{"id":1933,"date":"2024-04-09T00:00:15","date_gmt":"2024-04-09T00:00:15","guid":{"rendered":"https:\/\/contentguru.builtbytoast.uk\/?post_type=resource&#038;p=1933"},"modified":"2026-02-19T12:19:20","modified_gmt":"2026-02-19T12:19:20","slug":"calculating-erlang-a","status":"publish","type":"resource","link":"https:\/\/www.contentguru.com\/en-gb\/resources\/blogs\/calculating-erlang-a\/","title":{"rendered":"Testing Customer Patience with the Erlang A Formula"},"content":{"rendered":"\n\n    <section class=\"block-resource-hero toast-block no-margin no-padding\"> \n\n        \n\n\n    <div class=\"resource-hero\">\n        <div class=\"container\">\n                                                <div class=\"resource-type\">Blogs<\/div>\n                                <h1>Testing Customer Patience with the Erlang A Formula<\/h1>\n                <div class=\"publish-date\">Published: April 9th 2024<\/div>\n                    <\/div>\n    <\/div>\n\n    \n    <\/section>\n\n\n\n\n\n    <section class=\"block-columns toast-block no-margin padding-both has-bg\" style=\"background-color:#ddf3fc;color:#0c2943;\"> \n\n        \n\n\n<div class=\"columns-area\">\n    <div class=\"container\">\n                                <div class=\"main-column-grid grid\" style=\"grid-template-columns:repeat(1, 1fr)\">\n                                    <div class=\"col \">\n                        <p>In our last blog,\u00a0<a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/blogs\/calculating-erlang-c\/\">we went into detail on Erlang C; the formula commonly used for calculating the number of agents needed to service a queue of a given size.<\/a>\u00a0Erlang C is at the heart of contact center service levels, but there\u2019s another formula that adds another level of depth to our analysis; the Erlang A formula.<\/p>\n<p>In short, Erlang A goes one step further than Erlang C, in that it adds additional depth to its portrait of the customer. It measures\u00a0<b>customer patience<\/b>.<\/p>\n<p>How is this done, how does it interact with the original Erlang C calculation, and how can you integrate its insights into your contact center operations? Is the sequel, for once, better than the original? Read on to discover more.<\/p>\n<p>Before we continue, optimizing contact center performance depends on more than Erlang A. To go in-depth on contact center performance metrics,\u00a0download Content Guru\u2019s whitepaper:\u00a0<a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/whitepapers\/keeping-up-with-the-kpis\/\"><b>Keeping Up With the KPIs: The Contact Center KPIs Key to Outstanding CX.<\/b><\/a><\/p>\n                    <\/div>\n                            <\/div>\n            <\/div>\n<\/div>\n\n    \n    <\/section>\n\n\n\n\n\n    <section class=\"block-columns toast-block no-margin padding-both\"> \n\n        \n\n\n<div class=\"columns-area\">\n    <div class=\"container\">\n                                <div class=\"main-column-grid grid\" style=\"grid-template-columns:repeat(1, 1fr)\">\n                                    <div class=\"col \">\n                        <h2><strong>What is Erlang A?<\/strong><\/h2>\n<p>Erlang A was developed thirty years after the original Erlang C, in 1946, by Swedish mathematician, Cony Palm. (In addition to Erlang A and C, there is also a secret sibling, Erlang B, though this is not relevant to the operation of modern contact centers.)<\/p>\n<p>Erlang C, whilst essential, missed out on a critical aspect of customer behavior; customer patience. It assumed that, once a customer queued up, they would remain in the queue until their call was answered. This is obviously not true for the real world. Customers run out of patience and\u00a0<b>abandon<\/b>\u00a0their calls. (<i>A<\/i>\u00a0stands for\u00a0<i>abandons<\/i>. See? Now we\u2019re starting to appreciate the mystery of the thing.)<\/p>\n<p>Erlang A offers a number of crucial benefits that Erlang C fails to account for:<\/p>\n<ul>\n<li>By factoring in customer patience, you can better tailor contact center performance to your specific industry and need. Different types of organizations will experience different kinds of customer behavior \u2013 Erlang A allows you to account for that.