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Problem

Most people buy the same grocery and household products week after week, but every trip to the store starts from scratch: trying to remember what’s running low, writing items down in a notes app, or showing up and improvising. With access to SKU-level transaction data, an app can identify exactly which products a user buys repeatedly and turn that into something useful.

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Solution

Use SKU-level transaction data from Knot’s Sync Transactions endpoint to identify products a user buys repeatedly at linked grocery and retail accounts, then generate a personalized shopping list pre-populated with those items.
Banking app screen showing a personalized shopping list with frequently purchased grocery items, checkboxes, and an estimated total
This turns purchase history into a practical, recurring utility that gives users a reason to open the issuer’s app before every shopping trip.

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Flow

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Implementation

1

Sync transactions and build a purchase history

When a user switches their card at a merchant or they simply link their merchant account w/o switching their card, Knot begins collecting transaction data for that account. Listen for the NEW_TRANSACTIONS_AVAILABLE webhook event, then call Sync Transactions to sync SKU-level transactions on a daily basis. For each transaction, iterate over the products array and update a per-user, per-merchant purchase index.Key fields
FieldPurpose
products[].external_idUnique product identifier for deduplication across orders.
products[].nameProduct display name for the shopping list.
products[].price.unit_priceMost recent unit price, used to estimate the list total.
products[].image_urlProduct image for the list UI.
products[].urlLink to the product page for the list UI.
merchant.idIdentifies the retailer. Scope the list per merchant.
merchant.nameRetailer display name.
datetimePurchase timestamp for recency scoring.
external_user_idIdentifies the user in your system.
order_statusFilter to completed orders only.
Only include transactions with order_status of COMPLETED, DELIVERED, PICKED_UP, SHIPPED, BILLED, or ORDERED. Filter out CANCELLED, REFUNDED, and FAILED.
FOR each transaction in sync response:
    FOR each product in transaction.products:
        key = (external_user_id, merchant.id, product.external_id)
        purchase_index[key].count += 1
        purchase_index[key].last_purchased = transaction.datetime
        purchase_index[key].name = product.name
        purchase_index[key].last_price = product.price.unit_price
        purchase_index[key].image_url = product.image_url
        purchase_index[key].url = product.url
2

Generate the shopping list

When a user’s purchase index reaches the minimum threshold, compile their shopping list. Run this on initial sync (to catch historical data) and refresh after each new transaction batch.
candidates = products in purchase_index[user][merchant]
    WHERE count >= MIN_PURCHASE_COUNT
    AND last_purchased >= (today - PURCHASE_WINDOW_DAYS)

ranked = sort candidates by count descending, then last_purchased descending

shopping_list = ranked[:MAX_LIST_SIZE]
estimated_total = sum(p.last_price for p in shopping_list)
Configuration decisions for your team:
ParameterSuggested DefaultConsiderations
MIN_PURCHASE_COUNT3 purchases2 purchases may include one-time buys. 3+ is a reliable signal of a regular item.
PURCHASE_WINDOW_DAYS30 daysOnly count purchases within this window. Avoids surfacing products bought 3 times over a year that are not part of a regular routine.
MAX_LIST_SIZE30 itemsCovers a typical weekly grocery run without feeling overwhelming.
List refresh frequencyAfter each new transaction syncKeeps the list current as buying habits change.
3

Surface shopping list in the app

Send a push notification when the list is first generated or materially updated, and surface it as a persistent in-app feature.
Banking app screen showing a personalized shopping list with frequently purchased grocery items, checkboxes, and an estimated total
Users should be able to check off items as they shop, add one-off items manually, and skip items they don’t need that week.

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Expansion Path

  • Predictive reorder timing: For each recurring item, track the average interval between purchases (e.g., milk every 6 days). Surface items on the list when they are likely running low, rather than showing all items at once every week.
  • Cross-merchant list: Combine repeat-purchase data across multiple linked merchants into one unified list. For items that appear at more than one merchant, surface the lower price as a recommendation.
  • Store-brand swap suggestions: For each name-brand item on the list, check whether a comparable store-brand product is available at a lower price and offer an inline swap. This reuses the same purchase history data and adds a savings angle without requiring a separate flow.