KPI Tree
Attio logoAttio Integration

Turn Attio relationship data into metric trees that reveal what drives deals forward.

Attio gives you a modern CRM with powerful data modelling and relationship intelligence. But CRM data alone doesn't tell you why deals stall, which activities predict close rates, or who should act when pipeline health changes. KPI Tree connects to your Attio data via MCP, your existing warehouse, or a fully managed data foundation - maps the causal relationships between CRM activity and revenue outcomes, and assigns clear ownership so your go-to-market team knows exactly where to focus.

From Attio data to accountable revenue metrics

Connect Attio data to KPI Tree in three ways: pull directly via MCP with no warehouse needed, connect your existing warehouse where Attio data already lands, or let our professional services team build you turn-key AI foundations in a matter of weeks (data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer).

1

Connect your Attio data

Three ways to get started, depending on your stack.

MCP
MCP

Pull metrics from Attio directly through the Model Context Protocol.

SnowflakeBigQueryDatabricks
Warehouse

Connect your existing warehouse where Attio data already lands.

Fivetrandbt
Professional Services

Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.

2

Define metrics from your Attio data

Build metrics from Attio's rich data model - deals created, pipeline value, stage conversion rates, relationship scores, activity frequency, close rates by segment. Use SQL, your dbt semantic layer, or let KPI Tree suggest metrics from discovered tables.

3

Build causal trees and assign ownership

Arrange metrics into trees that show how leading indicators drive revenue. Assign RACI ownership so every rep, AE, and manager knows which metrics they own. KPI Tree adds statistical monitoring, correlation analysis, and personalised action plans automatically.

CRM intelligence that connects activity to outcomes

Attio captures rich relationship data. KPI Tree maps the causal chain from that data to the revenue metrics your team is accountable for.

Causal trees across your deal pipeline

Map the full journey from first touch to closed-won. See which pipeline stages leak, which deal attributes predict success, and how relationship engagement correlates with revenue - all in a single visual tree with clear ownership at every node.

Leading indicators that predict revenue outcomes

KPI Tree identifies the Attio metrics that statistically predict downstream outcomes. Is deal velocity in Stage 2 a better predictor of close rate than meeting frequency? The data tells you - no guesswork, no gut feel.

Real-time alerts when CRM health shifts

Get notified when pipeline coverage drops below safe thresholds, stage conversion rates fall outside normal bounds, or deal cycle times extend beyond historical patterns. Act before the quarter is at risk.

Pipeline trees that show cause, not just status.

Attio gives you a clean pipeline view. KPI Tree adds the dimension Attio can't: causation. Why did pipeline drop this week? Was it fewer deals created, slower stage progression, or larger deals falling out? The metric tree traces the answer through connected nodes, each owned by someone accountable.

  • Visual trees connecting pipeline metrics to their upstream drivers
  • Automatic decomposition showing exactly where pipeline value changed
  • Stage-by-stage conversion metrics with ownership at each transition
  • Period comparison to quantify what changed and by how much
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Relationship intelligence meets statistical rigour.

Attio captures rich relationship data - interaction frequency, recency, engagement patterns. KPI Tree runs correlation and regression analysis across those signals, surfacing which relationship behaviours actually predict closed deals. Stop guessing which activities matter; let the statistics tell you.

  • Correlation analysis between relationship engagement metrics and revenue
  • Identify which interaction patterns predict deal progression
  • Quantify the revenue impact of relationship depth and frequency
  • Compare high-performing reps' activity patterns against the team baseline
Correlation analysis loading

Every deal stage has an owner. Every metric has a target.

CRMs track who owns deals. KPI Tree tracks who owns the metrics that determine whether deals close. Assign RACI ownership to conversion rates, cycle times, and pipeline coverage - so accountability extends beyond individual deals to the systemic health of your revenue engine.

  • RACI ownership for every metric in the revenue tree
  • Personalised dashboards showing each team member their owned metrics
  • Automated action plans when metrics move outside expected ranges
  • Manager views with team-level aggregation and individual drill-down
RACI accountability matrix loading

Attio data alongside every other revenue signal.

