{"id":11986,"date":"2018-04-16T08:15:09","date_gmt":"2018-04-16T06:15:09","guid":{"rendered":"http:\/\/revva.net\/?p=11986"},"modified":"2023-11-23T15:46:54","modified_gmt":"2023-11-23T15:46:54","slug":"segmentation-with","status":"publish","type":"post","link":"https:\/\/imssc.org\/biflix\/segmentation-with\/","title":{"rendered":"Analogies between Customer Segmentation and finding sex partners"},"content":{"rendered":"<p>True story: sick and tired of failed relationships, I decided I had enough of the random encounter method (so called <em>destiny<\/em>) and opted for a matching algorithm instead. You see, I\u2019m a scientist at heart, much more rational than emotional. So I collected all the information I deemed necessary from online and offline sources, such as<\/p>\n<ul>\n<li>Age\/Size\/Weight<\/li>\n<li>Spending habits<\/li>\n<li>N\u00ba of former husbands\/boyfriends<\/li>\n<li>N\u00ba of actual husbands\/boyfriends<\/li>\n<li>Kids? Yes\/No<\/li>\n<li>Literary preferences<\/li>\n<li>Sports practiced<\/li>\n<\/ul>\n<p>And so on. I even added up zodiacal signs in the mix. Some might argue that\u2019s not very scientific, but when you\u2019re exploring data before modeling, you shouldn\u2019t discard possible correlations just because.<\/p>\n<p>I combined the data from past girlfriends as well as new prospects. Simultaneously, I weighted in my expectations and tried to establish my real chances of accomplishment. Hey, the product is what it is, and one must remain true to oneself!<br \/>\nBy then, the target market (model) was taking shape, so it was time to A\/B test a couple of campaigns with selected customer groups. Results were encouraging, as the expected segments came back again and again to consume the product. Next step was to zeroing on the few perfectly defined segments, launching an all-out laser-targeted marketing operation. Eureka! It was a success, and all I needed to do at that point was to hand-pick the most profitable customer and stick to it.<\/p>\n<p>Here\u2019s the result:<\/p>\n<figure id=\"attachment_11987\" aria-describedby=\"caption-attachment-11987\" style=\"width: 301px\" class=\"wp-caption aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" class=\" wp-image-11987\" src=\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/isamuy-225x300.jpeg\" alt=\"data-driven baby\" width=\"301\" height=\"402\" \/><figcaption id=\"caption-attachment-11987\" class=\"wp-caption-text\">Isabella, a data-driven baby<\/figcaption><\/figure>\n<p>Well, the output might not be exactly what I had calculated in the blueprints, but as anybody involved in production can confirm, a flawless manufacturing process is an oximoron. Mother\u2019s fault, of course.<br \/>\nStill, she\u2019s my greatest achievement to date, a data-driven baby.<\/p>\n<h4><strong>Who buys what, when, how<\/strong><\/h4>\n<p>In my previous articles related to <a href=\"https:\/\/imssc.org\/biflix\/dynamic-pricing-for-tour-operators\/\">dynamic pricing &gt;&gt;<\/a> and <a href=\"https:\/\/imssc.org\/biflix\/11888-2\/\">revenue management &gt;&gt;<\/a> I mentioned several times this <em>segmentation<\/em> thing, taking for granted that my vast audience (Hi Hanna! Hi, Bob!) knew exactly what I was talking about. Indeed, they knew, but I found out they were still stuck with old school demographics customer classification. Sure, age and sex still are relevant when it comes to define buyer personas (both as customers and sex partners), but in this day and age, considering the large amount of data available, I\u2019d rather go the airline and hotel way and categorize my customers by <strong>profitability<\/strong>.<\/p>\n<p>If I wanted to provide a logical order to my articles, I should have written one about segmentation first and foremost, as it is the primordial material for proper revenue management. It doesn\u2019t sound as sexy and hi-tech as dynamic pricing or RM though, but from segmentation everything else derives: know thy client!<\/p>\n<p>If you want to ditch the old-school method, then, ask yourself (or rather, your data collection) the following questions:<\/p>\n<ul>\n<li>Who are my buyers?<\/li>\n<li>Which channel do they prefer? (how and where are they booking)<\/li>\n<li>What\u2019s their buying behavior?<\/li>\n<li>What\u2019s their favourite destination\/hotel\/airline\/tour\/services?<\/li>\n<li>How many times do they shop around before buying?<\/li>\n<li>And more to the point: How much did I get from them and how should all that influence my pricing strategy?