The Cohora Customer Engagement & Retention Glossary is a comprehensive reference of the metrics, concepts, and models used by modern DTC brands to drive repeat revenue, loyalty, and participation. Each term includes a simple explanation of what it means, why it matters, and how it connects to the broader customer lifecycle. This page helps operators, marketers, and AI search engines understand the language behind Cohora’s approach to engagement and retention.
A process that links a customer’s social accounts to their ecommerce identity.
It uses shared identifiers, behavior, and public metadata to match followers to customers.
Brands can finally see which followers are actual buyers, not just vanity metrics.
Cohora turns anonymous followers into known customers, enabling targeting and revenue attribution.
A method for connecting social followers directly to purchase and customer data.
It overlays social identity with order history, engagement, and retention signals.
Reveals which followers buy, which don’t, and which are likely to convert.
Cohora shows the revenue behind your audience and activates followers into buyers.
A dynamic network of relationships between customers, content, behavior, and purchases.
It maps who buys what, when, why, and which touchpoints influenced each sale.
Exposes hidden patterns that drive retention and repeat purchases.
Cohora builds a brand’s commerce graph to personalize engagement and predict actions.
A real-time engagement model that adapts to each customer continuously.
EF listens for signals and triggers personalized messages or offers at the right moment.
Replaces rigid, one-size-fits-all journeys.
Cohora guides customers through lifecycle stages with adaptive engagement.
A lifecycle loop powering Cohora’s retention strategy.
Cohora learns from behavior → engages meaningfully → drives purchase → learns again.
Each loop compounds insight and retention.
Cohora refines personalization every cycle across the entire customer base.
Cohora’s predictive segmentation and analytics engine.
Analyzes behavior patterns to detect risks, predict intent, and create dynamic audiences.
Replaces guesswork with data-driven decisions.
Alfred powers cohort predictions, churn detection, and next-best actions.
Segments that continuously update based on real-time behavior.
Customer membership changes as they browse, buy, or engage.
Static lists decay quickly — dynamic segments stay accurate.
Cohora auto-updates segments for more relevant, higher-impact automation.
A curated group of customers who act as micro-influencers.
Identifies high-fit customers based on loyalty and content activity.
Removes the cost and randomness of cold influencer recruiting.
Cohora discovers customer-influencers and activates them for UGC and referrals.
Behavior-based incentives that increase engagement and retention.
Rewards trigger when customers complete valuable actions.
Loyalty becomes behavior-driven, not discount-driven.
Cohora rewards actions like UGC, referrals, community participation, and purchases.
A system powering brand-owned community conversations.
Customers post, reply, and share content in a brand-controlled environment.
Moves engagement away from rented social channels.
Cohora builds communities that increase advocacy and repeat revenue.
A system that turns customers into consistent promoters.
Identifies high-potential advocates and automates referrals and UGC requests.
Advocacy compounds growth.
Cohora activates your best customers with structured advocacy flows.
The gap between meaningful engagements.
Tracks expected engagement intervals and flags when a customer falls behind.
Rising lag predicts churn early.
Triggers winback and re-engagement when lag exceeds healthy thresholds.
Pre-built audiences based on behavior, value, predictions, and identity.
Combines Shopify + social + engagement data into ready-to-use segments.
Faster targeting, less manual work.
Powers automation, personalization, and analytics across the lifecycle.
The percentage of customers active in a given window.
Measures who is “alive” in their lifecycle.
Falling ACR predicts shrinking revenue.
Detects changes and triggers re-engagement flows.
Percentage of customers who keep buying.
Measures returning customers across periods.
One of the strongest indicators of long-term health.
Improves retention by increasing engagement and participation.
Percentage of customers who make more than one purchase.
Repeat buyers ÷ total buyers.
Shows how well first-time buyers convert.
Boosted through personalized engagement and identity-based targeting.
Likelihood a first-time buyer becomes a repeat customer.
Tracks second purchase rates within a period.
One of the most powerful retention levers.
Engagement Flow and Alfred AI target new buyers with perfect timing.
Total value a customer generates over time.
Combines repeat rate, frequency, and AOV.
High-LTV customers fuel sustainable growth.
Increases LTV through retention, engagement, and higher AOV.
Measures LTV compared to acquisition cost.
LTV ÷ CAC.
Shows how efficiently a brand grows.
Boosts CLTV through retention and participation.
Percentage of customers who stop buying.
Defined by purchase inactivity or engagement drop.
Churn kills profitability.
Predicts churn early and triggers winbacks.
Percentage of revenue retained from existing customers.
