{"id":464,"date":"2026-03-13T16:43:25","date_gmt":"2026-03-13T16:43:25","guid":{"rendered":"https:\/\/usepulseai.com\/blog\/2026\/03\/13\/how-to-build-marketing-dashboard-power-bi\/"},"modified":"2026-03-13T16:43:25","modified_gmt":"2026-03-13T16:43:25","slug":"how-to-build-marketing-dashboard-power-bi","status":"publish","type":"post","link":"https:\/\/usepulseai.com\/blog\/2026\/03\/13\/how-to-build-marketing-dashboard-power-bi\/","title":{"rendered":"How to Build a Marketing Dashboard in Power BI (Step-by-Step Guide)"},"content":{"rendered":"<p>A marketing dashboard in Power BI lets you track campaign performance, channel attribution, conversion rates, and ROI in one place. This guide walks through building one from scratch \u2014 connecting your marketing data, creating the data model, building key visualizations, and publishing for your team. Expect 4-8 hours for your first dashboard if you&#8217;re familiar with Power BI, longer if you&#8217;re new.<\/p>\n<p>The quick version: connect your marketing data sources (Google Analytics, ad platforms, CRM), model the relationships, build KPI cards for spend\/revenue\/ROAS\/conversions, add trend charts by channel, create a campaign performance table, and add date\/channel filters. If that sounds like more work than it should be, there&#8217;s a faster approach covered at the end.<\/p>\n<hr \/>\n<h2>What You&#8217;ll Need<\/h2>\n<p><strong>Data sources<\/strong> \u2014 at minimum, you&#8217;ll want:<\/p>\n<ul>\n<li>Google Analytics (website traffic, conversions)<\/li>\n<li>Ad platform data (Google Ads, Meta Ads, LinkedIn Ads)<\/li>\n<li>CRM data (HubSpot, Salesforce) for lead-to-revenue tracking<\/li>\n<li>Email platform data (Mailchimp, Klaviyo) for email metrics<\/li>\n<\/ul>\n<p><strong>Power BI Desktop<\/strong> \u2014 free download from Microsoft. Windows only.<\/p>\n<p><strong>Basic Power BI knowledge<\/strong> \u2014 this guide assumes you&#8217;ve opened Power BI before. If not, expect extra time learning the interface.<\/p>\n<p><strong>About 4-8 hours<\/strong> \u2014 connecting data takes the longest. The actual dashboard building goes faster once your data model is right.<\/p>\n<hr \/>\n<h2>Step 1: Connect Your Marketing Data Sources<\/h2>\n<p>Open Power BI Desktop \u2192 Home \u2192 Get Data.<\/p>\n<p><strong>Google Analytics 4:<\/strong><\/p>\n<p>Power BI has a native Google Analytics connector. Click Get Data \u2192 Online Services \u2192 Google Analytics \u2192 Connect. Sign in with your Google account, select your GA4 property, and choose the dimensions and metrics you need:<\/p>\n<ul>\n<li>Dimensions: Date, Source\/Medium, Campaign, Landing Page, Device Category<\/li>\n<li>Metrics: Sessions, Users, New Users, Conversions, Revenue (if e-commerce)<\/li>\n<\/ul>\n<p>Load the data. Note: GA4&#8217;s API can be slow for large date ranges \u2014 start with 12 months.<\/p>\n<p><strong>Google Ads:<\/strong><\/p>\n<p>Get Data \u2192 Online Services \u2192 Google Ads. Connect with your Google account. Select your account and pull:<\/p>\n<ul>\n<li>Campaign Name, Ad Group, Date<\/li>\n<li>Impressions, Clicks, Cost, Conversions, Conversion Value<\/li>\n<\/ul>\n<p><strong>Meta Ads (Facebook\/Instagram):<\/strong><\/p>\n<p>Power BI doesn&#8217;t have a direct Meta Ads connector. Two options:<\/p>\n<ol>\n<li><strong>Export CSV<\/strong> from Meta Ads Manager (Ads \u2192 Reports \u2192 Export \u2192 CSV) and load into Power BI