{"id":11449,"date":"2026-07-16T09:01:26","date_gmt":"2026-07-16T09:01:26","guid":{"rendered":"https:\/\/phantombuster.com\/blog\/?p=11449"},"modified":"2026-07-16T09:01:26","modified_gmt":"2026-07-16T09:01:26","slug":"phantombuster-workflows","status":"publish","type":"post","link":"https:\/\/phantombuster.com\/blog\/tools\/phantombuster-workflows\/","title":{"rendered":"PhantomBuster Workflows: How to Chain Automations Into End-to-End Pipelines"},"content":{"rendered":"<p>If your team still passes CSVs between PhantomBuster automations (formerly called Phantoms), checks runs manually, and hopes the next step starts at the right time, you don&#8217;t have a pipeline. You have a process that breaks as soon as volume, teammates, or timing changes.<\/p>\n<p>PhantomBuster Workflows orchestrate sequencing, data handoffs, pacing, branching, and error handling so multi-step automations run the same way every time\u2014without manual CSVs or timing guesswork.<\/p>\n<p>This article covers the principles that make Workflows reliable, then walks through three pipeline recipes with implementation notes. The goal isn&#8217;t maximum automation. The goal is automation you can deploy, monitor, and\u00a0scale without creating new problems.<\/p>\n<h2>What are Workflows and what do they replace?<\/h2>\n<h3>What&#8217;s the difference between an automation and a Workflow?<\/h3>\n<p>A PhantomBuster automation does one job, like exporting a LinkedIn search, extracting profile data, or sending connection requests. You configure it, run it, and it produces output.<\/p>\n<p>A Workflow is the orchestration layer. It sequences multiple automations, passes data between steps, adds delays and conditions, and helps you handle failures.<\/p>\n<p>This distinction matters because most automation failures come from handoffs. When you chain automations manually, you own every dependency. When you run a Workflow, the platform enforces sequencing and data transfer, reducing reliance on someone remembering the next step.<\/p>\n<h3>Why manual chaining creates operational debt<\/h3>\n<p>The manual pattern usually looks like this: run automation A, download a file, upload it to automation B, then schedule both and hope the timing stays aligned. While acceptable for one-off experiments, this method fails at weekly scale. Manual chaining creates predictable failure modes:<\/p>\n<ul>\n<li><strong>Timing mismatches:<\/strong> Step B starts before Step A finishes, so you process incomplete data or fail because the expected output isn&#8217;t ready.<\/li>\n<li><strong>File overwrites and reprocessing:<\/strong> Reusing or renaming files can restart processing from scratch when you lose track of what has already been handled.<\/li>\n<li><strong>No visibility into handoffs:<\/strong> One step completes, the next never runs, and you only notice when downstream systems show gaps.<\/li>\n<li><strong>Duplicate accumulation:<\/strong> Without deduplication between steps, the same lead can enter the system more than once, creating duplicates in the CRM.<\/li>\n<\/ul>\n<h3>Pre-built Workflows or custom pipelines?<\/h3>\n<p>PhantomBuster includes pre-built Workflows for common sequences, like LinkedIn search to outreach, engagement to connection request, or profile data extraction to CRM sync.<\/p>\n<p>You configure these end-to-end in the Workflow builder\u2014triggers, steps, pacing, and error handling\u2014so the whole sequence runs as one unit. Pre-built Workflows are a good fit when your process matches a standard pattern. Many ops teams, though, need custom logic, like routing by job title, skipping outreach when enrichment fails, or syncing only qualified leads to the CRM.<\/p>\n<p>The practical rule is simple: use pre-built when the pattern fits, go custom when your process needs branching, extra checks, or tighter control over what moves downstream.<\/p>\n<h2>What a reliable workflow needs to include<\/h2>\n<h3>Triggers and inputs: Where does the pipeline start?<\/h3>\n<p>Every Workflow starts with a trigger and an input. Common sources include a schedule, a webhook, a Google Sheet, a CSV upload, or the output of another PhantomBuster automation. Your pipeline is only as good as its inputs\u2014most failures trace back to stale data or loose filters.