{"id":121067,"date":"2026-04-02T10:26:19","date_gmt":"2026-04-02T10:26:19","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2026\/04\/02\/breaking-into-the-black-box-an-e-commerce-case-study-on-unlocking-metas-product-level-ad-data\/"},"modified":"2026-04-02T10:27:42","modified_gmt":"2026-04-02T10:27:42","slug":"breaking-into-the-black-box-an-e-commerce-case-study-on-unlocking-metas-product-level-ad-data","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2026\/04\/02\/breaking-into-the-black-box-an-e-commerce-case-study-on-unlocking-metas-product-level-ad-data\/","title":{"rendered":"Breaking into the \u2018Black Box\u2019: An e-commerce case study on unlocking Meta&#8217;s product-level ad data"},"content":{"rendered":"<p> <a href=\"https:\/\/go.fiverr.com\/visit\/?bta=1052423&nci=17043\" Target=\"_Top\"><img loading=\"lazy\" decoding=\"async\" border=\"0\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/fiverr.ck-cdn.com\/tn\/serve\/?cid=40081059\"  width=\"601\" height=\"201\"><\/a>\n<\/p>\n<div id=\"\">\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n<h2 class=\"wp-block-heading\" id=\"the-advertising-dilemma-the-black-box-of-dynamic-ads\">The promoting dilemma: The \u2018Black Field\u2019 of dynamic adverts<\/h2>\n<p>Ecommerce and Meta usually go hand in hand. You can provide Meta a 20,000-item catalogue and a price range \u2013 and with its <a rel=\"noreferrer noopener\" href=\"https:\/\/business.facebook.com\/business\/help\/397103717129942?id=1913105122334058&amp;content_id=KHT28yMwaUyu5Uz&amp;ref=sem_smb&amp;utm_term=dsa-1720753164846&amp;gclid=CjwKCAiAt8bIBhBpEiwAzH1w6bh_IyIYHapWQZpl1dx8U6yuMw6RxluIAqLmx2BGMQcCQfg6CdOM3BoC6lAQAvD_BwE&amp;gad_source=1&amp;gad_campaignid=21436655444&amp;gbraid=0AAAAACr-yC-rnl8fG69EVBcBZDewEyP9B\">AI-powered Advantage+<\/a> campaigns \u2013 it\u2019ll attempt to pair the proper individual with the proper product, whether or not that\u2019s a brand new buyer or somebody who\u2019s already considered these merchandise earlier than.<\/p>\n<p>However what\u2019s <em>really<\/em> occurring inside that advert? And is there a option to optimise this \u2018black field\u2019 Dynamic Product Advert (DPA) format?<\/p>\n<p>Advertisers can see ad-level efficiency, however have <strong>no<\/strong> platform-native insights on <em>which particular merchandise<\/em> are being proven, clicked, or ignored inside a broad DPA.<\/p>\n<p class=\"has-medium-font-size\"><strong>Is the algorithm making the proper selections?<\/strong><\/p>\n<p>That\u2019s precisely the query we needed to reply.\u00a0<\/p>\n<p>There are three widespread traps manufacturers fall into:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>Over-segmentation<\/strong> \u2013 manufacturers that need extra perception break aside their catalogue into area of interest product units with tons of DPAs\n<ul class=\"wp-block-list\">\n<li>Professionals \u2013 you can provide every advert a bespoke identify, which tells you precisely what\u2019s being served. Good!<\/li>\n<li>Cons \u2013 this reduces information density and might kill ROI. There\u2019s additionally a bent to attempt to predict which audiences will reply to which merchandise, which is now not efficient for many manufacturers since Meta\u2019s improved Andromeda updates<\/li>\n<\/ul>\n<\/li>\n<li><strong>Convoluted reporting<\/strong> \u2013 manufacturers attempt to infer what merchandise Meta is prioritising by pairing GA4 session information (<strong>classes<\/strong> by product) to Meta adverts information (the campaigns\/adverts that despatched these customers)\n<ul class=\"wp-block-list\">\n<li>Professionals \u2013 permits some evaluation with out falling into the \u2018over-segmentation\u2019 pitfall<\/li>\n<li>Cons \u2013 time-consuming to arrange, and incomplete. This methodology doesn\u2019t inform us something about product-specific engagement inside Meta \u2013 we might solely be guessing at click-through-rate, spend and impressions.