alt text

Author: forstisabella

src/connections/storage/warehouses/redshift-tuning.md

@@ -15,7 +15,10 @@ As your data volume grows and your team writes more queries, you might be runnin
 
 To check if you're getting close to your max, run this query. It will tell you the percentage of storage used in your cluster. Segment recommends that you don't exceed 75-80% of your storage capacity. If you approach that limit, consider adding more nodes to your cluster.
 
-![](images/asset_HvZs8FpE.png)
+```json
+SELECT sum(pct_used);
+FROM svv_table_info;
+```
 
 [Learn how to resize your cluster.](http://docs.aws.amazon.com/redshift/latest/mgmt/rs-resize-tutorial.html)
 
@@ -51,7 +54,7 @@ If you have multiple ETL processes loading into your warehouse at the same time,
 
 If you're a Segment Business Tier customer, you can schedule your sync times under Warehouses Settings.
 
-![](images/asset_fRccrNNd.png)
+![Screenshot of the Warehouse Settings page, with the Sync Schedule tab selected.](images/asset_fRccrNNd.png)
 
 You also might want to take advantage of Redshift's [Workload Management](http://docs.aws.amazon.com/redshift/latest/dg/c_workload_mngmt_classification.html) that helps ensure fast-running queries won't get stuck behind long ones.
 
@@ -70,7 +73,7 @@ Segment's initial recommendation is for 2 WLM queues:
 2.  leave the default queue with a concurrency of `5`
 
 
-![](images/asset_sHNEIURK.png)
+![Screenshot of the Workload Management Configuration settings page in AWS with a named queue, Queue 1, and the default queue present.](images/asset_sHNEIURK.png)
 
 Generally, Segment is responsible for most writes in the databases Segment connects to, so having a higher concurrency allows Segment to write as fast as possible. If you're also using the same database for your own ETL process, you may want to use the same concurrency for both groups. You may even require additional queues if you have other applications writing to the database.
 

src/connections/storage/warehouses/redshift-useful-sql.md

@@ -58,7 +58,7 @@ where event = 'completed_order'
 
 That SQL query returns a table that looks like this:
 
-![](images/sql-redshift-table-1.jpg)
+![Screenshot of a SQL table, with event, event_id, user_id, sent_at, item, color, size, and payment columns.](images/sql-redshift-table-1.jpg)
 
 But why are there columns in the table that weren't a part of the Track call, like `event_id`? 
 This is because the Track method (for client-side libraries) includes additional properties of the event, like `event_id`, `sent_at`, and `user_id`!

src/engage/audiences/index.md

@@ -207,7 +207,7 @@ Before you can download the CSV, you will need to generate it. There are three d
 
 <table>
   <tr>
-    <td>![](/docs/engage/images/large_audience_csv.png)</td>
+    <td>![Screenshot of the Download audience list popup in Segment, with the All external ID types and Unformatted settings selected.](/docs/engage/images/large_audience_csv.png)</td>
     <td width="45%">Generating a CSV can take a substantial amount of time for large audiences (around 30 seconds for a formatted CSV with 1 million rows). For CSVs that are expected to take over 20 seconds, Segment displays an estimated generation time. After you generate the CSV file, leave the modal window open while Segment creates the file.
     (If the audience recalculates between when you click Generate and when you download the file, you might want to regenerate the file. The CSV is a snapshot from when you clicked Generate, and could be outdated.)</td>
   </tr>

src/getting-started/01-what-is-segment.md

@@ -7,7 +7,7 @@ In a nutshell, the Segment libraries ([Sources](/docs/connections/sources/catalo
 
 ## Overview
 
-![](images/subwaymap-2.png)
+![Diagram showing that data is routed from your sources, through Segment, and downstream to your destinations.](images/subwaymap-2.png)
 
 [Segment Spec methods](/docs/connections/spec/) are how you collect interaction data from your interfaces, and the [Sources](/docs/connections/sources/) are what you package with your interfaces to collect and route the data.
 
@@ -71,7 +71,7 @@ Segment maintains a catalog of destinations where you can send your data.
 
 <!--TODO: big list o' destinations image (programmatically update?) should go here-->
 
-![](images/dests_grid.png)
+![""](images/dests_grid.png)
 
 
 <div class="double">

src/getting-started/02-simple-install.md

@@ -51,7 +51,7 @@ Make note of or write down your write key, as you'll need it in the next steps.
 
 Any time you change a library's settings in the Segment App, the write key regenerates.
 