via Get Data \u2192 Text\/CSV<\/li>\n<li><strong>Use a connector tool<\/strong> like Supermetrics, Funnel.io, or Fivetran to sync Meta data to a database or Google Sheets, then connect Power BI to that<\/li>\n<\/ol>\n<p><strong>HubSpot\/CRM:<\/strong><\/p>\n<p>Get Data \u2192 Online Services \u2192 HubSpot (if available) or connect via API using Web connector. Pull:<\/p>\n<ul>\n<li>Contacts: Create Date, Source, Lifecycle Stage, Deal Amount<\/li>\n<li>Deals: Close Date, Pipeline Stage, Amount, Source<\/li>\n<\/ol>\n<p><strong>Email platform:<\/strong><\/p>\n<p>Most email platforms (Mailchimp, Klaviyo) require CSV export or API connector. Pull:<\/p>\n<ul>\n<li>Campaign Name, Send Date, Recipients, Opens, Clicks, Unsubscribes, Revenue<\/li>\n<\/ul>\n<hr \/>\n<h2>Step 2: Build the Data Model<\/h2>\n<p>This is where most marketing dashboards fail. Without a proper data model, your numbers won&#8217;t add up across sources.<\/p>\n<p><strong>Create a Date Table:<\/strong><\/p>\n<p>Every marketing dashboard needs a shared date dimension. In Power BI, go to Modeling \u2192 New Table:<\/p>\n<pre><code class=\"language-dax\">DateTable = \n<p>ADDCOLUMNS(<\/p>\n<p>CALENDARAUTO(),<\/p>\n<p>\"Year\", YEAR([Date]),<\/p>\n<p>\"Quarter\", \"Q\" & FORMAT([Date], \"Q\"),<\/p>\n<p>\"Month\", FORMAT([Date], \"MMMM\"),<\/p>\n<p>\"Month Number\", MONTH([Date]),<\/p>\n<p>\"Week\", WEEKNUM([Date]),<\/p>\n<p>\"Day of Week\", FORMAT([Date], \"dddd\")<\/p>\n<p>)<\/code><\/pre>\n<\/p>\n<p>Mark it as a Date Table (Modeling \u2192 Mark as Date Table).<\/p>\n<p><strong>Create relationships:<\/strong><\/p>\n<ul>\n<li>DateTable[Date] \u2192 GA4[Date] (1:many)<\/li>\n<li>DateTable[Date] \u2192 GoogleAds[Date] (1:many)<\/li>\n<li>DateTable[Date] \u2192 MetaAds[Date] (1:many)<\/li>\n<li>DateTable[Date] \u2192 CRM_Deals[Close Date] (1:many)<\/li>\n<\/ul>\n<p><strong>Create a Channel mapping table<\/strong> to normalize source names across platforms:<\/p>\n<pre><code class=\"language-\">Channel Mapping:\n<p>Google Ads \u2192 Paid Search<\/p>\n<p>Meta Ads \u2192 Paid Social<\/p>\n<p>Email Campaign \u2192 Email<\/p>\n<p>Organic \u2192 Organic Search<\/p>\n<p>Direct \u2192 Direct<\/p>\n<p>Referral \u2192 Referral<\/code><\/pre>\n<\/p>\n<p>This ensures &#8220;google \/ cpc&#8221; from GA4 and &#8220;Google Ads&#8221; from the ads platform roll up into the same &#8220;Paid Search&#8221; category.<\/p>\n<hr \/>\n<h2>Step 3: Create DAX Measures<\/h2>\n<p>Build these core marketing measures:<\/p>\n<pre><code class=\"language-dax\">\/\/ Spend\n<p>Total Spend = SUM(GoogleAds[Cost]) + SUM(MetaAds[Spend])<\/p>\n\n<p>\/\/ Revenue<\/p>\n<p>Total Revenue = SUM(CRM_Deals[Amount])<\/p>\n\n<p>\/\/ ROAS (Return on Ad Spend)<\/p>\n<p>ROAS = DIVIDE([Total Revenue], [Total Spend], 0)<\/p>\n\n<p>\/\/ Cost Per Acquisition<\/p>\n<p>CPA = DIVIDE([Total Spend], COUNTROWS(FILTER(CRM_Deals, CRM_Deals[Stage] = \"Closed Won\")), 0)<\/p>\n\n<p>\/\/ Conversion Rate (Website)<\/p>\n<p>Website CVR = DIVIDE(SUM(GA4[Conversions]), SUM(GA4[Sessions]), 0)<\/p>\n\n<p>\/\/ Cost Per Click (Blended)<\/p>\n<p>Blended CPC = DIVIDE([Total Spend], SUM(GoogleAds[Clicks]) + SUM(MetaAds[Clicks]), 0)<\/p>\n\n<p>\/\/ Email Open Rate<\/p>\n<p>Email Open Rate = DIVIDE(SUM(Email[Opens]), SUM(Email[Recipients]), 0)<\/p>\n\n<p>\/\/ MoM Growth<\/p>\n<p>Revenue MoM =<\/p>\n<p>VAR CurrentMonth = [Total Revenue]\n<p>VAR PreviousMonth = CALCULATE([Total