Revenue doesn't live in one tool. KPI Tree connects Attio deal data alongside marketing metrics, outbound activity from Apollo, billing data from Stripe, and support signals from Intercom - all in one tree. See how every function contributes to revenue and where the system breaks down.

  • Combine Attio CRM metrics with data from any warehouse-connected tool
  • Single metric tree across sales, marketing, product, and support
  • Cross-functional correlation reveals hidden dependencies between teams
  • Unified ownership model across every tool and every team

How KPI Tree uses Attio data differently

Attio is a brilliant CRM for managing relationships and deals. KPI Tree adds the analytical and accountability layer that turns CRM data into a system that drives consistent revenue growth.

From deal tracking to causal revenue analysis

Instead of pipeline snapshots and activity logs, KPI Tree builds causal metric trees that show how CRM activity drives revenue. When numbers move, you trace the cause in seconds.

Metric ownership beyond deal ownership

Attio tracks who owns each deal. KPI Tree tracks who owns the systemic metrics - conversion rates, pipeline coverage, cycle times - that determine whether the entire team hits target.

Statistical analysis your CRM can't do

Correlation matrices, regression analysis, anomaly detection, and change decomposition - run automatically across your Attio data without building custom reports or exporting to spreadsheets.

Metrics you can track

25 Attio metrics ready to add to your metric trees.

Activity Volume Trends

CRM

Metric Definition

Activity volume trends measure the total count and distribution of logged activities (emails, calls, meetings, notes) across time periods in Attio. This metric tracks whether activity levels are increasing, decreasing, or seasonal, and segments volume by activity type, rep, and deal stage to reveal effort allocation patterns.

View metric

Company Segmentation Analysis

CRM

Metric Definition

Company segmentation analysis breaks down CRM performance metrics by company attributes stored in Attio: industry, employee count, revenue range, geography, and custom fields. It identifies which company segments yield the highest conversion rates, largest deal sizes, and fastest sales cycles.

View metric

Contact Engagement Score

CRM

Metric Definition

Contact engagement score is a composite metric that quantifies how actively a contact is interacting with your team based on activity data logged in Attio. It weights emails exchanged, calls completed, meetings held, and recency of last interaction to produce a single score indicating relationship strength and buying intent.

View metric

Contact Lifecycle Analysis

CRM

Metric Definition

Contact lifecycle analysis tracks how contacts progress through defined stages in Attio: from initial creation, through qualification, active engagement, opportunity association, and customer conversion. It measures conversion rates between stages, time spent in each stage, and identifies where contacts stall or churn out of the pipeline.

View metric

Contact-to-Deal Conversion Rate

CRM

Metric Definition

Contact-to-Deal Conversion Rate = (Contacts with Active Deals / Total Qualified Contacts) x 100

Contact-to-deal conversion rate measures the percentage of contacts in Attio that progress from initial CRM entry to being associated with an active deal. It is the most direct measure of whether your lead generation and qualification processes are producing contacts that enter the sales pipeline.

View metric

Customer Acquisition Cost

CRM

Metric Definition

Customer Acquisition Cost = Total Sales & Marketing Spend / Number of New Customers Acquired

Customer acquisition cost (CAC) measures the total sales and marketing investment required to convert a prospect into a paying customer. By combining Attio's deal and activity data with cost inputs, CAC quantifies the efficiency of the entire acquisition process from first touch to closed deal.

View metric

Deal Age Distribution

CRM

Metric Definition

Deal age distribution analyses the spread of open deals in Attio by their age (days since creation or days in current stage). It categorises deals into age buckets and compares the distribution against historical close patterns to identify deals that have exceeded normal timelines and may be stale or at risk.

View metric

Deal Conversion Rate

CRM

Metric Definition

Deal Conversion Rate = (Closed-Won Deals / Total Deals Created) x 100

Deal conversion rate measures the percentage of deals created in Attio that ultimately close as won. It is the broadest measure of pipeline effectiveness, capturing the combined impact of deal qualification, sales execution, competitive positioning, and pricing across the entire sales process.