-![](/docs/connections/images/find_writekey.png)
+![Screenshot of a source's settings page, with the API Keys tab selected.](images/find_writekey.png)
 
 > info ""
 > [Cloud-sources](/docs/connections/sources/about-cloud-sources/) do not have write keys, as they use a token or key from your account with that service. Cloud-sources have other considerations and aren't part of this tutorial.
@@ -388,7 +388,7 @@ Once you've set up your Segment library, and instrumented at least one call, you
 
 The Source Debugger is a real-time tool that helps you confirm that API calls made from your website, mobile app, or servers arrive at your Segment Source, so you can quickly see how calls are received by your Segment source, so you can troubleshoot quickly without having to wait for data processing.
 
-![](/docs/connections/sources/images/debugger_view.png)
+![Screenshot of a source's Debugger, with a Track call selected and the Pretty view for a sample event.](/docs/connections/sources/images/debugger_view.png)
 
 The Debugger is separate from your workspace's data pipeline, and is not an exhaustive view of all the events ever sent to your Segment workspace. The Debugger only shows a sample of the events that the Source receives in real time, with a cap of 500 events. The Debugger is a great way to test specific parts of your implementation to validate that events are being fired successfully and arriving to your Source.
 
@@ -399,7 +399,7 @@ The Debugger shows a live stream of sampled events arriving at the Source, but y
 
 You can search on any information you know is available in an event payload to search in the Debugger and show only matching payloads. You can also use advanced search options to limit the results to a specific event.
 
-![](/docs/connections/sources/images/debugger_search.png)
+![Screenshot of the Event Debugger Advanced settings.](/docs/connections/sources/images/debugger_search.png)
 
 
 Two views are available when viewing a payload:

src/getting-started/04-full-install.md

@@ -36,8 +36,17 @@ A Track call is the most basic type of call, and can represent any type of event
 
 ## Identify calls
 
-![](images/identify-call.png)
+<!---![An identify event that identifies Michael Phillips, a customer who lives in New York, New York.](images/identify-call.png)--->
 
+```js
+analytics.identify (user_id: "12345abcde",
+  traits: {
+    email: '[email protected]',
+    name: 'Michael Phillips',
+    city: 'New York',
+    state: 'NY',
+    internal: True })
+```
 
 The Identify call allows Segment to know **who** is triggering an event.
 

src/guides/how-to-guides/automated-multichannel-reengagement.md

@@ -23,7 +23,7 @@ Before we proceed, it's important to register for these tools and enable them on
 
 ## Set it up
 
-![](images/auto-multi_aXa02yaL.png)
+![Diagram showing tracking data moving from your source, to Segment, and then to Braze, customer.io, and AdRoll.](images/auto-multi_aXa02yaL.png)
 
 When you send tracking data from your app or website to Segment, Segment will send the same data to all of your tools. Segment also collects key messaging events like Push Notification Opened and Email Opened from Braze and Customer.io, respectively, and sends that to other tools. By defining cohorts based on these events, you can create dynamic campaign audiences, to which customers can add and remove themselves.
 
@@ -33,21 +33,21 @@ In each of your destinations—Braze, Facebook, Customer.io, AdRoll—you can cr
 
 In Braze, create a segment of customers who added a product to their cart, but did not check out. The segment definition, in this case, should be people who have performed `Product Added`, but have not performed `Order Completed` . Send a push notification to these customers with a message that the cart was abandoned and that they can complete the transaction with a 10% coupon (or up to you).
 
-![](images/auto-multi_DDMDnUuY.png)
+![An Apple iPhone with a push notification from Toastmates that says Oh no! You left something in your basket.](images/auto-multi_DDMDnUuY.png)
 
 ## 2nd Line of Defense: The Email Reminder
 
 Because Segment automatically collects second-party data from Braze, you now also have push notification event data, like `Push Notification Opened` and `Push Notification Received` in Segment. You can use the `properties` on each of these events to define a property called `campaign_name` so you can tie these activities to a given campaign.
 
-![](images/auto-multi_262jquPl.png)
+![Screenshot of an email from Toastmates reminding the customer that they have a toast in their cart and giving them a coupon and a direct link to their cart.](images/auto-multi_262jquPl.png)
 
 This is helpful because now, you can define segments in Customer.io for customers who have triggered `Push Notification Received`, but not `Push Notification Opened` . You've now automated the process of targeting customers who don't open your push notifications. In Customer.io, From here, you can create a campaign that sends an email to those people asking them to check their push notifications and offering them a coupon to complete their order.
 
 ## 3rd Line of Defense: Paid Advertising
 
 Since Segment collects email event data, like `Email Opened`, from Customer.io, you can similarly create segments in Facebook Ads and AdRoll for when customers don't open your email. Create a segment where users have an `Email Delivered` event, but no `Email Opened` event. When users meet these criteria, they'll get automatically added to your retargeting campaigns. You can then serve them custom creatives about them neglecting to open your emails and, again, perhaps offer them a coupon to complete the transaction.
 