<\/p>\n<p>If you use Google Sheets, treat the sheet like an interface contract. Share permissions must be correct, and column headers need to match what downstream steps expect. If a column name doesn&#8217;t match a message placeholder, you&#8217;ll get broken personalization or missing fields.<\/p>\n<p>Before you scale any Workflow, validate the input format and a small sample of\u00a0records.<\/p>\n<h3>Step order: How should you sequence based on dependencies?<\/h3>\n<p>Workflows run steps in sequence. Each step waits for the previous one to finish. That removes the timing problems you get when you schedule separate automations and hope they line up. Order should follow dependency:<\/p>\n<ol>\n<li>Extraction<\/li>\n<li>Enrichment<\/li>\n<li>Qualification and deduplication<\/li>\n<li>CRM sync<\/li>\n<li>LinkedIn actions<\/li>\n<li>Follow-up logic<\/li>\n<\/ol>\n<p>If you need a field for personalization, you can&#8217;t put messaging before the enrichment step that produces it. Build in layers so each stage (collection \u2192 enrichment \u2192 sync \u2192 outreach) is stable before you add the next.<\/p>\n<blockquote><p>&#8220;Layer your workflows first. Scale only after the system is stable.&#8221; \u2014 PhantomBuster Product Expert, <a href=\"https:\/\/www.linkedin.com\/in\/brianejmoran\/\" target=\"_blank\" rel=\"noopener\">Brian Moran<\/a><\/p><\/blockquote>\n<h3>Delays and pacing: How do you build consistent throughput?<\/h3>\n<p>When designing a workflow, include delays between the steps. They aren&#8217;t just cosmetic. They should align with platform limits and account activity history. What often creates risk is a pattern change, not a specific number.<\/p>\n<p>If an account is quiet for weeks and then suddenly runs a large batch in a short window, that shift stands out. Delays help you avoid &#8220;slide and spike&#8221; behavior by spreading activity over time.<\/p>\n<p>There isn&#8217;t one universal pacing setting. Long-active accounts handle steadier activity than dormant accounts; increase volumes only after several clean cycles. Design pacing around the baseline pattern of the account, then increase gradually once you&#8217;ve confirmed clean runs over multiple cycles.<\/p>\n<blockquote><p>&#8220;Avoid slide and spike patterns. Gradual ramps outperform sudden jumps.&#8221; \u2014 PhantomBuster Product Expert, <a href=\"https:\/\/www.linkedin.com\/in\/brianejmoran\/\" target=\"_blank\" rel=\"noopener\">Brian Moran<\/a><\/p><\/blockquote>\n<h3>Conditional logic: When should the pipeline branch?<\/h3>\n<p>Conditional branches let you route records based on data. Common examples include skipping outreach when enrichment fails, using different sequences by job title, or stopping follow-ups once a prospect replies.<\/p>\n<p>Without branching, every record follows the same path. That&#8217;s how you waste LinkedIn actions on people who are already connections, or send follow-ups to someone who already responded.<\/p>\n<p>Conditions are also easy to misconfigure. Missing columns, the wrong operator, or unexpected values can route leads to the wrong branch without obvious errors. Test branching with a small batch before you scale.<\/p>\n<h3>Errors: How to recover and keep runs moving<\/h3>\n<p>Failures happen in real pipelines. Cookies expire, LinkedIn returns temporary errors, enrichment providers time out, and data formats drift. The important question is what your Workflow does next.<\/p>\n<p>A Workflow that stops on the first error is brittle. A Workflow that logs failures, skips affected records when appropriate, and continues processing the rest is more resilient. For LinkedIn-facing steps, a common issue is session friction, cookie expiry, forced re-authentication, or disconnects.<\/p>\n<p>Treat those as early signals that the session needs attention. Pause the Workflow, refresh authentication, review recent pacing changes, then resume calmly.<\/p>\n<blockquote><p>&#8220;Session friction is often an early warning, not an automatic ban.&#8221; \u2014 PhantomBuster Product Expert, <a href=\"https:\/\/www.linkedin.com\/in\/brianejmoran\/\" target=\"_blank\" rel=\"noopener\">Brian Moran<\/a><\/p><\/blockquote>\n<h3>Destinations: Where does workflow output go?