Has Purchased

Has Purchased returns a list of orders that meets the threshold that you define – for example, exactly 4, less than 5, more than 2, or between 2 and 10 – and occurred during your chosen date range. For example, return all customers who have purchased 3 (or more) times in the previous 2 years:

Find which customers have interacted with products, channels, or brands during the specified date range.

After you specify a value and date range you may apply filters to associate customers who have purchased with specific products, brands, channels, and stores. For example, return all customers who have purchased wool or cotton socks from your website.

How this attribute works

Has Purchased represents a common approach people use when they build segments: start with all orders, and then associate that list of orders to the products and brands your customers purchased.

Has Purchased is a compound attribute, which means that it’s built from a combination of attributes that already exist in your data, and then appears as a single attribute that you can choose from the Segment Editor.

With this attribute, you can focus less on SQL and more on finding answers to your marketing, which simplifies the number of steps required to associate a list of customers to your products, stores, channels, and brands.

How does the SQL for Has Purchased work?

Has Purchased is built from standard columns that are output by Amperity. The following example returns a list of customers who have purchased 3 (or more) times in the previous 2 years:

Find which customers have interacted with products, channels, or brands during the specified date range.

The SQL for Has Purchased works like this. It uses order ID from the Unified_Itemized_Transactions table as its starting point:

SELECT
  amperity_id
  ,COUNT(DISTINCT order_id) AS purchase_count
FROM Unified_Itemized_Transactions

and finds all of the distinct orders that were made by all customers – the “purchase count” – and then returns all orders that match the date range:

WHERE order_datetime < DATE_TRUNC('day', CURRENT_TIMESTAMP - interval '2' year)

Returns and cancellations are filtered out automatically, like this:

AND (is_return IS NULL OR (NOT is_return))
AND (is_cancellation IS NULL OR (NOT is_cancellation))

From that list of customers who made a purchase within that date range, Has Purchased uses another SELECT statement to return only customers whose purchase count meets the threshold that you defined. For example, more than 2 purchases:

SELECT
  *
FROM total_distinct_orders
WHERE purchase_count > 2

This creates a list of customers who made at least 3 purchases during a specific time window. From here you can apply additional filters for your products, stores, channels, and brands.

The SQL for Has Purchased is more complex than what is described in the previous section. This is due to the way this attribute returns only a list of Amperity IDs, uses a series of common table expressions (CTEs), and takes advantage of workflows that Amperity does behind the scenes to pre-filter the product, purchase, and store attributes.

You can view the full SQL for Has Purchased from the Segment Editor. Start a new segment and add only this attribute (along with any required conditions and filter attributes), and then click the View SQL link at the top of the page.

Add to segments

To find all customers who made their first order within a specified date range, start with the Has Purchased attribute located under Transactional Behaviors:

Use the has purchased attribute to find which customers have interacted with products, channels, or brands during the specified date range.

After the attribute appears in your segment, set the purchase count and choose a date range:

Find which customers have interacted with products, channels, or brands during the specified date range.

After you specify a purchase count and date range you may apply filters to associate these customers to specific products, brands, channels, and stores.

About relative dates

Use relative dates to define ranges of dates and times that are relative to the date and time at which a segment is run. For example, a rolling window that returns results starting from 90 days ago would use a relative date of “today - 90 days”.

A relative date is specified using the following pattern:

<today or now> <+/-> <#> <days, weeks, months, or years>

The relative date is determined at the time a segment is run, where “today” is the day on which the segment is run, and “now” is the date and time on which the segment is run.

For example:

  • today - 2 weeks

  • today - 3 months

  • today - 1 year

-or-

  • now - 2 weeks

  • now - 6 months

  • now - 24 hours

Use non-relative dates to pick specific dates and specific ranges of dates and times.

Available conditions

The following table lists the conditions that are available to this attribute.

Note

Recommended conditions for this attribute are identified with “ Recommended” and conditions with more limited use cases are identified with “ Not recommended”.

Condition

Description

between

between returns a list of customers whose order threshold is in-between two specified values.

exactly

exactly returns a list of customers whose order threshold exactly matches specified value.

less than

less than returns a list of customers whose order threshold is less than the specified value.

more than

more than returns a list of customers whose order threshold is more than the specified value.

Filter attributes

A filter attribute is a standard column that is output by Amperity. When a filter attribute is associated with a compound attribute, you may use each filter attribute to filter the results of the compound attribute on specific items in your product catalog, brands, channels, and stores.

The following filter attributes may be made available to this attribute, when the underlying data in your tenant is available from the Unified_Itemized_Transactions table:

  • Product category

  • Product description

  • Product subcategory

  • Product gender

  • Purchase brand

  • Purchase channel

  • Store ID

Important

The following table lists the semantic tag that generates the column that is the pre-requisite for each attribute filter. If the column is missing from the Unified_Itemized_Transactions table (or if it has a non-standard name) it will not be available to the attribute filter group:

Semantic tag

Column name

Filter name

pc/product-category

product_category

Product category

pc/product-description

product_description

Product description

pc/product-subcategory

product_subcategory

Product subcategory

pc/product-gender

product_gender

Product gender

txn-item/purchase-brand

purchase_brand

Purchase brand

txn-item/purchase-channel

purchase_channel

Purchase channel

txn-item/store-id

store_id

Store ID