View metric

Deal Loss Analysis

CRM

Metric Definition

Deal loss analysis examines closed-lost deals in Attio to identify patterns in loss reasons, competitive losses, deal characteristics, and sales process gaps. It categorises losses by reason, stage of loss, deal size, competitor, and rep to surface systemic issues that can be addressed to improve future win rates.

View metric

Deal Size Trend Analysis

CRM

Metric Definition

Deal size trend analysis tracks the average, median, and distribution of deal values in Attio over time. It identifies whether deal sizes are growing, shrinking, or shifting across segments, revealing trends in customer willingness to invest and the effectiveness of up-sell and pricing strategies.

View metric

Deal Stage Conversion Analysis

CRM

Metric Definition

Deal stage conversion analysis measures the percentage of deals that successfully advance from one pipeline stage to the next in Attio. It calculates stage-to-stage conversion rates, time-in-stage, and identifies the specific transitions where deals most frequently stall or drop out.

View metric

Deal Velocity Analysis

CRM

Metric Definition

Deal velocity analysis examines the speed at which deals progress through pipeline stages in Attio. It measures time-in-stage at each transition, identifies stages where deals decelerate, and compares velocity across segments, deal sizes, and reps to surface factors that accelerate or slow the sales process.

View metric

Forecast Accuracy

CRM

Metric Definition

Forecast Accuracy = (1 - |Forecasted Revenue - Actual Revenue| / Actual Revenue) x 100

Forecast accuracy measures the percentage deviation between forecasted revenue (based on Attio pipeline data and probability-weighted projections) and actual closed revenue for a given period. It evaluates the reliability of the sales forecasting process and identifies systematic biases like chronic over-forecasting or under-forecasting.

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Lead Response Time

CRM

Metric Definition

Lead Response Time = Timestamp of First Rep Activity - Timestamp of Lead Creation

Lead response time measures the elapsed time between a new contact or lead being created in Attio and the first meaningful outreach by a sales rep (email, call, or meeting). It quantifies how quickly the sales team acts on new leads, which directly correlates with conversion probability.

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Lead Source Attribution

CRM

Metric Definition

Lead source attribution traces deals and revenue in Attio back to the original source that created the contact: inbound marketing, outbound prospecting, referrals, events, partnerships, or organic channels. It quantifies the pipeline and revenue contribution of each acquisition channel to inform investment decisions.

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List Performance Analysis

CRM

Metric Definition

List performance analysis evaluates how contacts and companies within Attio's lists and filtered segments perform across engagement and pipeline metrics. It compares lists head-to-head on conversion rates, deal sizes, and sales velocity to determine which segmentation strategies produce the best outcomes.

View metric

Pipeline Coverage Ratio

CRM

Metric Definition

Pipeline Coverage Ratio = Total Open Pipeline Value / Revenue Target

Pipeline coverage ratio measures the total value of open pipeline in Attio divided by the revenue target for a given period. It indicates whether sufficient pipeline exists to achieve the target, accounting for historical win rates and the time remaining to close deals.

View metric

Pipeline Health Score

CRM

Metric Definition

Pipeline health score is a composite metric that evaluates the overall quality and reliability of the sales pipeline in Attio. It combines deal age distribution, stage balance, activity recency, deal progression velocity, and coverage ratios into a single score that indicates whether the pipeline is likely to convert as expected.

View metric

Sales Pipeline Velocity

CRM

Metric Definition

Pipeline Velocity = (Number of Opportunities x Average Deal Value x Win Rate) / Sales Cycle Length

Pipeline velocity quantifies the rate at which the sales pipeline in Attio converts into revenue. It combines the number of open opportunities, average deal value, win rate, and sales cycle length into a single metric representing the revenue-generating throughput of the pipeline per unit of time.

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Revenue Attribution by Source

CRM

Metric Definition

Revenue attribution by source allocates closed-won deal revenue in Attio back to the original acquisition channel or lead source. It goes beyond pipeline attribution by measuring which sources actually produce paying customers and revenue, not just deals created, providing the most accurate view of channel ROI.