<\/h3>\n<p>Destinations are your final checkpoint. They decide where results land\u2014PhantomBuster Leads, your CRM (HubSpot\/Salesforce), a webhook, or a CSV\/JSON export\u2014so downstream systems stay in sync. If you sync to a CRM, field mapping needs to be correct. If the output column doesn&#8217;t map to the intended CRM field, data lands in the wrong place or gets dropped.<\/p>\n<h2>Recipe 1: Clean data pipeline, extraction to enrichment to CRM sync<\/h2>\n<h3>What this pipeline does<\/h3>\n<p>This pipeline pulls leads from a Sales Navigator search, enriches them, deduplicates, and syncs them to your CRM without manual CSV handling. It&#8217;s a solid base for any outbound system because it provides consistent CRM data.<\/p>\n<h3>Step-by-step workflow design<\/h3>\n<p>Build this as a single PhantomBuster Workflow configured in the Workflow builder:<\/p>\n<ol>\n<li><strong>Sales Navigator Search Export (PhantomBuster automation)<br \/>\n<\/strong>Extract leads from a saved search or search URL. Run on a schedule to capture new results as they appear.<br \/>\nSend output to PhantomBuster Leads for built-in deduplication, so the same LinkedIn URL doesn&#8217;t create duplicates across runs.<\/li>\n<li><strong>LinkedIn Profile Scraper (PhantomBuster automation)<\/strong><br \/>\nExtract additional profile fields such as current company, job title, location, and experience. If you use email discovery, treat it as enrichment, not a gate for outreach.<br \/>\nDecide up front what you do with records that come back without email, for example sync them with a lower status, or route them to review.<\/li>\n<li><strong>HubSpot Contact Sender (or your CRM sync automation)<\/strong><br \/>\nAdd this as the final step in the same Workflow. Sync enriched leads to your CRM with field mapping you&#8217;ve validated. Use a stable identifier, typically the LinkedIn profile URL, to update existing records instead of creating duplicates.<br \/>\nFor a deeper look at how this handoff works end-to-end, see <a href=\"https:\/\/phantombuster.com\/blog\/pipeline-management\/linkedin-to-crm-workflow-phantombuster\/\">LinkedIn to CRM workflow with PhantomBuster<\/a>.Test with a small batch first, then verify in the CRM that contacts were created or updated as expected.<\/li>\n<\/ol>\n<h3>Failure points and checkpoints<\/h3>\n<ul>\n<li><strong>Input quality:<\/strong> If your Sales Navigator search is too broad, you&#8217;ll automate irrelevant leads. Validate filters before you scale.<\/li>\n<li><strong>Enrichment gaps:<\/strong> Some profiles won&#8217;t return all fields or an email. Decide whether to sync incomplete records or route them to a separate queue to avoid clutter.<\/li>\n<li><strong>CRM duplicates:<\/strong> Duplicates almost always come from mismatched identifiers or field mapping. Fix mapping, then re-sync only the affected batch.<\/li>\n<\/ul>\n<div class=\"wp-layout-normal-table\"><table style=\"min-width: 100px;\">\n<colgroup>\n<col style=\"min-width: 25px;\" \/>\n<col style=\"min-width: 25px;\" \/>\n<col style=\"min-width: 25px;\" \/>\n<col style=\"min-width: 25px;\" \/><\/colgroup>\n<tbody>\n<tr>\n<td colspan=\"1\" rowspan=\"1\"><strong>Step<\/strong><\/td>\n<td colspan=\"1\" rowspan=\"1\"><strong>Success criteria<\/strong><\/td>\n<td colspan=\"1\" rowspan=\"1\"><strong>Failure signal<\/strong><\/td>\n<td colspan=\"1\" rowspan=\"1\"><strong>Recovery action<\/strong><\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Sales Navigator export<\/td>\n<td colspan=\"1\" rowspan=\"1\">Leads match ICP filters<\/td>\n<td colspan=\"1\" rowspan=\"1\">Irrelevant profiles in output<\/td>\n<td colspan=\"1\" rowspan=\"1\">Refine search filters, re-run<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Profile enrichment<\/td>\n<td colspan=\"1\" rowspan=\"1\">Key fields populated<\/td>\n<td colspan=\"1\" rowspan=\"1\">Missing required fields<\/td>\n<td colspan=\"1\" rowspan=\"1\">Adjust inputs, route incomplete records to review<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">CRM sync<\/td>\n<td colspan=\"1\" rowspan=\"1\">Records created or updated without duplicates<\/td>\n<td colspan=\"1\" rowspan=\"1\">Duplicate records appear<\/td>\n<td colspan=\"1\" rowspan=\"1\">Fix identifier and mapping, re-sync affected records<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n<h2>Recipe 2: Engagement to outreach pipeline, post engagers to enrichment to connect to message<\/h2>\n<h3>What this pipeline does<\/h3>\n<p>This pipeline captures people who engaged with a post, enriches and qualifies them, sends connection requests with personalized notes, then follows up after acceptance. It also includes stop-on-reply logic so you don&#8217;t keep messaging someone who has already responded.