KPI Tree

Apollo Metric

Sales Engagement

Contact engagement score is a composite metric that quantifies how actively a contact is interacting with your outbound efforts. It weights email opens, link clicks, replies, call connections, and meetings booked from Apollo sequences to produce a single score indicating buying intent and responsiveness.

ApolloSales Engagement

Contact Engagement Score

Contact engagement score is a composite metric that quantifies how actively a contact is interacting with your outbound efforts. It weights email opens, link clicks, replies, call connections, and meetings booked from Apollo sequences to produce a single score indicating buying intent and responsiveness.

Why contact engagement score matters for Apollo users

Apollo generates a wealth of engagement signals across email opens, clicks, replies, and call outcomes, but interpreting these signals individually is time-consuming and inconsistent between reps. A composite engagement score standardises how the team evaluates contact responsiveness, making it possible to rank and prioritise contacts objectively rather than relying on gut feel.

High engagement scores correlate strongly with conversion to meetings and opportunities. By surfacing contacts with rising scores, reps can focus follow-up on the contacts most likely to convert, while deprioritising contacts who are not engaging despite multiple touches. This drives efficiency across the entire outbound motion.

Understand and act on contact engagement score with KPI Tree

Sync Apollo email event data (opens, clicks, replies) and call outcomes into your warehouse via ETL. KPI Tree calculates engagement scores by weighting each interaction type and aggregating across all sequence touches for each contact.

Add contact engagement score to your metric tree as a leading indicator upstream of meeting booked rate and lead-to-opportunity conversion. Assign ownership to individual reps, set alerts for contacts whose scores spike (indicating hot leads), and compare engagement distributions across sequences to optimise messaging.

Get started with your Apollo data

Data Warehouse
SnowflakeBigQueryDatabricksRedshift

Connect your existing warehouse where Apollo data already lands.

Professional Services
FivetranSnowflakedbt

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.

Related Apollo metrics

Email Open Rate

Sales Engagement

Metric Definition

Email Open Rate = (Emails Opened / Emails Delivered) x 100

Email open rate is the percentage of delivered outbound emails from Apollo sequences that recipients open. While affected by email client privacy features like Apple Mail Privacy Protection, it remains a useful directional indicator of subject line effectiveness and sender reputation when tracked consistently over time.

View metric

Email Response Rate

Sales Engagement

Metric Definition

Email Response Rate = (Emails Replied / Emails Delivered) x 100

Email response rate measures the percentage of delivered outbound emails that receive a reply from the recipient. Unlike open rate, response rate is a definitive engagement signal that indicates genuine interest or objection, making it one of the most reliable leading indicators of pipeline generation from Apollo sequences.

View metric

Contact Lifecycle Analysis

Sales Engagement

Metric Definition

Contact lifecycle analysis tracks how contacts move through defined stages of outbound engagement: from initial list addition, through sequence enrolment, first touch, engagement, meeting booked, and opportunity creation. It identifies bottlenecks, drop-off points, and the average time contacts spend in each stage.

View metric

Sequence Performance Analysis

Sales Engagement

Metric Definition

Sequence performance analysis evaluates and compares Apollo sequences across their full set of engagement and outcome metrics: delivery rates, open rates, reply rates, positive reply rates, meetings booked, and pipeline generated. It identifies which sequences produce the best results and which should be revised or retired.

View metric

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