-![](images/auto-multi_dI5srBtw.png)
+![An ad for Toastmates with a frowning face on a piece of toast and a link for a coupon.](images/auto-multi_dI5srBtw.png)
 
 When users do not open an activation email, we can seamlessly add them to a specific retargeting campaign that contains messaging to remind them to activate.
 

src/guides/how-to-guides/cross-channel-tracking.md

@@ -16,7 +16,14 @@ _If you're interested in learning about what to track,_ [check out our guide on
 
 Digital marketing consists of owned marketing, earned marketing, and paid marketing.
 
-![](images/x-channel_WyEIhjxB.png)
+<!---![Table outlining the different types of marketing and how to track them.](images/x-channel_WyEIhjxB.png)--->
+
+| Type of marketing                  | How to track                                           |
+| ---------------------------------- | ------------------------------------------------------ |
+| Owned (domain, app)                | First-party data sources (on-page or in-app analytics) |
+| Owned (email, push notifications)  | Second-party data sources                              |
+| Earned (blogs, PR, partners, news) | UTM params, deep links on mobile                       |
+| Paid aquisition                    | UTM params, deep links on mobile                       |
 
 "Owned" marketing encompasses all activities you have full control over. It can be further split into first- and second-party data. First-party data is customer data generated on your site or in your app. Second-party data is customer data generated when your customers interact with your email or push notifications (e.g. "Email Opened", "Push Notification Received").
 
@@ -81,7 +88,7 @@ Add the complete URL as the `src` in the image tag.
 
 UTM parameters are types of query strings added to the end of a URL. When clicked, they let the domain owners track where incoming traffic is coming from and understand what aspects of their marketing campaigns are driving traffic.
 
-![](images/x-channel_GWqnp2I6.png)
+![Diagram showing how different UTM parameters redirect to your site and then are displayed in Traffic analytics.](images/x-channel_GWqnp2I6.png)
 
 UTM parameters are only used when linking to your site from outside of your domain. When a visitor arrives to your site using a link containing UTM parameters, Segment's client-side analytics.js library will automatically parse the URL's query strings, and store them within the `context` object as outlined [here](https://segment.com/docs/connections/spec/common/#context-fields-automatically-collected). These parameters do not persist to subsequent calls unless you pass them explicitly.
 
@@ -133,7 +140,15 @@ Your UTM parameters would match a pattern such as
 
 An example would be a National Toast Day campaign. This campaign would include emails, paid acquisition (via AdRoll and Facebook Ads), organic social (Twitter), and promotional content on partners' blogs.
 
-![](images/x-channel_Z3KerVHZ.png)
+<!---![Table outlining different channels and the UTM parameters you can pass through them.](images/x-channel_Z3KerVHZ.png)--->
+
+| Channel  | UTM Campaign      | UTM Medium     | UTM source  |
+| -------- | ----------------- | -------------- | ----------- |
+| Email    | national-toastday | email          | customer.io |
+| News     | national-toastday | news           | toastnation |
+| AdRoll   | national-toastday | display        | adroll      |
+| Facebook | national-toastday | paid-social    | facebook    |
+| Twitter  | national-toastday | organic-social | twitter     |
 
 Having the consistent UTM parameters naming convention simplifies the downstream analysis and the ease of querying across dimensions, such as within the campaign, which medium or source was the best. Or which placement of the display ad led to the most conversions.
 

src/guides/how-to-guides/dynamic-coupon-program.md

@@ -38,7 +38,7 @@ We'll conduct a split test (half of the cohort will represent the control group
 
 First, register for an account with Customer.io and Amplitude. Then, enable Customer.io and enable Amplitude on your Segment project. Finally, go into your Customer.io account and enable "sending data to Segment":
 
-![](images/dyncoupon_tps7HAgP.png)
+![Screenshot of the Streaming Data Out page in Customer.io, with an enabled Segment.com option.](images/dyncoupon_tps7HAgP.png)
 
 [You can find those destination settings in Customer.io here.](https://fly.customer.io/account/integration_settings)
 
@@ -50,13 +50,13 @@ When everything is enabled, customer event data such as `Order Completed` and `P
 
 Now we define the specific cohort in Customer.io as per our conditions listed earlier: someone who spends over $20 per order and shops over twice a month. In Customer.io, go to "Segments" and "Create Segment":
 
-![](images/dyncoupon_kTaK5L7l.png)
+![Screenshot of the Segment builder in Customer.io, with the title "Coupon Loyalty Experiment".](images/dyncoupon_kTaK5L7l.png)
 
 After this cohort is created, then when a customer makes the third purchase in a month and it's over $20, then she will be added to this segment.
 
 Next, we'll create a "segment trigger campaign", where Customer.io will send a message the first time someone enters a segment. The segment in this case will be the one we just created: Coupon Loyalty Experiment.
 