\u00a0<\/li>\n<\/ul>\n<\/li>\n<li><strong><em>\u201c<\/em>Set it and overlook\u201c<\/strong> \u2013 manufacturers quit all management and let Meta take the wheel\n<ul class=\"wp-block-list\">\n<li>Professionals \u2013 avoids over-segmentation points<\/li>\n<li>Cons \u2013 There\u2019s an enormous threat in trusting the algorithm. You may be pushing merchandise that get excessive impressions however low gross sales, successfully burning your price range and dropping effectivity.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>Attempting to deduce efficiency and make selections from Meta Adverts Supervisor UI information is a threat. Many entrepreneurs are nonetheless not assured in AI-powered campaigns \u2013 if this is applicable to you, you might want to learn this <a rel=\"noreferrer noopener\" href=\"https:\/\/www.impressiondigital.com\/resources\/white-papers\/ais-evolving-role-in-paid-social-advertising\/\">whitepaper<\/a> earlier than carrying on.<\/p>\n<p>In my position at Impression, we created our <a rel=\"noreferrer noopener\" href=\"https:\/\/www.impressiondigital.com\/about\/our-technology\/\"><strong>Dynamic Product Explorer <\/strong>(<strong>DPEx<\/strong>)<\/a> to resolve this problem \u2013 however worry not, I can stroll you thru the precise steps so you are able to do the identical to your model.<\/p>\n<p>Our pilot shopper for <strong>DPEx<\/strong> was a <strong>main toilet retailer<\/strong> investing closely in DPAs inside conversion campaigns.\u00a0<\/p>\n<p>Let\u2019s undergo the three phases in our journey to overcoming this ecommerce problem \u2013 so you possibly can recreate.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n<h2 class=\"wp-block-heading\" id=\"phase-one-surfacing-engagement-data\">Part one: Surfacing engagement information<\/h2>\n<p>The primary stage in <strong>DPEx<\/strong> was visibility \u2013 understanding what was occurring now inside these \u2018black field\u2019 DPA codecs.<\/p>\n<p>As I mentioned above, Meta doesn\u2019t instantly report which particular product led to a particular buy inside a DPA within the Adverts Supervisor interface. It\u2019s merely not an obtainable breakdown in the identical method that age, placement and so on. are provided.<\/p>\n<p>However the excellent news is {that a} treasure trove of perception is buried within the <a rel=\"noreferrer noopener\" href=\"https:\/\/developers.facebook.com\/docs\/marketing-api\">Meta APIs<\/a>:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>Meta Advertising API<\/strong> (particularly the <strong>Insights API<\/strong>) is the principle API we use to get all advert efficiency information. It\u2019s how we\u2019re pulling the important thing metrics like spend, impressions, and clicks for every ad_id and product_id.<\/li>\n<li><strong>Meta Commerce Platform API<\/strong> (or Catalogue API), this API gives the checklist of all product_ids and their related particulars (like identify, value, class, and so on)<\/li>\n<\/ol>\n<p>By becoming a member of these two APIs, we will extract important information, damaged down by product, with their particular <strong>clicks<\/strong> and <strong>impressions<\/strong>.\u00a0<\/p>\n<p>We collaborated with our Media Options colleagues to pipe this information into our information warehouse (we use BigQuery, however there are various choices on the market).