13 AI workflows under the "Customer Analytics and Segmentation" topic. Each includes a copyable prompt and FAQs. Paste them into your preferred AI assistant or Spark agent and run it.
Order volume is rising, but the merchant cannot say whether that came from new buyers or existing ones ordering more. In this case, the immediate task is to analyze customer growth and purchase distribution. The workflow should produce a customer segment table and interpretation guide, reconciled against the merchant's purchase and refund records.
Every month someone asks how many customers were won and lost, and the answer depends on who prepares it. In this case, the immediate task is to report customer acquisition and growth over time. The workflow should produce a customer segment table and interpretation guide, with period boundaries drawn from the merchant's first-purchase dates.
A dashboard reports one blended figure even though customers discovering the catalogue and customers ordering again do not behave alike. In this case, the immediate task is to compare new and returning customer behavior. The workflow should produce a customer segment table and interpretation guide comparing both groups on identical measures and dates.
Retention looks poor simply because newly acquired customers are measured alongside ones who have had months to reorder. In this case, the immediate task is to analyze customer retention using comparable observation windows. The workflow should produce a repeat-purchase cohort analysis, built from the merchant's own order history and first-purchase dates.
The team wants segments it can act on, yet ranking everyone on revenue alone puts recent one-time buyers and quiet long-standing accounts together. In this case, the immediate task is to segment customers by recency frequency and monetary value. The workflow should produce a customer segment table and interpretation guide scored from the merchant's records.
Segment definitions exist as loose descriptions, so a customer can land in two groups or in none. In this case, the immediate task is to create practical customer segments from purchase history. The workflow should produce a customer segment table and interpretation guide, with membership rules testable against the merchant's identifiers and refund rules.
The merchant wants to look after the best customers but has no agreed measure of value, so refunds get mixed in. In this case, the immediate task is to identify high-value customers using a stated value measure. The workflow should produce a customer segment table and interpretation guide, ranking customers by that measure.
The merchant suspects customers buy in predictable combinations and at predictable times, but has never checked the order data. In this case, the immediate task is to identify patterns in customer purchase behavior. The workflow should produce a customer segment table and interpretation guide, keeping only patterns the merchant's order history repeats.
Acquisition reporting counts everyone appearing in the current period as new, including people who ordered long before the window was set. In this case, the immediate task is to define a segment for newly acquired customers. The workflow should produce a customer segment table and interpretation guide built on the merchant's own first-purchase records.
Guest checkout and shared accounts let one buyer appear under several records, so repeat counts move with whoever matched the export. In this case, the immediate task is to identify returning customers using purchase history. The workflow should produce a customer segment table and interpretation guide derived from the merchant's own identifiers and dates.
A loyalty tier is about to launch, but the proposal describes desirable customers in adjectives while the benefit budget only stretches so far. In this case, the immediate task is to define a VIP segment from explicit value criteria. The workflow should produce a customer segment table and interpretation guide showing who clears the agreed threshold.
A campaign is planned for the most frequent buyers, but the current list was built from basket size rather than order count. In this case, the immediate task is to identify customers with frequent purchases. The workflow should produce a customer segment table and interpretation guide, counting separate orders inside one window in the merchant's own records.
A store mixes consumables replenished on a schedule with durable goods bought rarely, so one threshold misreads the base. In this case, the immediate task is to identify inactive customers relative to normal buying cycles. The workflow should produce a customer segment table and interpretation guide keyed to each last-order date and its product cycle.
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