<\/li>\n<\/ul>\n<ul>\n<li>Erlang A represents to first step toward a focus on\u00a0<b>Customer Experience (CX)<\/b>. Where Erlang C assumed that customers would sit in queues, suffering as they must, Erlang A appreciates that customers are individuals, who\u2019ll abandon calls when they feel ignored.<\/li>\n<\/ul>\n<ul>\n<li>Erlang C was built on a fundamentally inaccurate view of the world; Erlang A addresses that inaccuracy,\u00a0allowing for the most accurate forecast of demand possible.<\/li>\n<\/ul>\n<p>Erlang A, then, is a staple of the modern contact center. But how do we go about calculating it?<\/p>\n<div>\n<p>&nbsp;<\/p>\n<h2><strong>Calculating the Erlang A Formula<\/strong><\/h2>\n<p>To factor abandon rate into the Erlang C formula, Cony Palm brought together the original formula with the mathematics of Andrey Markov. Markov had discovered a number of formulae surrounding \u2018birth and death\u2019 processes. In this case, \u2018birth\u2019 is joining the queue, \u2018death\u2019 leaving it.<\/p>\n<p><img decoding=\"async\" id=\"15118302\" title=\"\" src=\"https:\/\/a.storyblok.com\/f\/152969\/380x70\/e8a9448e8f\/erlang-a-formula.gif\" alt=\"erland-a-formula\" \/><\/p>\n<p>The major difference between C and A is that Erlang A requires you to enter an \u2018average patience\u2019 value, in order to calculate the likelihood of a customer dropping out of the queue.<\/p>\n<h4>Average Patience<\/h4>\n<p>Average patience is the value at which 50% of your customers abandon their calls. This is simple to calculate; simply plot the percentage of abandoned calls against time, and you\u2019ll get a value to input here.<\/p>\n<p>This is where Erlang A allows for personalization; each contact center will have a different average patience value. A retailer, for instance, would likely see customers with less willingness to queue than a government agency.<\/p>\n<p>&nbsp;<\/p>\n<h2><b>Tools for Calculating Erlang A<\/b><\/h2>\n<p>If you thought Erlang C was difficult, Erlang A adds another level of complexity on top of that.<\/p>\n<p>Even when you understand the mathematics behind the formula, calculating the additional value of\u00a0<i>Average Patience<\/i>\u00a0would prove an extra challenge. The best way to approach Erlang A is through an automatic calculator, letting you input the basic values, in order to calculate customer patience.<\/p>\n<p>Such a solution would offer:<\/p>\n<ul>\n<li>Predictive demand, using historic data on average customer patience to calculate abandon rates, then building on that data to create an accurate prediction of contact demand.<\/li>\n<\/ul>\n<ul>\n<li>Automatic scheduling, leveraging the Erlang A to schedule enough agents to meet a given level of customer demand, ensuring that no customer has to wait longer than their average patience, reducing abandon rates.<\/li>\n<\/ul>\n<ul>\n<li>Flexible reports, combining Erlang A and Erlang C with other metrics of customer experience quality, for a complete overview of contact center performance.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><strong>Moving Beyond Erlang A<\/strong><\/h2>\n<p>Erlang A is a step beyond C. It offers a new level of complexity to demand forecasting, but it\u2019s not the only contact center performance metric. To get a complete overview of your customer contact, you need to combine Erlang A with metrics that measure the\u00a0<b>quality of Customer Experience<\/b>.<\/p>\n<ul>\n<li><a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/blogs\/csat-meaning\/\" target=\"_self\"><b>Customer Satisfaction (CSAT)<\/b><\/a>\u00a0&#8211; Customer satisfaction begins with a simple question, \u2018How satisfied are you with your experience?\u2019 From here, customers respond on a scale from \u2018very dissatisfied\u2019 to \u2018very satisfied\u2019. These results fall on a scale of 1 to 10, from which you can calculate an average score across various populations, anywhere between 1 and 100.<\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/blogs\/net-promoter-score\/\" target=\"_self\"><b>Net Promoter Score (NPS)<\/b><\/a><b>\u00a0\u2013\u00a0<\/b>Net Promoter Score doesn\u2019t just measure customer satisfaction, it measures customer\u00a0<i>advocacy<\/i>. By asking which customers are likely to recommend your business, it serves as an effective indicator of which customers are likely to stick by your side in the long run.