Connect spreadsheets, CRM, and finance data in minutesTrends, KPIs, forecasts & specialized analyticsCompetitor, customer & sentiment intelligenceBuild dashboards without a BI teamReports and board decks from your analysesAgentic AI for plain-English Q&A when you want itStart free — no credit card required
Analytics6 min

Customer 360

Customer 360 is the customer-intelligence module of the Analytics track. Connect a customer dataset, map a handful of columns, and it builds a complete picture of your base — RFM segmentation, churn prediction, lifetime-value modeling, and health scoring — then turns it into AI recommendations for retention, upsell, and growth. All of it over the time period you choose.

At a glance

Level
Beginner
Duration
6 min
Product
Intelligence Navigator
Prerequisites
A customer dataset in Data Hub
Audience
Owners, founders, firms, technical & non-technical teams
Subjects
RFM segmentationchurn predictionCLVhealth scoringAI recommendations

Map your customer data

Point Customer 360 at a connected dataset and map three essentials — a customer ID, a transaction date, and a metric like revenue or sales. Add optional fields for richer analysis: customer name, status, product, tenure, first-purchase date, and demographics like region, age, gender, and acquisition channel. Then pick the time period — from the last 7 days to year-to-date or the last 36 months.

  • Required: customer ID, transaction date, and a numeric metric (revenue, sales, amount, profit)
  • Optional: demographics and attributes — region, age, gender, channel, product, tenure
  • Time period from last 7 days to year-to-date or the last 36 months

See Customer 360 in action

Watch: map your customer fields, run the analysis, and read the AI insights and segments.

Five analyses in one run

A single run produces five connected analyses of your customer base. Each is computed from your data and explained in plain language with charts and an AI Insights panel.

  • Revenue Intelligence — revenue concentration (CR4), top-customer dependency, and growth signals
  • Health Scoring — a composite score per customer blending engagement, value, and retention signals
  • Cohorts & Retention — how groups of customers retained and spent over time
  • Behavior Patterns — recency, frequency, and monetary (RFM) segments built with ML clustering
  • ML Churn Model — at-risk customers with probability scores and the key drivers behind them

Read the intelligence

Results open with headline KPIs — average customer value, revenue concentration, portfolio health, and the count of at-risk customers — followed by distributions, value tiers, top customers, and a portfolio CLV projection. AI insights translate it into action: who's driving revenue, who's about to churn, and where to focus retention and expansion.

  • Concentration analysis shows how much of revenue depends on your top 5% and top 20%
  • Health-score distribution flags the customers below a health score of 50
  • CLV projection estimates 12-month forward-looking value for your top customers

Save and share it

Save any result straight to a Dashboard, a Presentation, or Reports, print a clean A4 report, copy a table to the clipboard, or download the analysis as Excel. Search individual customers by name, ID, or region, and open any one for a detailed profile view.

Best practices

  • Map the demographics columns (region, age, gender, channel) — they unlock richer segmentation and filters
  • Run on at least 12 months of history so RFM segments and churn signals have enough data to be reliable
  • Watch concentration (CR4) — if your top 5 customers carry most of revenue, retention there is the priority
  • Act on the churn list first: probability-ranked at-risk customers are the highest-leverage saves

Next steps

With a clear view of your customers, Dynamic Pricing optimizes the prices you charge them — next in the Analytics track.