{"id":39198,"date":"2026-08-01T12:27:35","date_gmt":"2026-08-01T12:27:35","guid":{"rendered":"https:\/\/www.nvecta.com\/blog\/?p=39198"},"modified":"2026-08-04T13:06:58","modified_gmt":"2026-08-04T13:06:58","slug":"what-is-retention-analysis","status":"publish","type":"post","link":"https:\/\/www.nvecta.com\/blog\/what-is-retention-analysis\/","title":{"rendered":"Retention Analysis: A Complete Guide"},"content":{"rendered":"\n<p>Acquiring a customer could look like a win, but the actual long-term growth is achieved through retaining customers. <strong>Retention analysis<\/strong> shows whether customers keep coming back or walk away.&nbsp;<\/p>\n\n\n\n<p>The problem is not the loss itself. The problem is not knowing which customers were at risk until it is too late to act. A customer who logged in daily for months can go quiet for weeks before anyone notices, and by then, the account is gone.<\/p>\n\n\n\n<p>This blog covers what retention analysis means, why it matters for business growth, the metrics and techniques behind it, and how to run it step by step for your business. We also explain how NVECTA turns these insights into automated retention action, across every customer touchpoint, in real time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Retention Analysis<\/strong><\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis.png\" alt=\"What Is Retention Analysis\" class=\"wp-image-39206\" srcset=\"https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis.png 1920w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-300x169.png 300w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-1024x576.png 1024w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-267x150.png 267w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-768x432.png 768w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-1536x864.png 1536w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-370x208.png 370w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-270x152.png 270w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-570x321.png 570w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/What-Is-Retention-Analysis-740x416.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n<\/div>\n\n\n<p>Retention analysis is the process of tracking how many customers stay active with a business over time, and studying the behavioural patterns behind why some stay and others leave.<\/p>\n\n\n\n<p>It uses real -time usage data, like purchases, logins, or feature activity. Teams groups customers into cohorts based on the first key action, then tracks how each group behaves over weeks or months.<\/p>\n\n\n\n<p>What counts as &#8220;retained&#8221; changes by industry. A repeat purchase counts for a retail brand. An active subscription counts for a software company. A renewed policy counts for an insurer. The tracking method stays the same. Only the definition shifts.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Retention Analysis Matters for Long-Term Growth<\/strong><\/h2>\n\n\n\n<p>Retention analysis matters as it gives a complete picture of whether your <a href=\"https:\/\/www.nvecta.com\/blog\/best-customer-retention-strategies\/\">retention strategies<\/a> are working or not. Teams can optimise campaigns, messaging, segments, and strategies to accelerate business growth.<\/p>\n\n\n\n<p>Here is what a retention analysis gives your team:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Lower acquisition spend, since existing customers already trust the product<\/li>\n\n\n\n<li>Steady revenue, because retained customers keep contributing month after month<\/li>\n\n\n\n<li>Clear forecasting, since retention data shows whether growth depends on new spend or loyal customers<\/li>\n\n\n\n<li>Lower support costs, since long-term customers need less help over time<\/li>\n<\/ul>\n\n\n\n<p>Together, these give finance, product, and marketing teams one view of business health, instead of three separate reports working in different directions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Retention Analysis vs Churn Analysis vs Customer Lifetime Value<\/strong><\/h3>\n\n\n\n<p>Retention analysis tracks who stays active with your business. Churn analysis tracks who leaves and when. Customer lifetime value puts a revenue figure on each customer relationship. Together, they show the full financial picture, not just one slice of it.