Customer Data Analytics
Customer Data Analytics Services
Bring customer records, orders, product usage, support and marketing data into one view, then use it to segment customers, measure retention and lifetime value, and decide where to invest next.
01 Match
Build a single customer view by matching CRM, order, product and marketing records to the same customer.
matched on emailmatched on phone
02 Segment
Segment customers by value, behaviour and lifecycle stage so teams can act on each group differently.
03 Measure
Measure retention, churn, repeat purchase and lifetime value with documented definitions.
Scope
Customer analytics work we take on
Customer data analytics turns scattered records into answers about who your customers are, what they do and what they are worth.
Unified customer data
Match customers across CRM, e-commerce, billing, product and support systems, with clear rules for duplicates and identity.
What You Get
What a customer analytics engagement includes
Each engagement leaves you with data, definitions and views your team can keep running.
customer_model.sql
Customer data model
Clean customer, order and event tables with documented matching rules.
customers customer_id · email_norm · first_seen orders order_id · customer_id · total events customer_id · name · ts -- match: email_norm, then phone_e164
segments.md
Segment definitions
Value, behaviour and lifecycle segments with the logic written down.
Champions bought in last 30d · 6+ orders Loyal bought in last 60d · 3+ orders At risk quiet 90–180d · 3+ orders -- reviewed quarterly
retention_clv
Retention and CLV views
Cohort retention, churn and lifetime value dashboards with known limitations noted.
activation_plan
Activation plan
Which segments go to which tools, how often they refresh and who owns them.
At riskHubSpotdailyChampionsMeta AdsweeklyLapsedEmailweekly
Process
From scattered records to customer decisions
We start with the decisions you need to make about customers, then build only the data model those decisions need.
Step 01 of 05
Agree the questions
Which customers to acquire, keep or grow, and what decision each answer should change.
Step 02 of 05
Map the sources
List every system holding customer data, its identifiers, freshness and known quality problems.
Step 03 of 05
Model the customer
Join and clean the data into customer, order and event tables in your warehouse.
Step 04 of 05
Analyse and report
Build segments, cohorts and lifetime value views, with dashboards your teams will use.
Step 05 of 05
Activate and review
Send segments to the tools that act on them, then review results and refine.
Best Fit
For businesses with customer data in too many places
Understand repeat purchase, churn and which customers are worth the most.
One customer table. 3 of 7 columns matter to E-commerce and subscription brands; everyone reads the same definitions.
| Customer | CRM owner | Orders | Product use | Revenue | Churn risk | Segment | Lifetime value |
|---|---|---|---|---|---|---|---|
| Meera Iyer | R. Kapoor | 7 | 12 / wk | ₹28,640 | Low | Loyal | ₹9,200 |
| Northwind Ltd | A. Shah | 31 | 140 / wk | ₹4.2L | Medium | Champions | ₹6.1L |
| Kabir Rao | None | 2 | 1 / wk | ₹3,100 | High | At risk | ₹1,400 |
| Brightline Co | A. Shah | 18 | 22 / wk | ₹1.8L | High | Needs attention | ₹90,000 |
Illustrative rows
Proof
Why work with Multivariate on customer data
Customer analytics fails when the data model is fragile or nobody trusts the definitions. We work across tracking, warehouses, CRM integrations and dashboards, so the analysis is built on data the team can maintain.
Exhibit A
We document how customers are matched and where matching is uncertain.
Exhibit B
We separate measured results from modelled estimates such as predicted lifetime value.
Exhibit C
We build on your existing stack where we can, rather than adding tools for their own sake.
FAQ
Customer data analytics FAQs
Multivariate analytics team
Replies within 24 hours
What is customer data analytics?
How is it different from marketing analytics?
Do we need a customer data platform first?
Which tools do you work with?
How do you handle customer privacy?
Suggested questions
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