Revenue], DATEADD(DateTable[Date], -1, MONTH))<\/p>\n<p>RETURN DIVIDE(CurrentMonth - PreviousMonth, PreviousMonth, 0)<\/p>\n\n<p>\/\/ Pipeline Value<\/p>\n<p>Pipeline Value =<\/p>\n<p>CALCULATE(SUM(CRM_Deals[Amount]), CRM_Deals[Stage] <> \"Closed Won\", CRM_Deals[Stage] <> \"Closed Lost\")<\/p>\n\n<p>\/\/ Customer Acquisition Cost<\/p>\n<p>CAC = DIVIDE([Total Spend], COUNTROWS(FILTER(CRM_Contacts, CRM_Contacts[Lifecycle Stage] = \"Customer\")), 0)<\/code><\/pre>\n<\/p>\n<hr \/>\n<h2>Step 4: Design the Dashboard Layout<\/h2>\n<p>Create a new report page. Set the canvas size to 1920\u00d71080 (View \u2192 Page Size \u2192 Custom).<\/p>\n<p><strong>Row 1 \u2014 KPI Cards (top strip):<\/strong><\/p>\n<p>Add 5-6 Card visuals across the top:<\/p>\n<ul>\n<li>Total Spend (formatted as currency)<\/li>\n<li>Total Revenue (formatted as currency)<\/li>\n<li>ROAS (formatted as decimal with &#8220;x&#8221; suffix)<\/li>\n<li>Conversions (whole number)<\/li>\n<li>CPA (formatted as currency)<\/li>\n<li>Revenue MoM Growth (formatted as percentage with conditional color)<\/li>\n<\/ul>\n<p>For conditional formatting on MoM Growth: select the card \u2192 Format \u2192 Conditional formatting \u2192 Font color \u2192 Rules \u2192 positive = green, negative = red.<\/p>\n<p><strong>Row 2 \u2014 Trend Charts:<\/strong><\/p>\n<p><strong>Left: Revenue &#038; Spend Over Time (combo chart)<\/strong><\/p>\n<ul>\n<li>X-axis: DateTable[Month]<\/li>\n<li>Column values: [Total Spend]<\/li>\n<li>Line values: [Total Revenue]<\/li>\n<li>This shows the relationship between investment and return over time<\/li>\n<\/ul>\n<p><strong>Right: Traffic by Channel (stacked area chart)<\/strong><\/p>\n<ul>\n<li>X-axis: DateTable[Month]<\/li>\n<li>Values: SUM(GA4[Sessions])<\/li>\n<li>Legend: Channel (from your mapping table)<\/li>\n<li>Shows which channels are growing or declining<\/li>\n<\/ul>\n<p><strong>Row 3 \u2014 Channel Performance:<\/strong><\/p>\n<p><strong>Left: Channel Breakdown Table<\/strong><\/p>\n<p>Create a matrix visual:<\/p>\n<ul>\n<li>Rows: Channel<\/li>\n<li>Values: Spend, Revenue, ROAS, CPA, Conversions, CVR<\/li>\n<li>Add conditional formatting (data bars for Revenue, color scale for ROAS)<\/li>\n<li>Sort by Revenue descending<\/li>\n<\/ul>\n<p><strong>Right: Campaign Performance (detailed table)<\/strong><\/p>\n<ul>\n<li>Rows: Campaign Name<\/li>\n<li>Values: Spend, Clicks, CPC, Conversions, CPA, Revenue, ROAS<\/li>\n<li>Add row-level conditional formatting<\/li>\n<li>Enable sorting on any column<\/li>\n<\/ul>\n<p><strong>Row 4 \u2014 Supporting Metrics:<\/strong><\/p>\n<p><strong>Funnel visualization:<\/strong> Sessions \u2192 Leads \u2192 MQLs \u2192 SQLs \u2192 Customers<\/p>\n<p>Use a Funnel chart visual with your CRM lifecycle stage counts.<\/p>\n<p><strong>Email performance card:<\/strong> Open Rate, Click Rate, Unsubscribe Rate<\/p>\n<p>Small KPI cards grouped together.<\/p>\n<hr \/>\n<h2>Step 5: Add Interactivity<\/h2>\n<p><strong>Date Range Slicer:<\/strong><\/p>\n<p>Add a Date slicer at the top. Set it to &#8220;Between&#8221; mode so users can pick custom ranges. Add quick-select buttons for Last 7 Days, Last 30 Days, Last Quarter, YTD.<\/p>\n<p>You can create a dynamic title that updates with the selected date range:<\/p>\n<pre><code class=\"language-dax\">Dashboard Title = \n<p>\"Marketing Performance: \" &<\/p>\n<p>FORMAT(MIN(DateTable[Date]), \"MMM DD, YYYY\") & \" \u2014 \" &<\/p>\n<p>FORMAT(MAX(DateTable[Date]), \"MMM DD, YYYY\")<\/code><\/pre>\n<\/p>\n<p><strong>Channel Filter:<\/strong><\/p>\n<p>Add a slicer for Channel. Set it to dropdown or tile style. This lets users isolate a single channel&#8217;s performance.