{"id":28124,"date":"2024-02-26T07:43:15","date_gmt":"2024-02-26T15:43:15","guid":{"rendered":"https:\/\/www.intentwise.com\/blog\/?p=28124"},"modified":"2024-02-26T07:43:17","modified_gmt":"2024-02-26T15:43:17","slug":"how-to-customize-an-amc-query","status":"publish","type":"post","link":"https:\/\/www.intentwise.com\/blog\/amazon-marketing-cloud\/how-to-customize-an-amc-query\/","title":{"rendered":"How to customize an Amazon Marketing Cloud query","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"\n

If you use Amazon Marketing Cloud on your own\u2014without the assistance of a platform like Intentwise Explore\u2014you\u2019re going to have to learn to customize queries. <\/p>\n\n\n\n

The quick background: When you use AMC, you are asking the platform your pressing business questions\u2014such as, \u201cshow me a list of shoppers who added to cart but did not purchase.\u201d Those questions are posed in the form of queries written in SQL. <\/p>\n\n\n\n

To get you started, Amazon gives you a library of several dozen instructional queries and audiences. But these queries have a number of important limitations<\/a>. Inevitably, you\u2019re going to want to modify those queries to meet your own specifications. <\/p>\n\n\n\n

But how do you actually modify an instructional query? In theory, it\u2019s just some code tweaks\u2014but the more complex you get, the more difficult it becomes. <\/p>\n\n\n\n

Our team customizes<\/a> queries for clients every single day, so we know this process inside and out. And we know it can sometimes feel a bit opaque how queries actually work. <\/p>\n\n\n\n

We wanted to pull back the curtain for a second, and show you what\u2019s actually happening when you modify an instructional query or audience. <\/p>\n\n\n\n

What do instructional queries actually say?<\/strong><\/h3>\n\n\n\n

To start, we\u2019re going to show you a very simple example\u2014the instructional audience for shoppers who added a product to cart but didn\u2019t purchase. This is a high-utility audience that you can\u2019t create in DSP<\/a>. <\/p>\n\n\n\n

We\u2019re choosing this example because it is one of the simplest instructional audiences available. Keep in mind that modifying pretty much any other instructional query or audience will be more complicated than this. <\/p>\n\n\n\n

In SQL code, the \u201cshoppers who added to cart but didn\u2019t purchase\u201d audience looks like this: <\/p>\n\n\n\n

\"SQL<\/figure>\n\n\n\n

What is all of this SQL actually saying? It helps to think of AMC queries as a series of tables, where each table contains signals from a set of shopper actions. AMC works by joining together those parts of the table that fit your specifications, and then creating a new output based on your specification.<\/p>\n\n\n\n

In this case, AMC\u2019s instructional query references 2 tables: <\/p>\n\n\n\n

    \n
  1. A table of shoppers sorted by their most recent purchase (called \u201cmax purchase event\u201d)<\/li>\n\n\n\n
  2. A table of shoppers sorted by their most recent add to cart event (called \u201cmax ATC event\u201d) <\/li>\n<\/ol>\n\n\n\n

    <\/p>\n\n\n\n

    Those tables might look a bit like this:<\/p>\n\n\n\n

    \"Table<\/figure>\n\n\n\n

    Once those tables are created, the work of narrowing down your audience comes in. The instructional query instructs AMC to create a pool of shoppers who added to cart but did not<\/em> purchase. <\/p>\n\n\n\n

    That means it wants to find the set of shoppers whose most recent add to cart date was after<\/em> their most recent purchase date. <\/p>\n\n\n\n

    We\u2019re simplifying a bit, but in a sense what AMC does is take those tables and highlight the shoppers who added a product to cart that they have yet to purchase. Using the logic of SQL, you might visualize the process along these lines:  <\/p>\n\n\n\n

    \"table<\/figure>\n\n\n\n

    As you can see, user 1 and user 4 each had a last purchase date more recent than their last add-to-cart date. That means they are disqualified from our final audience. <\/p>\n\n\n\n

    The three other users\u2014highlighted in green\u2014had an add-to-cart date more recent than their last purchase date, meaning they added a product to cart that they have yet to purchase.<\/p>\n\n\n\n

    Those three users will make it into your final audience, producing a table of user 2, user 3, and user 5. <\/p>\n\n\n\n

    But how do I modify the instructional query? <\/strong><\/h3>\n\n\n\n

    What we just outlined is a very simple, boilerplate instructional query. What if you want to make it more specific? For instance, what if you want to track shoppers who added specific ASINs<\/em> to their cart, but did not purchase them? <\/p>\n\n\n\n

    In this case, it\u2019s a relatively simple change, amounting to adding another column to your existing tables. <\/p>\n\n\n\n

    In the SQL code, what you want to do is take those two groups\u2014those add-to-cart and purchase tables\u2014and then add \u201cASIN\u201d as a filter for both. <\/p>\n\n\n\n

    The new column specifies which ASIN each customer bought or added to their cart. Like this: <\/p>\n\n\n\n

    \"table<\/figure>\n\n\n\n

    Then, through SQL code, you tell AMC to narrow down the tables. You add just a few extra lines of code, such as in the following example. The SQL we added is in bold: <\/p>\n\n\n\n

    \"AMC<\/figure>\n\n\n\n

    As before, you first want to find the users who added a product to cart more recently than they made a purchase. Once again, you\u2019ll be left with users 2, 3, and 5. <\/p>\n\n\n\n

    But now, with our modifications to the instructional query, you only are interested in users who added specific ASINs to cart but did not purchase. We used the tracked_asin <\/strong>column in order to focus on those specific ASINs.<\/p>\n\n\n\n

