Update shopify-ab-test.mdx

update code for grabbing stableID

docs/guides/shopify-ab-test.mdx

@@ -49,10 +49,16 @@ Below is boilerplate custom pixel code that provides a function to send events b
 ```js
 /**
 * Util function for tracking events back to statsig
+* Find the user's stableID checking js-sdk >= v2 then fallback to v1 storage key
 */
+const stableID = (function () {
+  for (var key in localStorage) {
+    if (key.includes('statsig.stable_id.')) return localStorage.getItem(key);
+  }
+})() || localStorage.getItem('STATSIG_LOCAL_STORAGE_STABLE_ID');
 const statsigEvent = async (eventKey, eventValue = null, metadata = {}, userObject = {}) => {
   Object.assign(userObject, {
-    customIDs: {stableID: localStorage.getItem('STATSIG_LOCAL_STORAGE_STABLE_ID')} // attach stableID automatically
+    customIDs: {stableID: stableID} // attach stableID automatically
   });
   await fetch('https://events.statsigapi.net/v1/log_event', {
     method: 'POST',
@@ -94,4 +100,4 @@ Whether you're using Shopify's [Hydrogen app](https://shopify.dev/docs/storefron
 #### Integrating data sources for experiment metrics
 Along with the measuring simple click stream and point-of-sale behavior as [outlined here](http://localhost:3004/guides/first-shopify-abtest#configure-event-tracking-and-metrics), commerce businesses performing deeper experimentation often want to integrate offline data systems and measure experiments using existing metrics that the broader business uses. 
 
-Commonly, the Data Warehouse is the source of truth for user purchase data and other categories of offline data. This affords customers the ability to define more [bespoke metrics](/statsig-warehouse-native/configuration/metrics#metric-types) using filtering, aggregations and incorporating other datasets in the warehouse for segmenting experiment results.   
+Commonly, the Data Warehouse is the source of truth for user purchase data and other categories of offline data. This affords customers the ability to define more [bespoke metrics](/statsig-warehouse-native/configuration/metrics#metric-types) using filtering, aggregations and incorporating other datasets in the warehouse for segmenting experiment results.