<\/p>\n<p><strong>Campaign Filter:<\/strong><\/p>\n<p>Another slicer for Campaign Name. Useful for digging into specific campaign performance.<\/p>\n<p><strong>Cross-filtering:<\/strong><\/p>\n<p>By default, clicking on a channel in the bar chart filters all other visuals on the page. This is automatic in Power BI \u2014 test it to make sure the interactions work as expected. To customize: go to Format \u2192 Edit Interactions.<\/p>\n<p><strong>Drill-through page:<\/strong><\/p>\n<p>Create a second page called &#8220;Campaign Detail.&#8221; Add detailed metrics for a single campaign. On the main page, right-click any campaign \u2192 Drill through \u2192 Campaign Detail. This keeps the main dashboard clean while allowing deep-dives.<\/p>\n<hr \/>\n<h2>Step 6: Format and Polish<\/h2>\n<p><strong>Color scheme:<\/strong> Pick 5-6 colors that match your brand or are visually distinct. Apply them consistently \u2014 same color for the same channel across all charts.<\/p>\n<p><strong>Background:<\/strong> Use a subtle light gray (#F5F5F5) background instead of white. It makes cards and charts pop.<\/p>\n<p><strong>Typography:<\/strong> Use a consistent font. Segoe UI (Power BI default) works well. Make KPI numbers large (24-28pt), labels small (10-12pt).<\/p>\n<p><strong>Borders and spacing:<\/strong> Add thin borders to cards. Leave consistent padding between visuals. Align everything to a grid.<\/p>\n<p><strong>Mobile layout:<\/strong> Go to View \u2192 Mobile Layout and arrange your visuals for phone viewing. Not optional \u2014 executives check dashboards on phones.<\/p>\n<hr \/>\n<h2>Step 7: Publish and Share<\/h2>\n<p><strong>Publish to Power BI Service:<\/strong><\/p>\n<p>Home \u2192 Publish \u2192 select your workspace. The dashboard is now accessible via browser at app.powerbi.com.<\/p>\n<p><strong>Set up scheduled refresh:<\/strong><\/p>\n<p>In Power BI Service \u2192 Dataset Settings \u2192 Scheduled Refresh. Set it to refresh daily (or more frequently if your data sources support it). You&#8217;ll need to configure a gateway if connecting to on-premises data.<\/p>\n<p><strong>Create a dashboard:<\/strong><\/p>\n<p>Pin your key visuals to a Dashboard (different from a Report in Power BI). Dashboards show summary tiles and can include visuals from multiple reports.<\/p>\n<p><strong>Share with your team:<\/strong><\/p>\n<p>Workspace \u2192 Share \u2192 enter email addresses. Or publish as an app for a cleaner experience.<\/p>\n<hr \/>\n<h2>Honest Assessment: Is This the Best Approach?<\/h2>\n<p>A Power BI marketing dashboard is powerful once built. But let&#8217;s be realistic about what it takes:<\/p>\n<p><strong>Building time:<\/strong> 4-8 hours minimum for a first dashboard. Longer if you&#8217;re learning DAX or struggling with data connections (especially Meta Ads, which has no native connector).<\/p>\n<p><strong>Maintenance:<\/strong> When APIs change, connections break. When you add a new ad platform, you need to update the data model, create new measures, and redesign visuals. Plan for 2-4 hours\/month of maintenance.