<\/p>\n<h3>Step-by-step workflow design<\/h3>\n<p>Build this as a single PhantomBuster Workflow: Post Engagers \u2192 Profile Data \u2192 Connect\/Message (with acceptance and stop-on-reply conditions).<\/p>\n<ol>\n<li><strong>LinkedIn Post Likers Export or LinkedIn Post Commenters Export (PhantomBuster automation)<\/strong><br \/>\nExtract profiles of people who liked or commented on a post. Use PhantomBuster&#8217;s Watcher Mode (where available) so repeated runs capture new engagers without reprocessing the same people.<\/li>\n<li><strong>LinkedIn Profile Scraper (PhantomBuster automation)<\/strong><br \/>\nExtract profile fields you need for qualification, like job title, company, industry, and location. Use those fields to filter against your ICP, because not every engager is a good lead.<\/li>\n<li><strong>LinkedIn Auto Connect or PhantomBuster Outreach Flow<\/strong><br \/>\nAdd one step in the Workflow to send invites to qualified leads. Keep volume aligned with the account&#8217;s recent history. For many established accounts, a conservative start looks like 15 to 25 invites per weekday, then gradual increases after clean cycles.<\/li>\n<li><strong>Conditional step: Send messages only after acceptance<\/strong><br \/>\nAdd a Conditional step in PhantomBuster Workflows to send messages only after acceptance (or use Outreach Flow with an acceptance condition). This keeps your sequence aligned with how LinkedIn messaging works in practice.<\/li>\n<li><strong>LinkedIn Message Sender (PhantomBuster automation): Follow-up with stop-on-reply<\/strong><br \/>\nSend a follow-up message to accepted connections. Enable Stop-on-Reply so responses route back to a rep and the Workflow halts messaging for that lead.<\/li>\n<\/ol>\n<h3>Failure points and checkpoints<\/h3>\n<ul>\n<li><strong>Engagement volume vs invite capacity:<\/strong> A high-performing post can generate more engagers than you can connect with in a week. Use filters or a queue so you don&#8217;t build an unmanageable backlog.<\/li>\n<li><strong>Acceptance delays:<\/strong> People accept on their own timeline. Your system needs to handle acceptance asynchronously instead of assuming a same-day response.<\/li>\n<li><strong>Template errors:<\/strong> If your message uses placeholders that aren&#8217;t present for every record, you&#8217;ll send broken personalization. Test templates with a small batch and include a fallback when a field is missing.<\/li>\n<\/ul>\n<h2>Recipe 3: Job change signal pipeline, detect change to re-enrich to targeted outreach<\/h2>\n<h3>What this pipeline does<\/h3>\n<p>This pipeline detects when existing contacts change roles, refreshes their data, then triggers <a href=\"https:\/\/phantombuster.com\/blog\/ai-automation\/ai-triggered-outreach\/\">targeted outreach<\/a> that acknowledges the transition. It also updates your CRM, even if you decide not to message.<\/p>\n<p>Job changes are often useful timing signals, but teams miss them because they rely on manual checks or stale CRM records. This workflow makes the signal operational without turning it into real-time monitoring.<\/p>\n<h3>Step-by-step workflow design<\/h3>\n<ol>\n<li><strong>HubSpot Contact Career Tracker (PhantomBuster automation)<\/strong><br \/>\nMonitor existing contacts for job changes on a regular schedule; if you use another CRM, add the equivalent PhantomBuster CRM change-detection step. The automation flags contacts as no longer working at the company and creates a new contact with the new job details.<\/li>\n<li><strong>Conditional branch: Route by change type<\/strong>\n<ul>\n<li><strong>Promotion inside the same company:<\/strong> Send a short check-in message.<\/li>\n<li><strong>Move to a new company:<\/strong> Reference the transition and reconnect contextually.<\/li>\n<li><strong>Move outside your ICP:<\/strong> Skip outreach, update CRM only.