    For example, let\u2019s say you\u2019re only interested in ASIN 3. That means you only want to see an audience of shoppers who added ASIN 3 to their cart but did not purchase it.<\/p>\n\n\n\n

    Your final table will look like this, with only user 5 in your audience: <\/p>\n\n\n\n

    \"output<\/figure>\n\n\n\n

    How hard is modifying queries, really? <\/strong><\/h3>\n\n\n\n

    Even if you don\u2019t have detailed knowledge of SQL, you might be able to get by with making a simple modification to an easy instructional query like this one. But remember, editing the code is only one part of the task of customizing a query or audience.<\/p>\n\n\n\n

    You also have to: <\/p>\n\n\n\n

      \n
    1. Validate the query<\/strong> to be sure the data is accurate. <\/li>\n\n\n\n
    2. Ensure your audience is larger <\/strong>than Amazon\u2019s 2k shopper minimum threshold. Remember, as you add more constraints, you\u2019re creating a higher probability that the audience will fall below that 2k minimum. <\/li>\n\n\n\n
    3. Run a cost-benefit analysis<\/strong> of whether the audience is worth all the trouble in the first place. Even if it clears the 2k threshold, is it a big enough or valuable enough audience to make back the money you spend creating it?\u00a0<\/li>\n\n\n\n
    4. Examine all of the available data sets in AMC<\/strong> to verify you are using the best possible data sets, along with the proper columns in those data sets. It therefore helps to have someone on your team with domain knowledge of AMC. In this case, the proper field to customize the audience was the tracked_asin column. <\/li>\n<\/ol>\n\n\n\n

      <\/p>\n\n\n\n

      Only after taking those steps will you know if your custom query is good to go.<\/p>\n\n\n\n

      What about more complicated modifications? <\/strong><\/h3>\n\n\n\n

      Oftentimes, you won\u2019t just want to add one tiny ASIN filter to AMC\u2019s instructional audiences. You may want to do something a bit more dramatic. <\/p>\n\n\n\n

      Say you want to create an audience of shoppers who were exposed to Sponsored Products and Sponsored Brands ads\u2014but not Sponsored Display ads\u2014and did not make a purchase. <\/p>\n\n\n\n

      You\u2019ll want to take the instructional query audience, \u201cAudience Exposed to Amazon Streaming TV Campaign but not Display Campaign<\/strong>,\u201d and then modify it to replace \u201cExposed to Amazon Streaming TV Campaign\u201d with \u201cExposed to SP and SB ads.\u201d Then, you also need to add the requirement that these shoppers haven\u2019t purchased. <\/p>\n\n\n\n

      What this audience calls for is an entirely new series of tables. First, build two common table expressions\u2014one for people exposed to Sponsored Products ads and another for people exposed to Sponsored Brands ads. Join them together, so you have an audience of people exposed to both SP and<\/em> SB. <\/p>\n\n\n\n

      Then, create a table of people who saw your Sponsored Display ads. Negate out all of the shoppers from your SP+SB table who saw the SD ads. Then, you\u2019ll have a new table that is essentially (SP+SB) -SD. <\/p>\n\n\n\n

      Finally, you want to create a table of shoppers who purchased from you. Again, negate out those purchasers from your (SP+SB) -SD table. At long last, you\u2019ll have your audience of people exposed to SB+SD, but not SD, who didn\u2019t purchase.<\/p>\n\n\n\n

      This customization takes a lot of work. The original instructional query was about 20 lines of SQL code. Now, the final custom query will probably be more than double that\u2014about 45 lines of code. <\/p>\n\n\n\n

      This modification is intensive enough that you will basically be writing a whole new query from scratch. It\u2019s a lot of work, and it requires expertise. If you want to learn how to do more modifications like this, we have a dedicated SQL training module<\/a> that is widely used across the industry. <\/p>\n\n\n\n

      Or, of course, you can just use Intentwise Explore to pull insights from Amazon Marketing Cloud. When you use Intentwise Explore<\/a>, get access to a vast library of fully customizable queries and audiences, which you can schedule to run in the background. <\/p>\n\n\n\n

      Bonus: Get your ultimate AMC guide<\/strong><\/h3>\n\n\n\n

      We just published our newest whitepaper, \u201cThe Amazon Marketing Cloud Strategy Guide<\/a>,\u201d which is the most robust guide to AMC available online. <\/p>\n\n\n\n

      Across 16 pages, we break down the best practices for AMC, the key events you can track, the differences between free and paid datasets, and the benefit of uploading your 1P data to AMC. <\/p>\n\n\n\n

      If you\u2019re still trying to make sense of AMC, this is the document for you. Share it with your team, share it with your clients, and level up the sophistication of your internal discussions about how to leverage AMC. <\/p>\n\n\n\n

      Download it now for free.<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

      If you use Amazon Marketing Cloud on your own\u2014without the assistance of a platform like Intentwise Explore\u2014you\u2019re going to have to learn to customize queries.  The quick background: When you use AMC, you are asking the platform your pressing business questions\u2014such as, \u201cshow me a list of shoppers who added to cart but did not […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":22,"featured_media":28132,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","cybocfi_hide_featured_image":"","footnotes":""},"categories":[135],"tags":[97,96,155],"class_list":["post-28124","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-amazon-marketing-cloud","tag-amazon-marketing-cloud","tag-mobile","tag-newsletter"],"yoast_head":"\nHow to customize an Amazon Marketing Cloud query<\/title>\n<meta name=\"description\" content=\"Writing custom queries is the key to using Amazon Marketing Cloud effectively. 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