{"id":14992,"date":"2020-10-08T15:14:39","date_gmt":"2020-10-08T09:44:39","guid":{"rendered":"https:\/\/www.instamojo.com\/blog\/?p=14992"},"modified":"2021-06-20T02:47:00","modified_gmt":"2021-06-19T21:17:00","slug":"machine-learning-concept-that-simplified-lead-scoring-at-instamojo","status":"publish","type":"post","link":"https:\/\/www.instamojo.com\/blog\/machine-learning-concept-that-simplified-lead-scoring-at-instamojo\/","title":{"rendered":"A Machine Learning Concept That Simplified Lead Scoring At Instamojo"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Hundreds of small businesses sign up on Instamojo every day. As a data analyst in the team, one of our many jobs is to sift through all the data and build scalable models for other teams to use.\u00a0<\/span>One such problem statement we recently worked on was a lead-scoring project that didn&#8217;t just simplify the sales teams&#8217; life but also helped us get the most out of the leads.<\/p>\n<blockquote><p><strong>Today, I&#8217;ll take you through how we used machine learning to build a lead-scoring model at Instamojo. &#8211; Kaustub Rao, Lead Data Analyst @ Instamojo.\u00a0<\/strong><\/p><\/blockquote>\n<h2><strong>How to score leads? &#8211; The problem statement<\/strong><\/h2>\n<p>At instamojo, the sign-up and onboarding process is very simple. That being said, not all those who sign up, complete the onboarding process and start collecting payments or start off with an <a href=\"https:\/\/www.instamojo.com\/start\/online-store\/?utm_source=imblog&amp;utm_medium=interlink&amp;utm_campaign=online_store\" target=\"_blank\" rel=\"noopener\">online store<\/a>.<\/p>\n<p><span style=\"font-weight: 400;\">We observed that h<\/span><span style=\"font-weight: 400;\">aving a sales representative call every onboarded lead and guide them through the platform (shortly after onboarding) can have a great impact on their probability of conversion (i.e. starting to collect payments). <\/span><\/p>\n<p><span style=\"font-weight: 400;\">It can also significantly improve the amount and number of transactions these businesses process through Instamojo while also improving retention over time. <\/span><\/p>\n<p><strong>However, our sales team has limited bandwidth and cannot contact all of these merchants.\u00a0<\/strong><\/p>\n<blockquote><p>How can we leverage Machine Learning to qualify leads that are worth contacting?<\/p><\/blockquote>\n<h2><strong>Qualifying leads &#8211; the approach<\/strong><\/h2>\n<p>To determine what approach to take, we focussed on what we already know about the businesses\/leads signing up and combined it with the requirement at hand:<\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Lead qualification needs to happen soon after a merchant onboards. <\/span><\/li>\n<li><span style=\"font-weight: 400;\">We only have information that was collected during the onboarding process and limited data related to the merchant&#8217;s activity on Instamojo for the first few days post onboarding.<\/span><\/li>\n<\/ul>\n<p><strong>A naive approach would be to use this information to predict a merchants payment volume and then send the best leads to sales. But we found a couple of problems with this approach:<\/strong><\/p>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">This approach had an underlying assumption that the optimum use of a sales reps time is to contact merchants that will bring in a high volume of payments, which may not be the case. Such a merchant may bring in the same volume irrespective of whether a sales rep contacts them or not.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Materialised GMV is really a product of 2 aspects of a merchant<\/span>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Potential &#8211; how big is the merchants entire business?<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Intent &#8211; how likely is this merchant to collect some or all of their payments via Instamojo?<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<blockquote><p><span style=\"font-weight: 400;\">We felt that the best approach would be to try to segment merchants based on the above traits (potential &amp; intent) first, and not place any assumptions on who the best leads to send to sales may be.<\/span><\/p><\/blockquote>\n<h2><b>Modelling Potential and Intent of the leads<\/b><\/h2>\n<p><b>Supervised vs Unsupervised learning<\/b><\/p>\n<h3><b>Potential:<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">When it comes to determining the potential size of a business, a supervised approach would not really work. We have no true labels even for historical data. In such cases, it is better to use unsupervised approaches, such as clustering your data based on relevant variables and studying the profile of clusters to determine which clusters are high potential.