Planned module

The right next product.

Filters for the customer with a new grinder, the refill for the starter kit, the size that’s back in stock. Oftenflow picks one product per customer that fits what they already have, and adds it to the message.

Planned: 30% off for the first 100 stores. One email when your invite is ready, no newsletter.

  • Works with the email tool you have
  • Never priced per contact
  • You approve every message
Design · not built yet: Reminder for 18 House Blend regulars: add paper filters, bought by most customers who own the same dripper.

“You might also like” usually means “our bestseller”.

The same five products recommended to everyone.

Suggestions for things the customer already owns.

Recommendation blocks that need their own tool and their own budget.

Every customer has a rhythm

Inside the app

What you’ll see, step by step

  1. Step 1 / 3

    Learn what goes together

    From your own orders: which products customers buy together, corrected for how popular each one is.

  2. Step 2 / 3

    One product per customer

    Something that fits what they own, isn’t already in their cupboard and is in stock.

  3. Step 3 / 3

    In the message you already send

    Added to a reminder or welcome, not as an extra email.

Planned module

Want product recommendations in your store?

Join the waitlist and tell us which modules you need first. Founding stores help decide the order.

  • Works with Shopify Messaging, Klaviyo or Brevo
  • Never priced per contact
  • Export all your data, anytime

Planned: 30% off for the first 100 stores. One email when your invite is ready, no newsletter.

A closer look

A closer look

Liftinstead of “most popular”

Based on what people buy together

Oftenflow counts which products the same customers buy, then corrects for how common each product is. A bestseller only gets suggested when it really goes with what someone bought.

1product per message

One suggestion, in context

One product, added to a message that was going out anyway: a reminder, a welcome or a thank-you. No extra email just to push products.

0suggestions from too little data

Silent when unsure

If too few customers bought two products together, there is no suggestion at all. Guessing would only add noise.

Product recommendations

Bestsellers only when they fit

Popular products only win when they really go with what the customer bought.

Never what they have

Products the customer already bought are left out.

Stock-aware

Sold-out products and excluded collections are skipped.

Explained

“22 of 30 grinder buyers also bought filters” instead of a black box.

Example
Reminder for 18 House Blend regulars: add paper filters, bought by most customers who own the same dripper.
In practice

Filters for grinder owners

  1. Learn

    Of 30 customers who bought the grinder, 22 also bought filters.

  2. Check

    8 grinder owners never bought filters and are due for coffee.

  3. Add

    Their reminder gets one line: “Running low on filters too?”

  4. You

    You approve the reminder as usual.

  5. Learn

    Results feed back into what gets suggested next time.

Planned flow; numbers are illustrative.

Without Oftenflow / With Oftenflow

Without OftenflowWith Oftenflow
Bestsellers recommended to everyoneA product that fits what the customer bought
Suggesting what they already ownPreviously bought products left out
A separate recommendation toolInside the messages you already send
No idea why something was suggestedThe reason in plain words

What you decide

  • Products and collections that are never suggested
  • Whether suggestions are on per kind of message
  • Only in-stock products
  • Turning the module off completely
Questions

Questions

Is this available now?

No. It’s a planned module. The calculation behind it is being built as part of the decision engine.

Does it work for small stores?

It needs a minimum of shared purchases to be reliable. With too little data, it simply doesn’t suggest anything.

Does it use AI models?

The core is plain statistics on your own orders. Product descriptions may later help with new products without sales history.

Will it show recommendations on my website?

Not at first. The first plan is recommendations inside messages.

Can I pick the products myself?

Yes. You can always replace or remove a suggestion before approving.

Waitlist · in development

Get an invite when we open the doors.

Planned: 30% off for the first 100 stores. One email when your invite is ready, no newsletter.

  • Plans from €29 a month, never per contact
  • Works with the email tool you already use
  • No card, no commitment
Example from the Oftenflow app: overnight the agent read new orders, updated buying rhythms and wrote drafts.
Join the waitlist