{"id":11958,"date":"2026-03-27T10:06:18","date_gmt":"2026-03-27T10:06:18","guid":{"rendered":"https:\/\/saleshiker.com\/blog\/?p=11958"},"modified":"2026-03-30T12:13:43","modified_gmt":"2026-03-30T12:13:43","slug":"sales-playbooks-powered-by-machine-learning","status":"publish","type":"post","link":"https:\/\/saleshiker.com\/blog\/sales-playbooks-powered-by-machine-learning\/","title":{"rendered":"Sales Playbooks Powered by Machine Learning"},"content":{"rendered":"\n<p>Sales playbooks were once just PDFs that were rarely read.<\/p>\n\n\n\n<p>In 2026, the best teams are evolving those static documents into living, learning systems driven by machine learning and <a href=\"https:\/\/saleshiker.com\/\" target=\"_blank\" rel=\"noopener\" title=\"\">AI<\/a>.<\/p>\n\n\n\n<p>Machine learning is not just about &#8220;doing things faster.\u201d<\/p>\n\n\n\n<p>And it changes the way you build, refresh, and execute your sales motion\u2014every call, email, and deal is now data that makes the next one better. In this blog, we\u2019ll introduce what a\u2002machine-learning-driven sales playbook is, why it\u2019s important, and how you can apply it in the real world.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is a Machine\u2011Learning Powered Sales Playbook?<\/strong><\/h2>\n\n\n\n<p>A classic sales playbook is a manual written by humans: ideal customer profile, messaging, stages, qualification, objection handling, templates, and KPIs.<\/p>\n\n\n\n<p>It is revised from time to time, usually after a quarter review or a big strategy shift.&nbsp;<\/p>\n\n\n\n<p><strong>Guided by Machine learning, a playbook evolves in three major areas:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>It\u2019s data-driven:<\/strong>\u2002the recommendations are based on historical conversions, win\/loss data, and behavior patterns \u2014 not just opinion.<\/li>\n\n\n\n<li><strong>It is dynamic:<\/strong> as new data comes in, often in real time, the content, steps, and recommendations are updated.<\/li>\n\n\n\n<li><strong>It\u2019s native:<\/strong> the guidance shows up in the tools reps are already using (CRM, email,\u2002dialer, revenue platform).<\/li>\n<\/ul>\n\n\n\n<p>A simple example: instead of a generic \u201cDiscovery Call Script,\u201d a rep sees a dynamic checklist and <strong>talking points tuned to:&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>This account industry and size\u2002<\/li>\n\n\n\n<li>The prospect&#8217;s role and previous interaction<\/li>\n\n\n\n<li>What has worked best in similar deals historically&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>So the \u201cplaybook\u201d is actually a playbook, like in sports, but with your methodology presented in the form of a recommendation engine.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Key Ways Machine Learning Transforms Sales Playbooks<\/strong><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>1. Lead and Account Prioritization<\/strong><\/h4>\n\n\n\n<p>Machine learning models are able to rank leads and accounts by their probability of buying, based on\u2002dozens or even hundreds of signals that include firmographics, technographics, website behavior, past outreach, and product usage.<\/p>\n\n\n\n<p>This changes the playbook away from \u201cCall all MQLs this way\u201d to \u201cHere is the subset of leads going this way, in this order, with the best next action for each.\u201d<\/p>\n\n\n\n<p><strong>Advantages:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Improved quality of the pipeline with the same or less amount of work<\/li>\n\n\n\n<li>Quicker matching of the right opportunities to the right reps<\/li>\n\n\n\n<li>SDRs and AEs get a clear prioritization, so they focus their time where it matters most<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. Next\u2011Best\u2011Action Recommendations<\/strong><\/h4>\n\n\n\n<p>Instead of a static flowchart, machine learning can surface the next best action at each stage of the sales cycle.<\/p>\n\n\n\n<p><strong>Examples:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>After a first call, recommend a customized case study that historically increases second-meeting rates with this buyer persona.<\/li>\n\n\n\n<li>After a\u2002conversation about pricing, recommend an ROI calculator or proof-of-concept template that has boosted close rates in like circumstances.