<\/p>\n<p>This new, mixed dataset was then visualised in a <a rel=\"noreferrer noopener\" href=\"https:\/\/lookerstudio.google.com\/navigation\/reporting\">Looker Studio<\/a> report template. Once more, different choices can be found. To make sense of the information, we would have liked an simply navigable report, moderately than pages of uncooked information. We constructed the next visualisations:<\/p>\n<p><strong>Product scatter chart<\/strong> \u2013 separating every product into 4 distinct classes:<\/p>\n<ul class=\"wp-block-list\">\n<li>\u2018<strong>Star performers<\/strong>\u2018: Excessive impressions and excessive clicks.<\/li>\n<li>\u2018<strong>Promising merchandise<\/strong>\u2018: Low impressions however a excessive click-through charge.<\/li>\n<li>\u2018<strong>Window customers<\/strong>\u2018: Excessive impressions however very low clicks.<\/li>\n<li>\u2018<strong>Low precedence<\/strong>\u2019: low clicks and impressions<\/li>\n<\/ul>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" class=\"wp-image-37737\" loading=\"lazy\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/images.impression.co.uk\/2026\/04\/dpex-ecommerce-scatter.png?auto=compress%2Cformat&amp;w=816&amp;h=280&amp;q=65&amp;format=webp\" width=\"1192\" height=\"408\"\/><figcaption class=\"wp-element-caption\"><strong><em>Supply: <\/em><\/strong><em>Impression Dynamic Product Explorer (DPEx), ecommerce shopper<\/em>.<\/figcaption><\/figure>\n<\/div>\n<p><strong>High\/backside merchandise bar charts <\/strong>\u2013 see at a look the highest 10 and backside 10 merchandise by engagement.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" class=\"wp-image-37739\" loading=\"lazy\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/images.impression.co.uk\/2026\/04\/dpex-top-products.png?auto=compress%2Cformat&amp;w=816&amp;h=493&amp;q=65&amp;format=webp\" width=\"1212\" height=\"732\"\/><\/figure>\n<\/div>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" class=\"wp-image-37741\" loading=\"lazy\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/images.impression.co.uk\/2026\/04\/dpex-bottom-products.png?auto=compress%2Cformat&amp;w=816&amp;h=486&amp;q=65&amp;format=webp\" width=\"1208\" height=\"718\"\/><figcaption class=\"wp-element-caption\"><strong><em>Supply: <\/em><\/strong><em>Impression Dynamic Product Explorer (DPEx), ecommerce shopper<\/em><\/figcaption><\/figure>\n<\/div>\n<p><strong>Product particulars desk \u2013 <\/strong>view detailed metrics for every product.<\/p>\n<p>This might all be filtered by product identify, product kind, availability, and another metrics we needed (color, value, and so on.)<\/p>\n<p>This gave us our first-ever view of the <strong>DPEx<\/strong> report \u2013 product-level advert engagement!<\/p>\n<p>Even with simply engagement information, this report supplied worth:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Inventive<\/strong> \u2013 we used the information to enhance inventive briefs\n<ul class=\"wp-block-list\">\n<li>For our toilet shopper, it was fascinating to see how a lot Meta was pushing <strong>non-white merchandise<\/strong> (orange sinks, inexperienced baths) \u2013 even though 95% of their product gross sales are conventional white variations.\u00a0<\/li>\n<li>We hadn\u2019t prioritised these merchandise initially, however have now created heaps extra video and creator content material that includes these extremely clickable variations<\/li>\n<\/ul>\n<\/li>\n<li><strong>Product segmentation<\/strong> \u2013 We are able to now construct highly effective, data-driven product units based mostly on actual engagement metrics\n<ul class=\"wp-block-list\">\n<li>For instance, we examined exhibiting solely our most participating \u2018Star Performer\u2019 merchandise in feed-powered assortment adverts in our higher funnel campaigns. In these campaigns, the algorithm has fewer indicators to optimise in the direction of, so a broad product set improved because of this.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Effectivity<\/strong> \u2013 this automated a fancy evaluation that was beforehand unwieldy and time-consuming<\/li>\n<\/ul>\n<p>Crucially, for the primary time, we had sufficient proof to problem Meta\u2019s \u2018finest observe\u2019 of utilizing the widest doable product set.