappings, integrations, data-quality issues, and continuous improvement.

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.

Discuss Your Customer Data Review Your CRM Reporting