<\/li>\n<\/ul>\n<p>To answer all that, using age-old spreadsheets is feasible; to use an automated analytics system like BIFLIX is smart, because you\u2019ll be able to insert in the equation your CRM as well as marketing automation tools, for starters.<\/p>\n<figure id=\"attachment_11988\" aria-describedby=\"caption-attachment-11988\" style=\"width: 700px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" class=\"size-full wp-image-11988\" src=\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/segment-a.fw_.png\" alt=\"Buyers, lookers, Look-to-book\" width=\"700\" height=\"400\" \/><figcaption id=\"caption-attachment-11988\" class=\"wp-caption-text\">Buyers, lookers, Look-to-book<\/figcaption><\/figure>\n<h4><strong>Segment by data<\/strong><\/h4>\n<p>It&#8217;s much like one of those online dating algorithms, after all. With data-driven segmentation, among other things you\u2019ll be able to:<\/p>\n<ul>\n<li>Design and implement laser-targeted marketing campaigns: Got millennials suddenly looking for two particular destinations at specific dates? There must be some happening there, that you are not aware of. Wake up, find out what\u2019s going on, and promote a tailor-made package through Pinterest, Instagram or whatever is the fancy name of the day for that segment.<\/li>\n<li>Predict demand: as per above, checking not only your booking systems but external data as well (like social media) you can anticipate the market. No kidding!<\/li>\n<li>Mind-boggling personalization, hence <strong>loyalty<\/strong>. Check <a href=\"https:\/\/tnooz.co\/2BpznPs\">here &gt;&gt;<\/a>\u00a0to see how Emirates did it on a rather large scale<\/li>\n<li>Apply the right price: remember <a href=\"https:\/\/imssc.org\/biflix\/dynamic-pricing-for-tour-operators\/\">dynamic pricing &gt;&gt;<\/a>? Wealthier or eager segments are willing to pay more; adjust rates accordingly to maximize profits for high-demand periods (or services).<\/li>\n<li>Create demand: combine the last two items, and you\u2019ll be even able to <strong>direct<\/strong> customers wherever you see fit for them. Again, no kidding: airlines and hotel chains have been doing it for ages, by analyzing their customers\u2019 information.<\/li>\n<\/ul>\n<p>Of course, all that can be achieved with a system like BIFLIX, and it\u2019s less complicated than it may appear at first. Actually, we are going a step ahead here, using machine learning algorithms (artificial intelligence) to predict whatever each segment will buy next: type of trip, destination, with or without children\u2026 the works. Crazy, uh? The likes of Hotelbeds, Expedia and TUI have been at it for a while, I am sure they are already benefiting from the results of these data experiments.<br \/>\nWhy shouldn\u2019t you, then?<\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-11989\" src=\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/buying-predict.fw_.png\" alt=\"buyer prediction\" width=\"700\" height=\"400\" \/><\/p>\n<p style=\"text-align: center;\"><em>For data boffins: this is a set of cluster segmentation predictions by profitability, using an unsupervised machine learning model.<\/em><\/p>\n<p>Go ahead: quit your tiresome relationships and let the truckload of data hidden in your organization help you find your ideal business partners, live happily ever after&#8230; maybe even conceive data driven babies!<\/p>\n<p>Thanks for reading<\/p>\n<p><em>Marcello Bresin<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>True story: sick and tired of failed relationships, I decided I had enough of the random encounter method (so called destiny) and opted for a matching algorithm instead. You see, I\u2019m a scientist at heart, much more rational than emotional. So I collected all the information I deemed necessary from online and offline sources, such [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":11989,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[70],"tags":[71,79,72],"class_list":["post-11986","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-bi","tag-hotels","tag-rm"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Analogies between Customer Segmentation and finding sex partners - BIFLIX<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/imssc.org\/biflix\/segmentation-with\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analogies between Customer Segmentation and finding sex partners - BIFLIX\" \/>\n<meta property=\"og:description\" content=\"True story: sick and tired of failed relationships, I decided I had enough of the random encounter method (so called destiny) and opted for a matching algorithm instead. 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