<\/li>\n\n\n\n<li>After a lull, pick the re-engagement email version performing best with similar sleeping deals.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>The \u201cplay\u201d is a series of context-sensitive nudges rather than a broad checklist.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Hyper\u2011Personalized Messaging and Content<\/strong><\/h4>\n\n\n\n<p><strong>Your data and rules transparent, generative models can now compose:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Email subject lines tailored for this prospect role and previous engagement<\/li>\n\n\n\n<li>Call openers that mention recent industry news or usage of the product<\/li>\n\n\n\n<li>Follow-up recaps and proposals based on the very pain points discussed<\/li>\n<\/ul>\n\n\n\n<p>\u201cYou continue to maintain control over the voice and guardrails, but the system does the heavy lifting to scale customization. This is where teams experience great time savings \u2014 and the quality hasn\u2019t been sacrificed.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4. Real\u2011Time Coaching and Call Intelligence<\/strong><\/h4>\n\n\n\n<p>Today&#8217;s conversation-intelligence solutions have the ability to transcribe calls, tag moments (objections, pricing, competitors), and associate patterns\u2002with outcomes.<\/p>\n\n\n\n<p><strong>This feeds into your playbook so you can:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Determine the questions that your top performers ask in discovery and make those standard.<\/li>\n\n\n\n<li>Identify the most common objection patterns and revise your talk tracks and battle cards.<\/li>\n\n\n\n<li>Deliver in-the-moment or post-call coaching cues aligned to your methodology.<\/li>\n<\/ul>\n\n\n\n<p>Specifically, \u201cOver time, the playbook morphs into the \u2018brain\u2019 that captures and disseminates what your best reps do instinctively.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Forecasting and Pipeline Health<\/strong><\/h4>\n\n\n\n<p>ML models can increase the accuracy of forecasts by using more information than just the deal stage and gut feel.<\/p>\n\n\n\n<p>They take into account email cadence, stakeholder involvement, past cycle length, discounting, and the presence of competitors.<\/p>\n\n\n\n<p><strong>Embedded in the playbook, this means:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Different plays for dealing with \u201chigh-risk\u201d signals. (e.g., single\u2011threaded, long silence)<\/li>\n\n\n\n<li>Clear early\u2011warning flags for managers so they can intervene.<\/li>\n\n\n\n<li>Sales, marketing, and finance are aligned better around realistic numbers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Components of a Machine\u2011Learning Sales Playbook<\/strong><\/h3>\n\n\n\n<p>To make this concrete, here are the core sections you\u2019ll want to design or redesign with machine learning in mind.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>1. Data Foundations<\/strong><\/h4>\n\n\n\n<p>Your playbook can only be as effective as the data behind it.<\/p>\n\n\n\n<p><strong>Before you layer on fancy models, you have to have :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Clean CRM data:<\/strong> consistent fields for stages, reasons, sources, and contact roles.<\/li>\n\n\n\n<li><strong>Integrated Systems:<\/strong> marketing automation, product analytics, customer success tools.<\/li>\n\n\n\n<li><strong>Standard Activities:<\/strong> Calls, emails, meetings, and notes all have to be logged\u2014structurally.<\/li>\n<\/ul>\n\n\n\n<p>This may not be the most exciting part, but it&#8217;s absolutely critical. \u201cMost\u2002of the failed AI projects are really data-hygiene failures masquerading as AI problems.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. Ideal Customer Profile and Segmentation<\/strong><\/h4>\n\n\n\n<p>Machine learning can take your ICP from high-level generalizations to.<\/p>\n\n\n\n<p>By reviewing historical wins and losses, you can discover micro-segments where you win more often and faster.