Customer lifetime value software with LTV math primitives
Composable LTV math primitives on the canonical customer graph — historical, predictive, cohort, per-channel, per-location, per-brand LTV as a runtime signal.
The problem
You have one number for CLV that finance computed last quarter. It averages every customer together. You do not know LTV per location, per channel, per cohort, per brand. Your save-flow agent treats a high-LTV customer the same as a low-LTV one because nothing exposes LTV at runtime.
Klaviyo CLV computes inside Klaviyo. Your GBP agent and PDP agent cannot read it. Lifetimely (Shopify $99-$500/month), Tresl CLV ($200-$2,000+/month), Theta CLV ($30,000-$200,000+/year enterprise), and Daasity are verticalized for ecommerce + DTC — you have 200 retail locations plus a call center plus in-store and Lifetimely sees only the website. Optimove ($50,000+/year), BlueShift ($30,000-$150,000/year), Insider, and Iterable Insights compute LTV but tie to single-tenant graphs. Salesforce Customer 360 ($30,000-$300,000+/year), Adobe Real-Time CDP ($30,000-$500,000+/year), Oracle Unity, and Treasure Data have LTV but at enterprise pricing with 6-12 month implementation. Predictive-analytics suites (SAS Customer Intelligence $50,000-$300,000+/year, IBM SPSS, Pega Customer Decision Hub $50,000-$500,000+/year, DataRobot $30,000-$300,000+/year) handle predictive-LTV but require ML / statistics expertise. DIY costs $80,000-$150,000/year per data-analyst FTE plus ML expertise for Buy-Til-You-Die / Pareto/NBD / BG/NBD models.
The gap is LTV math as composable primitives operating on the canonical multi-location-operator customer graph, exposed as a runtime signal every AI agent consumes.
What success looks like
Historical-LTV (bookings to date), predictive-LTV (Buy-Til-You-Die / Pareto/NBD / BG/NBD models), cohort-LTV, per-channel-LTV, per-location-LTV, per-brand-LTV, and per-vertical-LTV all compose into operator-defined custom metrics. Operations run on the canonical customer graph from identity-resolution-deterministic-probabilistic, so cross-device, cross-channel, cross-location customers count once.
Multi-brand portfolios get per-brand LTV math; multi-vertical operators get per-vertical LTV; multi-location operators get per-location LTV with cross-location roll-up. Predictive-LTV models pre-built; operator selects model appropriate to their business cycle. Calibration auto-tunes against historical data.
Every AI content-producing and decisioning agent reads LTV signals at runtime — not nightly batch. LTV-band transitions fire customer-change-event-emission events to downstream agents. Save-flow-propensity-scoring, churn-prediction-per-subscriber, predictive-tier-transition, loyalty member-journey-decisioning, lifecycle-flow-architecture, and per-location-list-segmentation all consume LTV signals at runtime. Klaviyo CLV, Lifetimely, and Salesforce Customer 360 remain useful for their respective domains; the operational LTV math layer lives in this skill.
How most operators solve this today
Six tiers of incumbent tools — none provide composable LTV math primitives on the canonical multi-location-operator customer graph as a runtime signal.
Specialized CLV/LTV tools (Lifetimely, Tresl CLV, Theta CLV, Daasity)
$99-$200,000+/year
Verticalized for ecommerce + DTC. Light-touch on multi-location operator graphs (in-store + call + GBP + retail not covered).
CDPs with LTV modules (Klaviyo CLV, Bloomreach Engagement, Segment Personas, Tealium LTV)
Bundled with CDP license
Compute LTV inside the platform; not exported as runtime signal feeding every AI content + decisioning agent.
Customer-analytics platforms (Optimove, BlueShift, Insider, Iterable Insights)
$30,000-$150,000+/year
LTV cohort modeling built-in but tied to single-tenant graph. Cross-location, cross-brand math manual.
Enterprise customer 360 (Salesforce Customer 360, Adobe Real-Time CDP, Oracle Unity, Treasure Data)
$30,000-$500,000+/year
LTV included; deeply tied to their platform. Over-built for multi-location-operator scale.