<\/p>\n<p class=\"has-medium-font-size\"><strong>Pitfalls &amp; key concerns<\/strong><\/p>\n<p>Nonetheless, this work solely went thus far. There have been some key issues lacking that simply tapping into Meta\u2019s API received\u2019t remedy:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Engagement vs. Conversions: <\/strong>The main downfall with that is that product-level breakdowns are solely obtainable for clicks and impression information \u2013 <strong>not<\/strong> income or conversions. The \u201cWindow Customers\u201d class, for instance, identifies merchandise that get low clicks, however we couldn\u2019t (on this section) definitively say they don\u2019t result in gross sales.<\/li>\n<li><strong>Context is vital: <\/strong>This information is a strong new diagnostic device. It tells us what Meta is exhibiting and what customers are clicking, which is a large step ahead. The why (e.g., \u201cis that this high-impression, low-click merchandise only a high-value product?\u201d) nonetheless requires our workforce\u2019s evaluation<\/li>\n<\/ul>\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n<p>We knew the above Meta-only information simply explores one a part of the journey. To evolve, we would have liked to affix with GA4 information to search out out what prospects are literally shopping for after they\u2019re interacting with our feed-powered dynamic product adverts.<\/p>\n<p>As an alternative of counting on platform-modelled conversions, we grabbed information from GA4 particularly for buy occasions. This gave us the exhausting numbers: the income and items bought for each transaction.<\/p>\n<p>The essential key right here was getting this information by the merchandise ID, as that is what we used to attach our \u201crevenue\u201d information again to the \u201cengagement\u201d information from Meta.<\/p>\n<p class=\"has-medium-font-size\"><strong>Pitfalls &amp; key concerns<\/strong><\/p>\n<p>Becoming a member of Meta and GA4 information sounds simple sufficient \u2013 however there have been some key blockers to beat. Our first two have been hygiene-focused:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Clear information.<\/strong> The entire mannequin breaks in case your Meta ID doesn\u2019t cleanly match your GA4 IDs. You need to guarantee your product catalogues and your GA4 tagging are completely aligned earlier than you begin.<\/li>\n<li><strong>UTM self-discipline.<\/strong> To correctly hyperlink a purchase order in GA4 again to a particular Meta advert, we would have liked to seize the ad_id by way of your UTM parameters. This ad_id is the magic bridge that lets us be part of the 2 datasets.<\/li>\n<\/ul>\n<p>Nonetheless, the final one was more durable to beat \u2013 <strong>attribution points<\/strong>. The GA4 information will virtually all the time present <em>decrease<\/em> conversion numbers than Meta\u2019s UI.\u00a0<\/p>\n<p>It is because Meta usually \u2018over-credits\u2019 \u2013 it advantages from longer attribution home windows, together with view-through conversions, and it offers itself full credit score for every conversion it measures (moderately than spreading out throughout a number of channels).\u00a0<\/p>\n<p>GA4, nevertheless, usually \u2018under-credits\u2019 channels like Meta. It makes use of <a rel=\"noreferrer noopener\" href=\"https:\/\/www.impressiondigital.com\/blog\/how-data-driven-attribution-works-in-google-analytics-4\/\">data-driven attribution<\/a> to attempt to give credit score to a number of touchpoints \u2013 however it\u2019s unable to fully mirror consumer journeys \u2013 that means GA4 doesn\u2019t simply recognise when a consumer has interacted with adverts earlier than buying, and credit score accordingly.