<\/p>\n<p><strong>The DAX barrier:<\/strong> Measures like ROAS and CPA are simple. But time intelligence calculations (year-over-year comparisons, rolling averages, cohort analysis) require intermediate DAX skills. Most marketers don&#8217;t have these \u2014 and shouldn&#8217;t need them.<\/p>\n<p><strong>Data freshness:<\/strong> Scheduled refresh runs 1-8 times per day on Pro. You&#8217;re always looking at slightly stale data. For fast-moving campaigns, this lag matters.<\/p>\n<hr \/>\n<div style=\"background: #f0f7ff; border: 2px solid #2563eb; border-radius: 16px; padding: 36px 40px; margin: 48px 0; position: relative; overflow: hidden;\">\n<div style=\"position: absolute; top: 0; right: 0; width: 200px; height: 200px; background: radial-gradient(circle at top right, rgba(37,99,235,0.08) 0%, transparent 70%); pointer-events: none;\"><\/div>\n<p><span style=\"background: #2563eb; color: #ffffff; display: inline-block; padding: 6px 16px; border-radius: 20px; font-size: 0.85em; font-weight: 700; letter-spacing: 0.5px; text-transform: uppercase; margin-bottom: 16px;\">\u26a1 FASTER ALTERNATIVE<\/span><\/p>\n<div style=\"background: linear-gradient(135deg, #f0f7ff 0%, #e8f4fd 100%); border-left: 5px solid #2563eb; border-radius: 12px; padding: 28px 32px; margin: 16px 0; box-shadow: 0 2px 8px rgba(37, 99, 235, 0.08);\">\n<h3 style=\"margin-top: 0; margin-bottom: 16px; color: #1e40af; font-size: 1.3em;\">\u26a1 Build a Marketing Dashboard in 60 Seconds \u2014 Not 8 Hours<\/h3>\n<p>You just read 7 steps, DAX formulas, data model configuration, and hours of setup. Here&#8217;s the same result in Pulse AI:<\/p>\n<ul style=\"margin: 12px 0;\">\n<li><em>&#8220;Show me a marketing dashboard with spend, revenue, and ROAS by channel&#8221;<\/em><\/li>\n<li><em>&#8220;What&#8217;s our cost per acquisition by campaign this month?&#8221;<\/em><\/li>\n<li><em>&#8220;Compare Google Ads vs Meta Ads performance for the last 90 days&#8221;<\/em><\/li>\n<li><em>&#8220;Which campaigns have the best conversion rate this quarter?&#8221;<\/em><\/li>\n<\/ul>\n<p>Pulse AI connects directly to Google Analytics, Google Ads, HubSpot, and other marketing tools \u2014 no data warehouse, no Power Query, no DAX. Ask a question, get a visualization. The AI handles joins, calculations, and chart selection automatically.<\/p>\n<p><strong>The real comparison:<\/strong><\/p>\n<table style=\"width: 100%; border-collapse: collapse; margin: 12px 0;\">\n<tr style=\"background: #e8f4fd;\">\n<th style=\"padding: 8px; text-align: left;\">Task<\/th>\n<th style=\"padding: 8px; text-align: left;\">Power BI<\/th>\n<th style=\"padding: 8px; text-align: left;\">Pulse AI<\/th>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Connect marketing data<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">1-2 hours (connectors, gateways)<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">5 minutes (OAuth connect)<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Calculate ROAS &#038; CPA<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Write DAX measures<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Ask in English<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Build the dashboard<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">2-4 hours (layout, visuals)<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Instant (auto-generated)<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Add new metric<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Write new DAX, add visual<\/td>\n<td style=\"padding: 8px; border-bottom: 1px