<\/p>\n\n\n\n<style>\n.nv-ra-wrap{width:100%;overflow-x:auto;margin:0 0 1.5em;}\n.nv-ra-wrap table.nv-ra{width:100%;border-collapse:collapse;table-layout:fixed;}\n.nv-ra-wrap table.nv-ra th,\n.nv-ra-wrap table.nv-ra td{\n  border:1px solid #e2e6ea;\n  padding:12px 14px;\n  text-align:left;\n  vertical-align:top;\n  word-break:break-word;\n  overflow-wrap:break-word;\n  white-space:normal !important;\n}\n.nv-ra-wrap table.nv-ra th:nth-child(1),\n.nv-ra-wrap table.nv-ra td:nth-child(1){width:19%;}\n.nv-ra-wrap table.nv-ra th:nth-child(2),\n.nv-ra-wrap table.nv-ra td:nth-child(2){width:27%;}\n.nv-ra-wrap table.nv-ra th:nth-child(3),\n.nv-ra-wrap table.nv-ra td:nth-child(3){width:27%;}\n.nv-ra-wrap table.nv-ra th:nth-child(4),\n.nv-ra-wrap table.nv-ra td:nth-child(4){width:27%;}\n.nv-ra-wrap table.nv-ra thead th{background:#f4f7fa;font-weight:700;}\n.nv-ra-wrap table.nv-ra tbody td:first-child{background:#f9fafb;font-weight:600;}\n@media screen and (max-width:600px){\n  .nv-ra-wrap table.nv-ra,\n  .nv-ra-wrap table.nv-ra tbody,\n  .nv-ra-wrap table.nv-ra tr,\n  .nv-ra-wrap table.nv-ra td{display:block;width:100% !important;}\n  .nv-ra-wrap table.nv-ra thead{display:none;}\n  .nv-ra-wrap table.nv-ra tr{margin-bottom:16px;border:1px solid #d7dce1;border-radius:8px;overflow:hidden;}\n  .nv-ra-wrap table.nv-ra td{border:0;border-bottom:1px solid #eef1f4;}\n  .nv-ra-wrap table.nv-ra td:last-child{border-bottom:0;}\n  .nv-ra-wrap table.nv-ra tbody td:first-child{background:#eef3f8;font-weight:700;font-size:15px;}\n  .nv-ra-wrap table.nv-ra td:not(:first-child):before{content:attr(data-label);display:block;font-size:11px;font-weight:700;text-transform:uppercase;letter-spacing:.03em;color:#6b7280;margin-bottom:4px;}\n}\n<\/style>\n<div class=\"nv-ra-wrap\">\n<table class=\"nv-ra\">\n<thead>\n<tr><th>Aspect<\/th><th>Retention Analysis<\/th><th>Churn Analysis<\/th><th>Customer Lifetime Value<\/th><\/tr>\n<\/thead>\n<tbody>\n<tr><td>What it measures<\/td><td data-label=\"Retention Analysis\">Customers who remain active over a period<\/td><td data-label=\"Churn Analysis\">Customers who stop engaging over a period<\/td><td data-label=\"Customer Lifetime Value\">Total revenue a customer generates over the relationship<\/td><\/tr>\n<tr><td>Core question<\/td><td data-label=\"Retention Analysis\">Who stays, and why<\/td><td data-label=\"Churn Analysis\">Who leaves, and when<\/td><td data-label=\"Customer Lifetime Value\">How much is each customer worth<\/td><\/tr>\n<tr><td>Data focus<\/td><td data-label=\"Retention Analysis\">Usage, purchases, engagement patterns<\/td><td data-label=\"Churn Analysis\">Cancellations, inactivity, drop-off points<\/td><td data-label=\"Customer Lifetime Value\">Purchase history, average order value, retention period<\/td><\/tr>\n<tr><td>Typical formula<\/td><td data-label=\"Retention Analysis\">(End customers minus new customers) \u00f7 start customers \u00d7 100<\/td><td data-label=\"Churn Analysis\">Customers lost \u00f7 customers at start of period \u00d7 100<\/td><td data-label=\"Customer Lifetime Value\">Average revenue per customer \u00d7 average customer lifespan<\/td><\/tr>\n<tr><td>Primary use case<\/td><td data-label=\"Retention Analysis\">Spotting patterns that keep customers engaged<\/td><td data-label=\"Churn Analysis\">Measuring the scale and speed of customer loss<\/td><td data-label=\"Customer Lifetime Value\">Guiding budget for acquisition and retention spend<\/td><\/tr>\n<tr><td>Owned by<\/td><td data-label=\"Retention Analysis\">Product and customer success teams<\/td><td data-label=\"Churn Analysis\">Product and finance teams<\/td><td data-label=\"Customer Lifetime Value\">Marketing and finance teams<\/td><\/tr>\n<tr><td>Signals action for<\/td><td data-label=\"Retention Analysis\">Onboarding, engagement, loyalty programs<\/td><td data-label=\"Churn Analysis\">Win back campaigns, exit surveys<\/td><td data-label=\"Customer Lifetime Value\">Pricing, upsell strategy, acquisition budget<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n<p>Retention and churn are two sides of the same coin; one counts who stays, the other counts who leaves. CLV adds the value that makes both numbers matter to the business.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to Use Retention Analysis Step by Step<\/strong><\/h2>\n\n\n\n<p>Running retention analysis takes seven steps, from defining an active customer to acting on what the data shows. Skip a step, and you miss the real reason behind churn.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Define active usage<\/strong><\/h3>\n\n\n\n<p>Pick an action that shows real value, not just a login. For a messaging app, that means sending a message. For a design tool, it means editing a project.