<\/p>\n\n\n\n<p><strong>Your playbook should include :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Primary ICP segments (e.g., mid\u2011market SaaS, 200\u20131000 employees, North\u2002America)<\/li>\n\n\n\n<li>Data-driven \u201csweet-spot\u201d patterns (e.g., technology stack, funding stage,\u2002growth rate)<\/li>\n\n\n\n<li>Plays customized to each segment: messaging, channels, rhythm, proof\u2002points<\/li>\n<\/ul>\n\n\n\n<p>As the performance data changes, these segments could also\u2002be automatically re-weighted, and the playbook refreshed.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Stage\u2011by\u2011Stage ML\u2011Backed Plays<\/strong><\/h4>\n\n\n\n<p><strong>For each stage in the opportunity, specify:&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Goal of the stage (e.g., \u201cVerify pain and success criteria\u201d)<\/li>\n\n\n\n<li>Actions to take, ordered by expected value (from historical deals)<\/li>\n\n\n\n<li>Content and tools related to suggestions (case studies,\u2002demos, calculators)<\/li>\n\n\n\n<li>ML signals to watch (engagement score thresholds, risk indicators, intent spikes)<\/li>\n<\/ul>\n\n\n\n<p>The result is a step map that adjusts rather than a one\u2011size\u2011fits\u2011all checklist.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4. Objection Handling and Competitive Plays<\/strong><\/h4>\n\n\n\n<p>Machine\u2002learning is able to bring to the surface which objection responses are the ones that result in deals that are saved.<\/p>\n\n\n\n<p><strong>Use that in the playbook to:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Rank responses and talk tracks by historical win impact<\/li>\n\n\n\n<li>Recommend specific battlecards when certain keywords or competitors are detected<\/li>\n\n\n\n<li>Continually update objection libraries as markets move<\/li>\n<\/ul>\n\n\n\n<p>This makes objection handling into a measurable and improvable asset rather than \u201ctribal knowledge.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Onboarding and Continuous Enablement<\/strong><\/h4>\n\n\n\n<p>A contemporary playbook is your enablement backbone as well.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>With ML and AI, you can :&nbsp;<\/li>\n\n\n\n<li>Generate role-specific learning paths from the performance gaps\u2002of a rep.<\/li>\n\n\n\n<li>Create scenario-based quizzes and simulations using real call data.<\/li>\n\n\n\n<li>Suggest training\u2002modules when a buddy pattern is identified (eg, low conversion at a certain stage).<\/li>\n<\/ul>\n\n\n\n<p><strong>New reps aren\u2019t simply given a massive PDF:<\/strong> they are taken through a series of plays and practice customized to them.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"642\" src=\"https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2026\/03\/ai-sales-playbooks-machine-learning-strategy-2026.webp\" alt=\"Machine learning sales playbooks strategy and automation examples\" class=\"wp-image-11961\" srcset=\"https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2026\/03\/ai-sales-playbooks-machine-learning-strategy-2026.webp 1400w, https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2026\/03\/ai-sales-playbooks-machine-learning-strategy-2026-300x138.webp 300w, https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2026\/03\/ai-sales-playbooks-machine-learning-strategy-2026-1024x470.webp 1024w, https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2026\/03\/ai-sales-playbooks-machine-learning-strategy-2026-768x352.webp 768w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Practical Steps to Build Your First ML-Powered Playbook<\/strong><\/h3>\n\n\n\n<p>You don&#8217;t need a massive data science team to begin.<\/p>\n\n\n\n<p>Take a staged, practical approach.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 1: Clarify Your Outcomes<\/strong><\/h4>\n\n\n\n<p><strong>Figure out what you want the playbook to enhance first:<\/strong>\u2002<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>More Qualified Pipeline.<\/li>\n\n\n\n<li>Better stage-to-stage conversion.<\/li>\n\n\n\n<li>Shorter sales cycles.<\/li>\n\n\n\n<li>More Accurate Forecasts With Better Visibility.<\/li>\n<\/ul>\n\n\n\n<p>Choose one or two key metrics.<\/p>\n\n\n\n<p>This will inform your tooling decisions and\u2002prevent you from implementing \u201cAI for AI\u2019s sake.\u201d<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 2: Audit Your Current Playbook and Data<\/strong><\/h4>\n\n\n\n<p><strong>Review :<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What is available:<\/strong> documents, slides, Notion pages, and playbooks.