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Delay<\/strong><br \/>\nAdd a delay so you don&#8217;t message immediately after the profile changes. Outreach that lands hours after an update can feel overly monitored. Use a 1\u20132 week delay for job-change outreach unless your motion requires earlier contact; this avoids looking hyper-reactive.<\/li>\n<li><strong>LinkedIn Message Sender or LinkedIn Auto Connect (PhantomBuster automation)<\/strong><br \/>\nSend a message based on the branch. Keep it specific and light. The intent is to reopen a relationship, not to force a pitch.<\/li>\n<li><strong>CRM update step<\/strong><br \/>\nSync updated profile data back to the CRM so reps work from current information, whether or not outreach converts.<\/li>\n<\/ol>\n<h3>Failure points and checkpoints<\/h3>\n<ul>\n<li><strong>False positives:<\/strong> Not every profile edit is a job change. Title formatting changes and company rebrands can trigger noise. Consider routing first to a review queue, or only triggering outreach when the change is clear.<\/li>\n<li><strong>Timing sensitivity:<\/strong> Too soon can feel intrusive, too late loses relevance. Use a delay that fits your audience and sales cycle.<\/li>\n<li><strong>CRM automation conflicts:<\/strong> CRM workflows may trigger on company or title changes. Align with your CRM admin before deployment so you don&#8217;t fire unrelated emails or tasks.<\/li>\n<\/ul>\n<h2>Operational rollout: How to deploy workflows without creating new problems<\/h2>\n<h3>When\u00a0is a Workflow the right choice vs. a standalone automation?<\/h3>\n<p>Use a standalone automation for true one-offs, like a single export or a quick experiment to validate a source. Use a Workflow for recurring, multi-step tasks that need reliable handoffs and downstream sync.<\/p>\n<h3>How to test in layers before you scale<\/h3>\n<ol>\n<li><strong>Start with step one only:<\/strong> Run a small batch, usually 10 to 20 records, and verify columns, values, and output destinations before adding anything else.<\/li>\n<li><strong>Add one step at a time:<\/strong> After each addition, confirm that data passes correctly, delays behave as expected, and conditions route records to the right path.<\/li>\n<li><strong>Only increase volume after the full pipeline has run cleanly for multiple cycles:<\/strong> What works at 20 records can fail at 200 due to rate limits, session friction, or unexpected data variation.<\/li>\n<\/ol>\n<h3>Monitoring and maintenance: What to watch after launch<\/h3>\n<ul>\n<li><strong>Check Workflow run history regularly during the first weeks:<\/strong> Look for steps that complete with no output, unexpected volumes, or records that get stuck.<\/li>\n<li><strong>Enable PhantomBuster notifications for session status: <\/strong>When you see session friction, pause the Workflow, refresh authentication, and resume after reviewing pacing.\u00a0You can also route alerts via your monitoring tool to catch disconnects early.<\/li>\n<li><strong>Review output quality, not just step success:<\/strong> A workflow can run &#8220;green&#8221; while producing incomplete, duplicated, or irrelevant data in the CRM.<\/li>\n<\/ul>\n<h3>How to avoid the set and forget trap<\/h3>\n<p>Workflows aren&#8217;t autonomous systems you can ignore. They&#8217;re operational infrastructure, and they need review cycles like any other system. Platforms change, data quality varies, and your own requirements evolve.<\/p>\n<p>Plan periodic audits of inputs, mappings, templates, and pacing, especially after you change your ICP or your outbound motion. If you&#8217;re managing automations across a <a href=\"https:\/\/phantombuster.com\/blog\/linkedin-automation\/manage-automation-distributed-sales-team\/\">distributed sales team<\/a>, this kind of structured review cadence becomes even more critical.<\/p>\n<blockquote><p><strong>Note:<\/strong> The goal isn&#8217;t maximum automation. It&#8217;s reliable automation that stays aligned with your data quality and platform constraints.<\/p><\/blockquote>\n<h2>Conclusion<\/h2>\n<p>Workflows turn disconnected automations into one pipeline\u2014data moves step-to-step with built-in handoffs, pacing, branching, and recovery. They provide repeatable systems for sales ops teams. The goal is a pipeline that holds up when inputs drift, sessions expire, and downstream tools enforce strict mapping rules.