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Build time-based cohorts<\/strong><\/h3>\n\n\n\n<p>&nbsp;Group customers by signup week, purchase month, or first key action. This lets you compare groups under the same starting conditions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Segment by relevant traits<\/strong><\/h3>\n\n\n\n<p>Split cohorts by plan type, channel, or region to see which segments retain well and which need attention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Track engagement depth<\/strong><\/h3>\n\n\n\n<p>&nbsp;Look past return visits and measure how much the customer engages with the product.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Find drop-off points<\/strong><\/h3>\n\n\n\n<p>&nbsp;Find where most customers exit, whether that is onboarding, week two, or renewal time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Apply predictive signals<\/strong><\/h3>\n\n\n\n<p>&nbsp;Use AI churn prediction models to flag accounts at risk, based on patterns like declining usage, inactivity, or a drop in engagement, before they cancel.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7. Trigger the next best action<\/strong><\/h3>\n\n\n\n<p>Match the risk signal to the right response: an onboarding nudge, a win-back offer, or a support outreach, based on what that specific customer needs at that moment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>8. Measure and refine<\/strong><\/h3>\n\n\n\n<p>Track whether the action actually improved retention for that segment, then adjust the trigger rules for the next cycle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Retention Metrics and Techniques<\/strong><\/h2>\n\n\n\n<p>Retention analysis works on a set of core metrics and proven techniques. Together, they show not just whether customers stay, but which group or time period moves that number up or down.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Customer retention rate<\/strong><\/h3>\n\n\n\n<p>Formula: CRR = [(E \u2212 N) \/ S] \u00d7 100<br>Where E = customers at the end of the period, N = new customers acquired during the period, and S = customers at the start of the period.<br>This is the number most teams check first, since it works as a general health check on the business.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Customer churn rate<\/strong><\/h3>\n\n\n\n<p>&nbsp;Formula: Churn Rate = (Customers Lost \/ S) \u00d7 100<br>Where S = customers at the start of the period.<br>This is the direct inverse of retention rate and shows the scale of loss in the same time frame.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>N Day retention<\/strong><\/h3>\n\n\n\n<p>Formula: N Day Retention = (Users Active on Day N \/ Total Users in Cohort) \u00d7 100<br>Common checkpoints are Day 1, Day 7, and Day 30. A weak Day 1 number usually points to a rough first session.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Net revenue retention<\/strong><\/h3>\n\n\n\n<p>Formula: NRR = [(Starting MRR + Expansion \u2212 Downgrades \u2212 Churn) \/ Starting MRR] \u00d7 100<br>Cross 100 per cent, and existing customers are spending more, even as some leave.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Cohort retention rate<\/strong><\/h3>\n\n\n\n<p>Formula: Cohort Retention Rate = (Active Customers in Cohort at Time T \/ Total Customers in Cohort at Signup) \u00d7 100<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Repeat purchase\/Usage rate.<\/strong><\/h3>\n\n\n\n<p>Formula: Repeat Rate = (Customers with 2 or More Purchases or Sessions \/ Total Customers) \u00d7 100<br>This short-term signal often predicts longer-term retention, especially for e-commerce and SaaS activation tracking.<\/p>\n\n\n\n<p>A few techniques add more value and depth to these numbers:<\/p>\n\n\n\n<p><strong>Cohort analysis-<\/strong> groups customers by shared signup traits to compare behaviour over time.<br><strong>Survival analysis- <\/strong>estimates how long a typical customer stays before churning.<br><strong>RFM segmentation-<\/strong> ranks customers by recency, frequency, and spend to separate high-value accounts from at-risk ones.<br><strong>Predictive churn modelling-<\/strong> uses historical patterns to forecast which customers are likely to leave next.