Where loyalty is earned transaction
by transaction.

Retail customer bases move fast, where seasonal peaks, promotional distortion, and high volumes of one-and-done purchasers make static segmentation particularly unreliable. SIVV models behavioural trajectory across the full base continuously.

Identify accelerating customers before they plateau
The window between second purchase and embedded advocacy is short. SIVV surfaces it before it closes.

Separate promotional buyers from loyal customers
Customers who only buy on discount look like active customers. SIVV distinguishes trajectory from promotion response.

Recover high-value lapsed customers selectively
Not every lapsed customer is worth chasing. SIVV scores the archive cohort by historical value and recovery propensity.

Infrequent transactions. High lifetime value. Long gaps.

Telecommunications businesses carry millions of customers with contracted relationships, high ARPU variance, and churn that is expensive and often predictable weeks before it happens. SIVV models the signals that standard CRM tools miss.

Predict churn before contract ends
Disengagement signals appear in usage and engagement data well before a customer acts. SIVV detects the drift early enough to intervene.

Identify upgrade propensity by behavioural trajectory
Customers on an accelerating engagement trajectory are more likely to respond to upsell. SIVV surfaces them at the right moment.

Segment large bases without manual rules
Telco bases can be millions of customers. SIVV models every one of them continuously.

Infrequent transactions. High lifetime value. Long gaps.

Travel businesses face a unique modelling challenge as low transaction frequency and long natural gaps between purchases mean standard recency signals are misleading. SIVV normalises against each customer's own baseline, not industry averages.

Distinguish natural gap from real churn
A customer who books once a year looks lapsed after 13 months. SIVV knows the difference.

Identify high-LTV customers earlier in the relationship
First-booking behaviour predicts long-term value in travel more than in almost any other vertical. SIVV captures it.

Personalise reactivation by travel type and value tier
A lapsed business traveller and a lapsed leisure traveller need completely different win-back strategies.

Subscription and ticketing businesses with seasonal volatility.

Entertainment and media businesses face high engagement variance — peak events, seasonal drop-off, and subscription fatigue create a customer base that looks healthy in aggregate but contains significant at-risk cohorts underneath the surface.

Identify subscription churn before renewal
Declining engagement in the weeks before renewal is a strong churn predictor. SIVV sees the pattern and triggers intervention in time.

Separate event-driven customers from loyal base
Customers who only engage around major events look active but aren't. SIVV distinguishes genuine loyalty from occasion-driven behaviour.

Build upsell audiences from engagement trajectory
Customers deepening their engagement across content types or event categories are the most receptive to premium tier offers.

Donor relationships that need nurturing, not just messaging.

Charities operate on relationships built over time. Donors lapse quietly. The signals are subtle, but they're there. SIVV models them at the individual level so teams can intervene before the relationship is gone.

Identify lapsing donors before they stop giving
Donation frequency and amount trajectory predict lapse weeks in advance. SIVV surfaces the at-risk cohort while intervention is still cost-effective.

Segment donor base by engagement depth, not just gift size
A small but growing donor is a different relationship than a large but declining one. SIVV models both trajectories separately.

Prioritise upgrade asks by propensity and lifecycle stage
Asking at the wrong moment damages the relationship. SIVV identifies the window when the ask is most likely to land.

Distinguish natural gap from real churn
A customer who books once a year looks lapsed after 13 months. SIVV knows the difference.

Member engagement that compounds over time — when you get it right.

Wellness businesses live and die by habit formation. Members who build a routine, stay. Those who don't, churn — often after just a few weeks. SIVV tracks behavioural momentum at the individual level so you can intervene early and reinforce the habit before it breaks.

Identify members losing momentum before they cancel
Declining visit frequency, shorter sessions, and narrowing service usage all signal disengagement weeks before cancellation. SIVV catches the drift early.

Distinguish seasonal lapse from genuine churn risk
A member who doesn't visit in January is different from one who hasn't been in four months. SIVV normalises against each member's own baseline.

Surface upsell and upgrade moments from engagement trajectory
Members deepening their engagement across services are the most receptive to premium tier offers. SIVV identifies the window.