Predictive-analytics suites (SAS Customer Intelligence, IBM SPSS, Pega Customer Decision Hub, DataRobot)
$30,000-$500,000+/year
Generic predictive modeling; LTV is one use case among many. Requires ML / statistics expertise.
DIY (custom SQL + pandas + R + Excel)
$80,000-$150,000/year per data-analyst FTE
Historical LTV easy; predictive-LTV (Buy-Til-You-Die / Pareto/NBD / BG/NBD models) requires ML + statistics expertise.
What changes when this is an agent skill
The Completions ltv-math-primitives skill exposes LTV math as composable primitive operations — historical-LTV, predictive-LTV (Buy-Til-You-Die / Pareto/NBD / BG/NBD), cohort-LTV, per-channel-LTV, per-location-LTV, per-brand-LTV, per-vertical-LTV. Operations run on the canonical customer graph from identity-resolution-deterministic-probabilistic (loop 030) and behavioral-signal-ingestion (loop 025).
Multi-brand portfolios get per-brand LTV; multi-vertical operators get per-vertical LTV; multi-location operators get per-location LTV with cross-location roll-up. Predictive-LTV models pre-built; operator selects model appropriate to business cycle. Calibration auto-tunes against historical data.
Every AI content-producing and decisioning agent reads LTV signals at runtime. LTV-band transitions fire customer-change-event-emission (loop 029) events to downstream consumers — save-flow-propensity-scoring, churn-prediction-per-subscriber, predictive-tier-transition, loyalty member-journey-decisioning, lifecycle-flow-architecture, per-location-list-segmentation.
Klaviyo CLV, Lifetimely, Salesforce Customer 360 remain useful for their respective domains; this skill provides composable LTV math primitives every Completions agent consumes at runtime.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Customer Data Graph Foundation Agent
Resolves DTC subscriber identity, computes LTV math, and emits the canonical customer-data-graph downstream subscription agents consume.
Early-adopter
$2,500–$4,500/mo
FAQ
- What is customer lifetime value software?
- Software that computes customer lifetime value (CLV / LTV) — total revenue a customer generates over the customer-relationship lifetime. This skill provides LTV math as a primitive operation every AI content and decisioning agent consumes at runtime.
- How is this different from Lifetimely (Shopify) or Tresl CLV?
- Specialized CLV/LTV tools are verticalized for ecommerce + DTC. This skill operates on multi-location-operator canonical customer graphs (web + mobile + in-store + call + email + SMS) with per-location + per-channel + per-brand math.
- How is this different from Klaviyo CLV or Bloomreach Engagement?
- CDP-bundled LTV computes inside the platform; not exported as a runtime signal feeding every AI content + decisioning agent. This skill exposes LTV as a runtime signal cross-agent.
- How is this different from Salesforce Customer 360 or Adobe Real-Time CDP?
- Enterprise customer 360 platforms include LTV at $30,000-$500,000+/year with 6-12 month implementation. This skill is purpose-built for multi-location-operator scale.
- What LTV math primitives does the skill compute?
- Historical-LTV (bookings to date), predictive-LTV (Buy-Til-You-Die / Pareto/NBD / BG/NBD models), cohort-LTV, per-channel-LTV, per-location-LTV, per-brand-LTV, per-vertical-LTV. Operator composes them into custom metrics.
- What predictive-LTV models are supported?
- Buy-Til-You-Die, Pareto/NBD (Schmittlein-Morrison-Colombo), BG/NBD (Beta-Geometric / Negative Binomial), MBG/NBD. Calibration auto-tunes against historical data.
- How does this compose with identity-resolution and behavioral-signal-ingestion?
- Identity-resolution resolves cross-device customer identity. Behavioral-signal-ingestion feeds the behavioral signal stream. This skill consumes both and computes LTV math primitives.
- What downstream agents consume LTV signals?
- Save-flow-propensity-scoring, churn-prediction-per-subscriber, predictive-tier-transition, loyalty member-journey-decisioning, lifecycle-flow-architecture, per-location-list-segmentation, paid creative agent, PDP agent, and any decisioning agent needing LTV signal.