solid #e2e8f0;\">Ask a new question<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px;\"><strong>Total time<\/strong><\/td>\n<td style=\"padding: 8px;\"><strong>4-8 hours<\/strong><\/td>\n<td style=\"padding: 8px;\"><strong>Under 10 minutes<\/strong><\/td>\n<\/tr>\n<\/table>\n<p style=\"margin-bottom: 0; margin-top: 20px;\"><a href=\"https:\/\/usepulseai.com\" style=\"display: inline-block; background: #2563eb; color: #ffffff; padding: 12px 28px; border-radius: 8px; text-decoration: none; font-weight: 600; font-size: 1em;\">Try Pulse AI Free \u2192<\/a><\/p>\n<\/div>\n<\/div>\n<h2>FAQ<\/h2>\n<p><strong>What&#8217;s the best data connector for Google Analytics 4 in Power BI?<\/strong><\/p>\n<p>The native GA4 connector works but is slow with large date ranges and limited in dimension\/metric combinations per query. For better performance, export GA4 data to BigQuery (free for most volumes) and connect Power BI to BigQuery instead.<\/p>\n<p><strong>Can Power BI connect directly to Meta Ads?<\/strong><\/p>\n<p>Not natively. You need a third-party connector (Supermetrics, Funnel.io, Windsor.ai) or export CSV files manually. This is one of Power BI&#8217;s biggest gaps for marketing dashboards.<\/p>\n<p><strong>How often should a marketing dashboard refresh?<\/strong><\/p>\n<p>Daily is the minimum for most marketing teams. If you&#8217;re running high-spend campaigns, twice daily or more. Power BI Pro supports up to 8 refreshes\/day. Premium supports 48\/day.<\/p>\n<p><strong>Should I use Power BI for marketing analytics or a dedicated marketing tool?<\/strong><\/p>\n<p>Power BI is great if you need custom analysis across multiple data sources. But if you mainly want to see &#8220;how are my campaigns doing&#8221; without building a data model, tools like Pulse AI give you instant answers without the setup.<\/p>\n<p><strong>Can I embed the Power BI dashboard in our website or Slack?<\/strong><\/p>\n<p>Yes. Power BI supports iframe embedding (with proper licensing) and has a Slack integration for scheduled dashboard screenshots. You can also use Power BI&#8217;s REST API to embed visuals in custom applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Complete step-by-step guide to building a marketing dashboard in Power BI \u2014 from connecting Google Analytics and ad platforms to creating DAX measures for ROAS, CPA, and conversion tracking.<\/p>\n","protected":false},"author":1,"featured_media":463,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[13,4,5],"tags":[103,100,133,101,83,23,134],"class_list":["post-464","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence","category-data-analytics","category-guides","tag-dax","tag-google-analytics","tag-marketing","tag-marketing-dashboard","tag-power-bi","tag-pulse-ai","tag-roas"],"_links":{"self":[{"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/posts\/464","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/comments?post=464"}],"version-history":[{"count":0,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/posts\/464\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/media\/463"}],"wp:attachment":[{"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/media?parent=464"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/categories?post=464"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/usepulseai.com\/blog\/wp-json\/wp\/v2\/tags?post=464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}