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How AI Improves Retention Analysis<\/strong><\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis.png\" alt=\"How AI Improves Retention Analysis\" class=\"wp-image-39207\" srcset=\"https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis.png 1920w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-300x169.png 300w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-1024x576.png 1024w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-267x150.png 267w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-768x432.png 768w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-1536x864.png 1536w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-370x208.png 370w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-270x152.png 270w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-570x321.png 570w, https:\/\/cdn3.notifyvisitors.com\/blog\/wp-content\/uploads\/2026\/08\/How-AI-Improves-Retention-Analysis-740x416.png 740w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n<\/div>\n\n\n<p>AI improves retention analysis by catching churn risk in real time, not once a month when a report shows a drop.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-time risk scoring<\/strong><\/h3>\n\n\n\n<p>AI scans usage patterns across thousands of accounts at once and flags the ones showing early warning signs, before a customer decides to leave.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Unified customer profiles<\/strong><\/h3>\n\n\n\n<p>AI pulls data from product, billing, and support tools into one profile per customer. Teams work from a single picture, not scattered dashboards.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Automated triggers<\/strong><\/h3>\n\n\n\n<p>Once risk is flagged, AI can launch a retention campaign on its own, with no manual setup needed for each account.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How NVECTA Powers Business Growth with Retention Analysis<\/strong><\/h2>\n\n\n\n<p>NVECTA is an AI-powered <a href=\"https:\/\/www.nvecta.com\/blog\/what-is-customer-data-platform-cdp\/\">customer data platform<\/a>. It facilitates business growth by turning retention analysis into a system that runs on its own, not a report someone builds each month.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Unified Customer Profiles<\/strong><\/h3>\n\n\n\n<p>NVECTA pulls data from every channel- web, app, support, and billing into one profile per customer. Teams see the full picture in one place.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>AI-powered Churn Scoring<\/strong><\/h3>\n\n\n\n<p>NVECTA&#8217;s models score churn risk for every account in real time, using signals like a drop in usage, support tickets, and billing activity. Teams get a ranked list of at-risk customers, sorted by urgency.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Automated retention campaigns<\/strong><\/h3>\n\n\n\n<p>&nbsp;Once risk is flagged, NVECTA builds targeted segments and triggers campaigns across email, SMS, push, and in-app messages, with no manual setup for each one.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-time dashboards<\/strong><\/h3>\n\n\n\n<p>Retention trends update alongside revenue data, so leadership tracks both together, not as separate reports weeks apart.<\/p>\n\n\n\n<p>For businesses across ecommerce, SaaS, and BFSI, this turns retention analysis from a monthly task into a steady part of how the business grows and tracks its users.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h3>\n\n\n\n<p>Every business loses some customers. What decides growth is how fast a team spots friction points, like who is about to leave, and later acts on time before they actually stop engaging.<\/p>\n\n\n\n<p>Retention analysis turns scattered behaviour data into clear signals. Teams stop guessing and start acting on real patterns, from onboarding drop-offs to renewal risk.<\/p>\n\n\n\n<p>NVECTA brings this together in one platform. It unifies customer data, predicts churn early, and triggers retention campaigns across channels.&nbsp;<\/p>\n\n\n\n<p><strong><em>See how NVECTA helps you turn retention data into steady, long-term growth.&nbsp;<\/em><\/strong><\/p>\n\n\n\n<p><strong><em><a href=\"https:\/\/www.nvecta.com\/products\/schedule-demo\">Schedule a demo today.<\/a><\/em><\/strong><\/p>\n\n\n\n<p><strong>Frequently Asked Questions<\/strong><\/p>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1785586206341\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is a good customer retention rate?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A good retention rate depends on the industry, but most subscription businesses aim for 85 per cent or higher each year. E-commerce brands often track monthly rates closer to 30 to 40 per cent, since purchase cycles are short and less steady than subscriptions.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785586233215\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>How is retention analysis different from churn analysis?