<\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/whitepapers\/customer-service-behavioral-analytics\/\" target=\"_self\"><b>Value Enhancement Score (VES)<\/b><\/a>\u00a0\u2013 A relatively new metric, VES asks two questions, \u2018How successfully were you able to use our product\/service?\u2019, and, \u2018How confident are you with your purchase?\u2019 In answering these questions, the customer gives their opinion on both your business and the impression conveyed by your customer service.<\/li>\n<\/ul>\n<ul>\n<li><a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/blogs\/customer-effort-score\/\" target=\"_self\"><b>Customer Effort Score (CES)<\/b><\/a><b>\u00a0\u2013\u00a0<\/b>CES measures how much effort a customer had to put in to reach a solution to their problem. Friction within your customer service estate, confusion on the part of agents, or a failure to resolve a problem all lead to poor Customer Effort Score.<\/li>\n<\/ul>\n<p>Every metric offers something different; a new perspective on a complex problem. And the more viewpoints you get, the deeper an insight you can draw. To truly perfect customer contact, you need complete contact center transparency.<\/p>\n<\/div>\n                    <\/div>\n                            <\/div>\n            <\/div>\n<\/div>\n\n    \n    <\/section>\n\n\n\n\n\n    <section class=\"block-columns toast-block no-margin padding-both has-bg\" style=\"background-color:#ddf3fc;\"> \n\n        \n\n\n<div class=\"columns-area\">\n    <div class=\"container\">\n                                <div class=\"main-column-grid grid\" style=\"grid-template-columns:repeat(1, 1fr)\">\n                                    <div class=\"col \">\n                        <h2><strong>Erlang A Made Easy with storm<span class=\"superscript\"><sub>\u00ae<\/sub><\/span><\/strong><\/h2>\n<p>Erlang A is just one step toward true contact center transparency. To achieve that, you need a complete reporting solution.\u00a0<b>storm<\/b><b><span class=\"superscript\"><sub>\u00ae<\/sub><\/span><\/b><b>\u00a0<\/b>VIEW\u2122 brings every metric of contact center performance into a single, flexible interface. Users can pick from a pre-made range of metrics, or design their own custom KPIs. This data can be displayed on real-time dashboards and wallboards, or brought into shareable reports.<\/p>\n<p>Content Guru\u2019s Workforce Engagement Management (WFM) solution,\u00a0<b>storm<\/b>\u00a0WFM\u2122 takes this one step further to offer automated scheduling, AI-backed demand prediction, and deep contact center performance insights, down to the level of the individual agent. WFM removes the complex formula, democratizing CX and giving you direct control of your Customer Experience.<\/p>\n<p>Want to learn more about key Customer Experience metrics and contact center KPIs?\u00a0Download Content Guru\u2019s whitepaper: <a href=\"https:\/\/www.contentguru.com\/en-gb\/resources\/whitepapers\/keeping-up-with-the-kpis\/\"><b>Keeping Up With the KPIs: The Contact Center KPIs Key to Outstanding CX.<\/b><\/a><\/p>\n<p>&nbsp;<\/p>\n                    <\/div>\n                            <\/div>\n            <\/div>\n<\/div>\n\n    \n    <\/section>\n\n","protected":false},"excerpt":{"rendered":"<p>Erlang C is at the heart of contact center service levels, but there\u2019s another formula that adds another level of depth to our analysis; the Erlang A formula.<\/p>\n","protected":false},"featured_media":6120,"template":"","meta":{"_acf_changed":true,"inline_featured_image":false},"resource_industry":[],"resource_topic":[],"resource_type":[5],"resource_folder":[73],"class_list":["post-1933","resource","type-resource","status-publish","has-post-thumbnail","hentry","resource_type-blogs"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Testing Customer Patience with the Erlang A Formula - English - UK<\/title>\n<meta name=\"description\" content=\"Erlang C is at the heart of contact center service levels, but there\u2019s another formula that adds another level of depth to our analysis; 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