<\/li>\n\n\n\n<li><strong>How reps work in real life:<\/strong> what they use, what\u2002they ignore.<\/li>\n\n\n\n<li><strong>Where information is stored: <\/strong>CRM fields, call recordings, emails, and product logs.<\/li>\n<\/ul>\n\n\n\n<p><strong>Identify :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Critical gaps (no standard reasons for closed-lost, for example)<\/li>\n\n\n\n<li>Low-hanging fruit (allowing basic lead scoring based on existing data)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 3: Start With One High\u2011Impact Use Case<\/strong><\/h4>\n\n\n\n<p><strong>Common first use cases :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Predictive lead scoring with prioritized outreach sequences.<\/li>\n\n\n\n<li>Generate emails and messages by segment and role.<\/li>\n\n\n\n<li>Call Recording Analysis to uncover best-practice questions.<\/li>\n<\/ul>\n\n\n\n<p><strong>Create a\u2002basic workflow :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data in \u2192 ML or AI tool \u2192 recommendation \u2192 sales workflow (CRM, sequences)<\/li>\n<\/ul>\n\n\n\n<p>Document how this fits into your play and roll your\u2002reps out on it.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 4: Embed in Daily Tools and Routines<\/strong><\/h4>\n\n\n\n<p>Impact is determined by adoption.<\/p>\n\n\n\n<p><strong>&nbsp;Focus on :&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bringing recommendations to life where reps live. (CRM widgets, inbox sidebars, dialer pop-ups)<\/li>\n\n\n\n<li>Brief in-context explanations of &#8220;why this is recommended&#8221; so reps have trust in the system.<\/li>\n\n\n\n<li>When to override recommendations with clear guidance to reps on when to use their judgment.<\/li>\n<\/ul>\n\n\n\n<p>Your playbook should feel like a co\u2011pilot, not a surveillance system.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Step 5: Iterate Using Feedback Loops<\/strong><\/h4>\n\n\n\n<p><strong>Think of your playbook as a product:&nbsp;<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Continuously collect rep feedback on what aids and what impedes.<\/li>\n\n\n\n<li>Quantify the effect of particular plays and recommendations.<\/li>\n\n\n\n<li>Update models, thresholds, and messaging using data and human intelligence as inputs.<\/li>\n<\/ul>\n\n\n\n<p>Eventually, you get a virtuous cycle:\u2002more use \u2192 more data \u2192 smarter playbook \u2192 better results.<\/p>\n\n\n\n<div class=\"wp-block-group custom-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\"><strong>Governance, Trust, and Change Management<\/strong><\/h3>\n\n\n\n<p>Introducing machine learning to your sales playbook is a people problem, not a technology problem.<\/p>\n\n\n\n<p><strong>Key considerations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transparency:<\/strong> In plain language, disclose the signals the system is and is not using (e.g., we&#8217;re not looking at your personal messages).<\/li>\n\n\n\n<li><strong>The guardrails:<\/strong> Define boundaries around what AI can and cannot do (e.g., it\u2019s not allowed to send messages without human review on high-stakes transactions).<\/li>\n\n\n\n<li><strong>Ethics and Compliance:<\/strong> Make sure your use of data is in line with your\u2002privacy policies, your contracts, and your local regulations \u2014 particularly when it comes to call recordings and customer data.<\/li>\n\n\n\n<li><strong>Skills: <\/strong>To interpret data and coach with it, not\u2002against it. Enable managers and enablement leaders.<\/li>\n<\/ul>\n\n\n\n<p>When salespeople\u2002realize the system is truly helping them make quota \u2014 and isn\u2019t just another layer of inspection \u2014 they\u2019re much more willing to buy in.