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Retention analysis measures who stays active and why. Churn analysis measures who leaves and when. Both use the same data, but one shows what works and the other shows what does not.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785586252596\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What tools are used for retention analysis?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Teams use a customer data platform to unify behaviour data, along with product analytics tools for event tracking and BI tools for reporting. Platforms like NVECTA combine data unification, cohort analysis, and predictive scoring in one system.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785586296758\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>How often should retention analysis be done?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Most businesses review retention weekly for new products and monthly for mature ones, with a deeper cohort review each quarter. Subscription businesses should also check retention around renewal dates, since risk often rises near billing cycles.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785586316505\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Can AI predict customer churn before it happens?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. AI models trained on usage, billing, and support data can flag accounts at risk of churn weeks before they cancel. This gives teams time to step in with a targeted offer or outreach before the customer leaves.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n<ul class=\"wp-block-social-links is-layout-flex wp-block-social-links-is-layout-flex\"><li class=\"wp-social-link wp-social-link-gravatar  wp-block-social-link\"><a rel=\"me noopener\" href=\"https:\/\/gravatar.com\/heroic73a4bea1f3\" class=\"wp-block-social-link-anchor\" target=\"_blank\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M10.8001 4.69937V10.6494C10.8001 11.1001 10.9791 11.5323 11.2978 11.851C11.6165 12.1697 12.0487 12.3487 12.4994 12.3487C12.9501 12.3487 13.3824 12.1697 13.7011 11.851C14.0198 11.5323 14.1988 11.1001 14.1988 10.6494V6.69089C15.2418 7.05861 16.1371 7.75537 16.7496 8.67617C17.3622 9.59698 17.6589 10.6919 17.595 11.796C17.5311 12.9001 17.1101 13.9535 16.3954 14.7975C15.6807 15.6415 14.711 16.2303 13.6325 16.4753C12.5541 16.7202 11.4252 16.608 10.4161 16.1555C9.40691 15.703 8.57217 14.9348 8.03763 13.9667C7.50308 12.9985 7.29769 11.8828 7.45242 10.7877C7.60714 9.69266 8.11359 8.67755 8.89545 7.89537C9.20904 7.57521 9.38364 7.14426 9.38132 6.69611C9.37899 6.24797 9.19994 5.81884 8.88305 5.50195C8.56616 5.18506 8.13704 5.00601 7.68889 5.00369C7.24075 5.00137 6.80979 5.17597 6.48964 5.48956C5.09907 6.8801 4.23369 8.7098 4.04094 10.6669C3.84819 12.624 4.34 14.5873 5.43257 16.2224C6.52515 17.8575 8.15088 19.0632 10.0328 19.634C11.9146 20.2049 13.9362 20.1055 15.753 19.3529C17.5699 18.6003 19.0695 17.241 19.9965 15.5066C20.9234 13.7722 21.2203 11.7701 20.8366 9.84133C20.4528 7.91259 19.4122 6.17658 17.892 4.92911C16.3717 3.68163 14.466 2.99987 12.4994 3C12.0487 3 11.6165 3.17904 11.2978 3.49773C10.9791 3.81643 10.8001 4.24867 10.8001 4.69937Z\" \/><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">Gravatar<\/span><\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Acquiring a customer could look like a win, but the actual long-term growth is achieved through retaining customers. Retention analysis shows whether customers keep coming back or walk away.&nbsp; The problem is not the loss itself. The problem is not knowing which customers were at risk until it is too late to act. A customer [&hellip;]<\/p>\n","protected":false},"author":32,"featured_media":39204,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2929],"tags":[],"class_list":["post-39198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-growth"],"_links":{"self":[{"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/posts\/39198","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/users\/32"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/comments?post=39198"}],"version-history":[{"count":0,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/posts\/39198\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/media\/39204"}],"wp:attachment":[{"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/media?parent=39198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/categories?post=39198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nvecta.com\/blog\/wp-json\/wp\/v2\/tags?post=39198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}