\u00a0<\/p>\n<p>Though we\u2019d like to get a 1:1 match from every product buy again to the precise product interacted with on Meta, neither platform can simply present this perception. There&#8217;s nonetheless worth find the relative insights and developments.<\/p>\n<p>Right here\u2019s an instance:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Meta\u2019s UI:<\/strong> Reported our \u2018Luxurious Freestanding Baths \u2013 Inexperienced\u2019 product is our prime performer final month, with excessive volumes of clicks and impressions in our dynamic adverts.<\/li>\n<li><strong>The Downside:<\/strong> Once we joined our GA4 information, we noticed no gross sales for that <em>particular tub <\/em>final month, in any respect, from any channel! That is the place, earlier than we would\u2019ve panicked and excluded the product from our catalogue to \u2018save spend\u2019, and that is the pitfall.<\/li>\n<\/ul>\n<p>However, by taking a look at <em>all<\/em> objects bought in these GA4 classes that originated from the \u2018Luxurious Tub \u2013 Inexperienced\u2019 product, we uncover that many customers who clicked the tub went on to transform however purchased the white variation as an alternative.<\/p>\n<p><strong>The Actionable Perception:<\/strong> The \u2018Luxurious Tub\u2019 advert wasn\u2019t a failure; it was a extremely efficient halo product \u2013 drawing in aspirational prospects who then convert to purchase different merchandise.<\/p>\n<p>We are able to then confidently fee creator content material, specializing in this tub to attract in new customers even when we all know customers are probably to purchase a distinct color relating to buy.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n<h2 class=\"wp-block-heading\" id=\"phase-three-performance-enhanced-feeds\">Part three: Efficiency-enhanced feeds<\/h2>\n<p>As soon as we have now this information at your fingertips, the temptation is to deal with it purely for insights and information.<\/p>\n<p>The subsequent stage was even higher \u2013 utilizing this information to create automated supplementary feeds. Utilizing those self same 4 product classes from our scatter charts, we created automated logic to fabricate new product units and carried out the next assessments:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>\u2018Window Customers\u2019:<\/strong> (Excessive impressions, low clicks\/gross sales). Feed these into an <em>exclusion<\/em> set to grasp if effectivity improves once we take away them from the feed.<\/li>\n<li><strong>\u2018Promising Merchandise\u2019:<\/strong> (Excessive CTR, excessive CVR, low impressions). Feed these right into a <em>scaling<\/em> set with extra price range to grasp if demand is hidden.<\/li>\n<li><strong>\u2018Star Performers\u2019:<\/strong> (Excessive impressions, excessive clicks). Feed these right into a <em>retargeting<\/em> set to recapture engaged customers with our signature ranges.<\/li>\n<\/ul>\n<p class=\"has-medium-font-size\"><strong>Pitfalls &amp; key concerns<\/strong><\/p>\n<p>The assessments above are merely examples of hypotheses. Nonetheless, your mileage will range! We strongly suggest testing these use instances strategically with a view to perceive whether or not it provides to your total efficiency.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n<h2 class=\"wp-block-heading\" id=\"is-your-brand-ready-to-break-out-of-the-black-box\">Is your model prepared to interrupt out of the \u2018Black Field\u2019?<\/h2>\n<p>Breaking out of Meta\u2019s \u201cBlack Field\u201d isn\u2019t only a technical train; it\u2019s a strategic must-do for e-commerce advertisers. The journey strikes from surfacing fundamental engagement information (Part one) to becoming a member of it with gross sales information for true, profit-driven insights (Part two), and in the end, to automating your technique with performance-enhanced feeds (Part three).