Use case

Replenishment reminders
before they run out, not after

Consumables sell again on a rhythm. Purands learns each customer's rhythm per product and reminds them a few days before they run out.

The problem

A customer finishes a jar or a bag and buys whatever is nearest. The reorder is lost to convenience, not preference.

A monthly newsletter cannot catch that moment. Customers run out on different days, and products last different lengths of time.

What triggers it, and what it reads

The trigger is a predicted run-out date, modelled per customer and per product rather than one blanket interval.

  • How long each product has lasted this customer before
  • Order frequency and quantity per SKU
  • Life stage and context, where the category has one
  • Purchase intent and churn risk, scored daily
  • Stock and catalogue state, so nothing unavailable is suggested

Brands running this skill report repurchase moving from 12% to 35%, and per-SKU reminders lifting click-through from 6% to 29%.

What gets sent

Examples only, written for this page. Real messages are generated per customer from their own profile and your catalogue. The WhatsApp template checker includes a checked replenishment template.

  • WhatsApp: Hi Ravi, Buddy's Royal Canin Medium Adult bag is almost finished. Based on his feeding schedule you will run out in 5 days.
  • Email: Names the product, and offers a one-tap reorder of the exact size bought last time.
  • SMS: Running low on your Niacinamide 10% serum? Reorder in one tap: [link]
  • Push: Timed to the predicted run-out date and deep-linked to that product.
  • Web assistant: Recognises a returning customer and offers the repeat order before they search.

How the agent picks the moment and the channel

Consumption modelling learns how long each product lasts each customer, so heavy and light users are reminded on different days.

The reminder lands before the run-out, not on it. A skincare customer on a 60-day serum is nudged around day 50.

Channel is chosen from where that customer replies, and the send lands in their peak engagement window.

If the customer has already bought again, or heard from you too often this week, the reminder is not sent.

Setting it up

Connect the store and let order history sync. The model learns from repeat purchases, so accuracy improves over the first cycles.

Per-SKU reminders need a clean catalogue: sizes and variants as separate products, so a 3kg bag is not modelled as a 1kg one.

Switching from LimeChat? See Purands vs LimeChat.

The other retention jobs

All six run on one engine: Agentic Skills, reading Profiling.

FAQ

Replenishment reminders

It models consumption per customer and per SKU from that customer's own order history, learning how long each product has actually lasted them before.
Early reminders lean on how the product behaves across customers, and tighten to the individual as that customer's own repeat purchases come in.
Yes. Profiling maps affinities, so a customer can be moved to a compatible product or the next life-stage formula instead of a straight repeat.
Whichever that customer actually reads. Channel affinity is part of the profile, and the agent picks per customer rather than sending on all of them.
See it on your data

See this running on your own data

A 30 minute demo on your catalogue and your customers, with the decisions the agent would make shown one by one.

Book a demo