<\/p>\n<p>Start with one pipeline, build it in layers, test with a small batch, then scale once you&#8217;ve seen clean cycles. <a href=\"https:\/\/phantombuster.com\/signup\" target=\"_blank\" rel=\"noopener\">Start your free trial<\/a> to build your first Workflow.<\/p>\n<h2>FAQ<\/h2>\n<h3>What is the difference between an automation and a Workflow?<\/h3>\n<p>An automation performs one task. A Workflow sequences multiple PhantomBuster automations, passes data between steps, adds delays and conditions, and handles failures for repeatable runs.<\/p>\n<h3>Can I build custom workflows, or do I have to use pre-built ones?<\/h3>\n<p>You can do both. Pre-built Workflows cover common patterns. Custom Workflows let you define your own step order, triggers, delays, and branching so the pipeline matches how your team actually operates.<\/p>\n<h3>How do I reduce the risk of LinkedIn restrictions when I automate steps inside a workflow?<\/h3>\n<p>Design for consistency. Add delays between LinkedIn-facing actions, keep daily volumes aligned with the account&#8217;s recent history, and ramp up only after clean low-volume runs. Most risk comes from abrupt pattern changes and repeated failures, not from &#8220;one magic number.&#8221;<\/p>\n<h3>What happens if a workflow step fails?<\/h3>\n<p>Configure step-level error handling: stop on critical failures, skip bad records, or route to review. Choose per step based on downstream impact. Some failures should stop the run, others should skip the affected record and continue, and some should route to a review queue.<\/p>\n<h3>How do I know if my workflow is working reliably?<\/h3>\n<p>Check run history, then verify outputs in the destination systems. Look for mapping accuracy, duplication rate, and data completeness, not just &#8220;step succeeded.&#8221; For LinkedIn-facing steps, treat session friction alerts as an operational signal to pause and stabilize.<\/p>\n<h3>Why should LinkedIn actions sit later in the pipeline, after enrichment and qualification?<\/h3>\n<p>Put LinkedIn actions last so you only act on records you actually want. This protects scarce LinkedIn actions, reduces template breakage, and keeps CRM sync clean.\u00a0Enrichment and qualification first means fewer wasted actions\u00a0and better personalization.<\/p>\n<h3>What should I do if a workflow hits cookie expiry, forced logout, or repeated re-authentication prompts?<\/h3>\n<p>Treat it as session friction and pause. Refresh the session, review any recent changes in volume or timing, then resume at a calmer cadence. Avoid letting a Workflow retry aggressively, because repeated failed attempts can make recovery harder.<\/p>\n<h3>How do I prevent CRM duplicates and bad field mapping when I sync workflow outputs?<\/h3>\n<p>Deduplicate early and test end-to-end with a small batch. Use a stable identifier, usually LinkedIn profile URL, to update existing records instead of creating new ones. Keep column headers consistent across steps so mapping does not drift.<\/p>\n<h3>If actions look blocked, how do I tell platform enforcement from workflow execution failure?<\/h3>\n<p>Run a manual parity check. Try the same action in LinkedIn manually from the same account. If it works manually, you&#8217;re likely dealing with execution issues like session disconnects, UI surface variance, or template data problems.<\/p>\n<p>Check <a href=\"https:\/\/phantombuster.com\/blog\/tools\/phantombuster-features-explained\/\">PhantomBuster session status<\/a> and recent run logs; if manual actions fail too, slow your cadence and stabilize the account pattern.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PhantomBuster Workflows guide: chain Phantoms into reliable pipelines with sequencing, pacing, branching and error handling, plus 3 recipes for CRM and LinkedIn.&#8221;<\/p>\n","protected":false},"author":8,"featured_media":13037,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[46],"tags":[34],"class_list":["post-11449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tools","tag-automation"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - 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