<\/p>\n<\/div><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Example Structure of a Machine\u2011Learning Sales Playbook<\/strong><\/h3>\n\n\n\n<p>Here is a concise outline you can adapt:<\/p>\n\n\n\n<ol class=\"wp-block-list merged-list\">\n<li><strong>Foundations<\/strong><strong><br><\/strong>\n<ul class=\"wp-block-list\">\n<li>Data sources and quality standards<br><\/li>\n\n\n\n<li>ICP segments and ML\u2011derived sweet spots<br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Prospecting Plays<\/strong><strong><br><\/strong>\n<ul class=\"wp-block-list\">\n<li>Lead scoring and prioritization rules<br><\/li>\n\n\n\n<li>Dynamic cadences by segment and intent level<br><\/li>\n\n\n\n<li>AI\u2011generated outreach templates with review guidelines<br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Opportunity Plays (By Stage)<\/strong><strong><br><\/strong>\n<ul class=\"wp-block-list\">\n<li>Stage objectives and required milestones<br><\/li>\n\n\n\n<li>Recommended actions and content (ranked)<br><\/li>\n\n\n\n<li>Risk and opportunity signals (what triggers a change in play)<br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Objections and Competitive Plays<br><\/strong>\n<ul class=\"wp-block-list\">\n<li>Top\u2011performing responses and talk tracks<br><\/li>\n\n\n\n<li>Dynamic battlecard recommendations<br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Coaching and Enablement<\/strong><strong><br><\/strong>\n<ul class=\"wp-block-list\">\n<li>Call\u2011intelligence\u2011driven coaching flows<br><\/li>\n\n\n\n<li>Personalized learning paths for reps<br><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Governance and Continuous Improvement<\/strong><strong><br><\/strong>\n<ul class=\"wp-block-list\">\n<li>How updates are made (cadence, owners)<br><\/li>\n\n\n\n<li>Metrics monitored and how decisions are taken<br><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p>Even in a simplified form, this structure forces you to connect strategy, data, and daily execution.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<h4 class=\"wp-block-heading\"><strong>The Future: From Playbooks to Autonomous Revenue Systems<\/strong><\/h4>\n\n\n\n<p>That said, as models get better and sales tech platforms collapse, the difference between \u201cplaybook\u201d and \u201csystem\u201d will blur.<\/p>\n\n\n\n<p>It\u2019s already happening in tools that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Logs playbooks for new segments based on past successes<\/li>\n\n\n\n<li>Build role- specific training tracks for new hires<\/li>\n\n\n\n<li>Run 24\/7 outbound \u201cagents\u201d that prospect and qualify before a human steps in<\/li>\n<\/ul>\n\n\n\n<p>The enduring lever of advantage will not be \u2018can\u2002you have AI\u2019 \u2013 it will be how well you encode your unique sales DNA, customer intelligence, and strategy into these systems.<\/p>\n\n\n\n<p>And if you begin now with a targeted, machine-learning-enabled sales playbook, you\u2019re not simply updating documentation.<\/p>\n\n\n\n<p>You\u2019re building the operating system for how your revenue team will sell, learn, and win in the years to come.<\/p>\n<\/blockquote>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/saleshiker.com\/\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/saleshiker.com\/blog\/wp-content\/uploads\/2025\/11\/boost-sales-in-day.webp\" alt=\"CTA Image\"\/><\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Sales playbooks were once just PDFs that were rarely read. In 2026, the best teams are evolving those static documents into living, learning systems driven by machine learning and AI. Machine learning is not just about &#8220;doing things faster.\u201d And it changes the way you build, refresh, and execute your sales motion\u2014every call, email, and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":11959,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24],"tags":[],"class_list":["post-11958","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-whatsapp-marketing-sales-strategy"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/posts\/11958","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/comments?post=11958"}],"version-history":[{"count":5,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/posts\/11958\/revisions"}],"predecessor-version":[{"id":12011,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/posts\/11958\/revisions\/12011"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/media\/11959"}],"wp:attachment":[{"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/media?parent=11958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/categories?post=11958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saleshiker.com\/blog\/wp-json\/wp\/v2\/tags?post=11958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}