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Sales Cycle Length

CRM

Metric Definition

Sales Cycle Length = Average(Close Date - Deal Creation Date)

Sales cycle length measures the average number of days from deal creation in Attio to a closed-won outcome. It captures the full duration of the active sales process and reveals how efficiently deals move through the pipeline from qualification to close.

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Sales Rep Performance Analysis

CRM

Metric Definition

Sales rep performance analysis compares individual reps across the full spectrum of CRM metrics in Attio: activity volume, lead response time, deal creation rate, conversion rates, deal size, cycle length, and revenue closed. It identifies top performers, surfaces coaching opportunities, and reveals the specific behaviours that differentiate high and low performers.

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Task Completion Rate

CRM

Metric Definition

Task Completion Rate = (Tasks Completed On Time / Total Tasks Assigned) x 100

Task completion rate measures the percentage of assigned tasks in Attio (follow-up calls, email responses, proposal sends, contract reviews) that are completed within their due date. It reflects sales process discipline and directly impacts deal progression and pipeline health.

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Win Rate by Deal Size

CRM

Metric Definition

Win rate by deal size segments overall deal conversion rates in Attio into value brackets, revealing how close rates vary across different deal sizes. It identifies the deal value ranges where the team is most and least competitive, informing pricing strategy, qualification criteria, and resource allocation.

View metric

Workspace Utilisation Analysis

CRM

Metric Definition

Workspace utilisation analysis measures how effectively the team uses Attio's CRM capabilities: list creation and maintenance, custom field population rates, automation usage, integration activity, and data completeness. It identifies underutilised features and data quality gaps that reduce the value of the CRM investment.

View metric

Related integrations

Other data sources that work with KPI Tree.

Common questions

Attio tends to be the first CRM that founder-led sales, partnerships, and RevOps teams take seriously, and KPI Tree layers on top without forcing you to adopt a heavier stack. MCP queries Attio directly, so custom objects, lists, workflows, deals, and relationship fields flow into metric trees without an intermediate warehouse. As you mature and start syncing Attio to a warehouse via Fivetran, Hightouch, or a custom worker, KPI Tree reads the warehouse tables in place and the trees keep working. If you would rather skip the DIY stage, our professional services team builds Snowflake or BigQuery, the ELT, the dbt semantic layer, and the starter metric tree templates, so Attio becomes a production-grade analytics source without your RevOps team needing to hire a data engineer.
Any metric derivable from your Attio data: deals created, pipeline value by stage, conversion rates, deal velocity, win rates, average deal size, relationship engagement scores, activity frequency, and any custom metrics you define using Attio's custom objects and attributes.
Yes. As long as your custom objects and attributes are synced to your warehouse, KPI Tree can build metrics from them. The data model in your warehouse is what matters - KPI Tree is schema-agnostic.
With MCP, you can be pulling Attio data in minutes. If your Attio data is already in a warehouse, you can have metrics defined and trees built in under an hour. The professional services route takes longer but delivers a complete, production-ready data foundation.
Absolutely. Many teams use Attio alongside Apollo for outbound, Stripe for billing, or other tools. KPI Tree builds metric trees across all data sources in your warehouse - giving you a single view of how every function drives revenue.
No. Attio's native reporting is excellent for deal management and pipeline views. KPI Tree adds causal analysis, statistical monitoring, cross-tool metric trees, and RACI ownership - capabilities that complement Attio's built-in analytics.
KPI Tree connects to your warehouse with least-privilege read-only access. No Attio credentials are involved. All warehouse credentials are encrypted at rest with HSM-backed KMS, and your existing warehouse security policies remain fully enforced.
Especially so. Small teams can't afford to guess which activities drive revenue. KPI Tree gives a lean go-to-market team the same causal analysis and accountability that large organisations build with armies of analysts - without the overhead.

Related guides

Deep dives into the frameworks and metrics that work with Attio.

Your Attio data knows what drives deals. Make sure your team does too.

Connect Attio data to KPI Tree through your warehouse. Build causal metric trees, assign ownership across your revenue team, and let statistical analysis show you what actually moves the number.

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