-![](images/dyncoupon_lE6v8cXN.png)
+![Screenshot of the Segment Trigger Campaign page in Customer.io, with a sample email ready to send to customers in the segment.](images/dyncoupon_lE6v8cXN.png)
 
 Save the changes and then enable the campaign!
 
@@ -71,29 +71,29 @@ In Amplitude, we can create a funnel that compares the two cohorts—one who rec
 
 First, let's define a behavioral cohort with the conditions of being loyal customers so we can use it when we analyze the conversion funnel:
 
-![](images/dyncoupon_uiJaB5iF.png)
+![Screenshot of a Loyal Customers segment in Amplitude, comprised of users who spent at least $20 and purchased more than two times in the last 30 days](images/dyncoupon_uiJaB5iF.png)
 
 We'll also have to create a second identical cohort, except with the only difference that these customers did not receive the coupon email. We need this cohort so we can create the conversion funnel with the control group.
 
-![](images/dyncoupon_vt165Rxc.png)
+![Screenshot of a Loyal Customers (Control) segment in Amplitude, comprised of users who spent at least $20 and purchased more than two times in the last 30 days, but did not get the loyal customer email.](images/dyncoupon_vt165Rxc.png)
 
 After we've created these two cohorts, we'll create two funnel charts. The first funnel will look at the control group. The second funnel will look at the group that received the coupon email.
 
-![](images/dyncoupon_2IEwSA4L.png)
+![Screenshot of the Funnel Analysis page in Amplitude showing the Loyal Customers (Control) segment.](images/dyncoupon_2IEwSA4L.png)
 
 Resulting in:
 
-![](images/dyncoupon_kEDuPed8.png)
+![A bar chart showing 233 visits to the main landing screen, 98 products added to cart, and 66 purchases.](images/dyncoupon_kEDuPed8.png)
 
 We can see that the control group that did not receive the email for the coupon resulted in 233 people visiting the store, with 66 conversions.
 
 The funnel for the group who did receive the emails can be created with these parameters:
 
-![](images/dyncoupon_AIZxZDJG.png)
+![Screenshot of the Funnel Analysis page in Amplitude showing the Loyal Customers segment.](images/dyncoupon_AIZxZDJG.png)
 
 Resulting in:
 
-![](images/dyncoupon_TUWSroDC.png)
+![A bar chart showing 758 emails delivered, 560 emails opened, 168 visits to the main landing screen, 134 products added to cart, and 95 purchases.](images/dyncoupon_TUWSroDC.png)
 
 The email itself drove 168 customers to the store, which also saw higher conversions to `Product Added` and ultimately `Order Completed` .
 

src/guides/how-to-guides/forecast-with-sql.md

@@ -91,13 +91,13 @@ To retrieve a table with the right columns for analysis, let's use the follow SQ
 
 This returns a table where each row is a unique user and the columns are email, number of purchases within the time window, number of discrete time units since last purchase, and average purchase order.
 
-![](images/forecast_MiUlaNKr.jpg)
+![Screenshot of a spreadsheet with columms email, frequency, days_since_last_transaction, days_since_first_transaction, and average_transaction_size.](images/forecast_MiUlaNKr.jpg)
 
 > Here is a screenshot of the first twelve rows returned from the query in Mode Analytics.
 
 Export this data to a CSV, then copy and paste it in the first sheet of the Google Sheet where the blue type is in the below screenshot:
 
-![](images/forecast_JBqlb4Hf.png)
+![Screenshot of the Segment Tracking Plan Google Spreadsheet, with 11 user records.](images/forecast_JBqlb4Hf.png)
 
 Also be sure to add the total time in days in cell B6. This is important as the second sheet uses this time duration for calculating net present value of future payments.
 
@@ -107,7 +107,7 @@ After you paste in the CSV from the table into the first tab of the sheet, the n
 
 You can export your Google Sheet as an Excel document. Then, use Excel Solver to minimize the log-likelihood number in cell B5, while keeping the parameters from B1:B4 greater than 0.0001.
 
-![](images/forecast_GkZFJP5q.png)
+![Screenshot of the Solver Parameters popup in Google Sheets, with the cells B1:B4 set greater than or equal to 1 E to the -5.](images/forecast_GkZFJP5q.png)
 
 After Solver runs, cells B1:B4 will be updated to represent the model's estimates. Now, you can hard code those back into the sheet on Google Sheets. The next sheet relies on these model estimates to calculate the expected purchases per customer.
 
@@ -184,7 +184,7 @@ Below is a simple query to get a table of a user's actions in rows. Just replace
 
 This above query for user whose `user_id` is `"46X8VF96G6"` returns the below table:
 
-![](images/forecast_3N82oB4v.png)
+![A table with two columns: event_or_page_viewed and recieved_at.](images/forecast_3N82oB4v.png)
 
 At Toastmates, most of the highest forward-looking expected LTV customers share one thing in common: averaging two orders per month with an average purchase size of $20.