<\/p>\n<p>That is how you progress from trusting the algorithm to difficult it with proof. In case you\u2019re a decision-maker questioning the place to begin, listed below are the three inquiries to ask:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>\u201cAre you able to present me which particular <em>merchandise<\/em> in our catalogue are being prioritised by Meta?\u201d<\/strong><\/li>\n<li><strong>\u201cAre our Meta product_ids and GA4 item_ids an identical?\u201d<\/strong><\/li>\n<li><strong>\u201cAre we capturing the advert.id in our UTM parameters on each single advert?\u201d<\/strong><\/li>\n<\/ol>\n<p>If the solutions to those questions are \u2018I don\u2019t know,\u2019 you\u2019re in all probability nonetheless working contained in the black field. Breaking it open is feasible \u2013 it simply requires the proper information, the proper technical joins, and the need to lastly see what\u2019s really driving efficiency. <a rel=\"noreferrer noopener\" href=\"https:\/\/www.impressiondigital.com\/get-in-touch\/\">Get in touch with our team.<\/a><\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a rel=\"noreferrer noopener\" href=\"https:\/\/www.impressiondigital.com\/us\/paid-media\/\"><img decoding=\"async\" class=\"wp-image-27529\" loading=\"lazy\" src=\"https:\/\/mailinvest.blog\/wp-content\/themes\/breek\/assets\/images\/transparent.gif\" data-lazy=\"true\" data-src=\"https:\/\/images.impression.co.uk\/2024\/03\/Paid-Media_Banner_01.jpg?auto=compress%2Cformat&amp;w=816&amp;h=115&amp;q=65&amp;format=webp\" width=\"3840\" height=\"540\"\/><\/a><\/figure>\n<\/div>\n<\/div>\n<iframe data-lazy=\"true\" data-src=\"https:\/\/www.fiverr.com\/gig_widgets?id=U2FsdGVkX18x7XQvttUTrv1oEqmGNGTgvvCUiUoJ\/AP4z\/UyMz8lXGOLpu15jIMxBbTR0gmD5uBoFvhC4KWeALQRp3h\/X\/AwcVD0K8Wj9H\/ZzYKzcCNHosB9oS4SCJJFWiN85P9ICAc4OgCoE\/wHKIY7CDkf2\/DQ1vqGvk4smVe5cRDEmrLPCWi4FC8p40VUhSmWQ5udCm0zoJtorgWv3vbDQw0kKYkwn39ozAnQXDe+YvWMxkLFWA+O3TFwkJvdkIK+\/AUSnRssPKt5WHY0FhNOxnSPcLslEL4G4\/RfP95ve99U+kRnDy3X+KtzdQLY+u935ghON\/o3UE4IMv9oN6JX9RnxzL\/LRcOgnHigxStSGPKsZYtnz8RWNVT\/rOLAibqiWJadC5MYHRbekF3eg6FOGrQGkXYbsn0+a5aovnlLCbLwIqY9fcS17UX8J235iQ6cdmHNbrPeS84CMm34RA==&affiliate_id=1052423&strip_google_tagmanager=true\" loading=\"lazy\" data-with-title=\"true\" class=\"fiverr_nga_frame\" frameborder=\"0\" height=\"350\" width=\"100%\" referrerpolicy=\"no-referrer-when-downgrade\" data-mode=\"random_gigs\" onload=\" var frame = this; var script = document.createElement('script'); script.addEventListener('load', function() { window.FW_SDK.register(frame); }); script.setAttribute('src', 'https:\/\/www.fiverr.com\/gig_widgets\/sdk'); document.body.appendChild(script); \" ><\/iframe>\n<br \/><a href=\"https:\/\/www.impressiondigital.com\/blog\/breaking-into-the-black-box-an-e-commerce-case-study-on-unlocking-metas-product-level-ad-data\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The promoting dilemma: The \u2018Black Field\u2019 of dynamic adverts Ecommerce and Meta usually go hand in hand. You can provide Meta a 20,000-item catalogue and&#8230;<\/p>\n","protected":false},"author":1,"featured_media":121068,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-121067","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-universe"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Breaking into the \u2018Black Box\u2019: An e-commerce case study on unlocking Meta&#039;s product-level ad data - mailinvest.blog<\/title>\n<meta name=\"description\" content=\"Technology is forever changing, and there are always new pieces of technology to replace obsolete ones. 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