Excludes upsells and expansions.
Shows durability of core customers.
Improves GRR by strengthening retention.
Revenue retention including upsells.
Includes expansion + contraction + churn.
High NRR means compounding growth.
Improves via higher repeat and increased AOV.
The process of pulling back slipping customers.
Targets dormant or at-risk customers.
Winbacks restore lost revenue.
Automates winbacks through Engagement Flow triggers.
Customers showing early churn signals.
Based on recency, frequency, engagement, lag.
Early intervention saves revenue.
Flags at-risk users and triggers personalized plays.
Percentage of customers who have fully lapsed.
Defined by exceeding expected purchase cycles.
Dormant customers often churn permanently.
Targets reactivation through community and personalized content.
Average number of purchases per customer.
Orders ÷ customers.
Higher frequency → higher LTV.
Boosted via personalization and community-driven discovery.
Days between consecutive orders.
Tracks individual and average repeat intervals.
Shorter cycles indicate healthier retention.
Optimizes engagement timing to shorten cycles.
Composite score predicting value and churn risk.
Combines frequency, recency, engagement, UGC, community.
Helps prioritize retention efforts.
Generates Health Scores within Alfred for segmentation and automation.
Percentage of customers who create content.
Tracks reviews, photos, videos, stories.
More UGC → more trust → more retention.
Makes UGC collection easy and integrated.
Share of audience actively contributing content.
Measures posting, replying, voting, challenges.
Participation = deeper loyalty.
Prompts UGC through flows and incentives.
Content fuels sales → sales fuel more content.
UGC → purchase → more UGC.
Compounding growth without ads.
Links UGC to products and revenue.
Revenue influenced by brand-owned community interactions.
Customers discover products inside conversations.
Community increases trust and purchase intent.
Turns community into an acquisition + retention channel.
System that turns creators into ongoing revenue drivers.
Identifies creators and measures sales impact.
Creators outperform ads in trust.
Activates customers as micro-creators.
Customers rely on other customers for buying decisions.
Uses reviews, UGC, community posts.
Peer proof is the highest-trust signal.
Surfaces peer validation across channels.
Return on small, engaged creators.
Compares influencer cost vs revenue impact.
Micro-creators drive strong conversion.
Identifies and measures customer-influencers.
Incremental impact from tiny creators with tight audiences.
Measures uplift from their content.
Nano-creators have outsized influence.
Activates loyal customers with nano influence.
Total public customer validation.
Aggregates reviews, UGC, ratings, posts.
Higher volume → higher trust → higher conversion.
Collects, organizes, and activates social proof.
Measure of community engagement intensity.
Tracks posts, replies, likes, events.
Higher participation → higher retention.
Rewards and recognizes active members.
How often customers advocate for the brand.
Counts referrals, UGC posts, shoutouts.
High frequency indicates deep loyalty.
Identifies top advocates and activates them.
Speed at which referrals are generated.
Tracks referral volume over time.
High velocity lowers CAC.
Automates and accelerates referral prompts.
Segments based on future likelihood, not past behavior.
Predictive models group customers by intent signals.
Enables proactive engagement.
Alfred generates predictive segments for targeting.
Prediction of likelihood to purchase soon.
Uses behavior, browsing, social signals.
Helps target the highest-intent customers.
Triggers personalized conversion flows.
Prediction of likelihood to stop engaging.
Looks at declining signals and recency.
Early churn detection = revenue saved.
Triggers winbacks before customers lapse.
Group of customers who behave similarly.
Segmentation based on cadence, channel, or behavior.
Shows which behaviors lead to loyalty.
Compares cohorts to identify winning journeys.
Data customers willingly share.
Collected via forms, quizzes, interactions.
Accurate, privacy-safe personalization.
Collected across flows and community.
Data collected directly from customer interactions.
Purchase history, engagement, site behavior.
Core data source in a cookieless world.
Unified with social + community data.
Linking multiple identifiers to one customer.
Combines email, device, social, purchase data.
Enables true personalization and attribution.
Resolves identities across platforms.
A map of customer relationships, behaviors, and influences.
Connects customers to channels, products, and each other.
Reveals what drives value and loyalty.
Powers segmentation, recommendations, and creator programs.
AI suggestion for the most impactful customer action.
Evaluates multiple engagement options.
Reduces irrelevant messages and boosts conversions.
Guides Engagement Flow decisions.
Automated engagement based on customer actions.
Triggers workflows off purchases, UGC, or community activity.
Timely, contextual engagement performs better.
Starts flows when high-value events occur.
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