<\/span><\/p>\n<h3><b>Intent:<\/b><\/h3>\n<p><b> <\/b><span style=\"font-weight: 400;\">This can be defined using a specific activity that merchants have done on our platform that has been historical indicators of high intent. A supervised classification model works here, as we have true labels for historical data.\u00a0<\/span><\/p>\n<h3><b>Variable Selection:<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Broadly speaking, we have 2 types of merchant data &#8211;<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Profile data &#8211; these are attributes of a merchants business that have little to do with Instamojo. For example &#8211; a merchant\u2019s website\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Activity data &#8211; this is data related to specific actions that a merchant has taken on the Instamojo platform<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">We only considered variables that could be derived from profile data for the potential model, as any activity related data could bias our clusters (as any activity at all is a sign of intent and including such data would underestimate the potential of low intent merchants).<\/span><\/p>\n<p><img loading=\"lazy\" class=\"alignnone size-full wp-image-14996\" src=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/profile-clusters.png\" alt=\"profile clusters\" width=\"741\" height=\"534\" srcset=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/profile-clusters.png 741w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/profile-clusters-300x216.png 300w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/profile-clusters-380x274.png 380w\" sizes=\"(max-width: 741px) 100vw, 741px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Conversely, we avoided using profile attributes in our intent model, as high potential merchants are generally more likely to be high intent and we did not want the model to learn and amplify such relationships that are purely correlational in nature.<\/span><\/p>\n<p><img loading=\"lazy\" class=\"alignnone wp-image-14995\" src=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles.png\" alt=\"correlated data for cluster profiles\" width=\"778\" height=\"438\" srcset=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles.png 961w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles-300x169.png 300w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles-768x432.png 768w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles-380x214.png 380w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/correlated-data-for-cluster-profiles-800x450.png 800w\" sizes=\"(max-width: 778px) 100vw, 778px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2><b>The solution to scalable lead scoring\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">We combined the output of these models to create our final segments.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" class=\"alignnone size-full wp-image-14993\" src=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/quadrant-diagram.png\" alt=\"quadrant diagram\" width=\"662\" height=\"540\" srcset=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/quadrant-diagram.png 662w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/quadrant-diagram-300x245.png 300w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/quadrant-diagram-380x310.png 380w\" sizes=\"(max-width: 662px) 100vw, 662px\" \/><\/p>\n<blockquote><p><span style=\"font-weight: 400;\">Leads from the quadrants ranking high on either <strong>intent<\/strong> or <strong>potential<\/strong> (HIHP, HILP, LIHP) are sent to the sales team for targeting. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">We also hold out a control sample from each of these segments, and use this to measure where the effect of sales contact is maximum.<\/span><\/p><\/blockquote>\n<h2><img loading=\"lazy\" class=\"alignnone size-full wp-image-14994\" src=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/process-of-qualifying-leads.png\" alt=\"process of qualifying leads\" width=\"755\" height=\"543\" srcset=\"https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/process-of-qualifying-leads.png 755w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/process-of-qualifying-leads-300x216.png 300w, https:\/\/www.instamojo.com\/blog\/wp-content\/uploads\/2020\/10\/process-of-qualifying-leads-380x273.png 380w\" sizes=\"(max-width: 755px) 100vw, 755px\" \/><\/h2>\n<h4>As a result of implementing this model, we saw a 40% lift\u00a0on revenue for the leads that were sent to sales vs a similar control group.<\/h4>\n<blockquote><p>&#8220;Talking to merchants\u00a0<i data-stringify-type=\"italic\">who need what we sell<\/i>\u00a0is an ultimate dream for any sales team. This lead scoring mechanism helped us identify high potential leads automatically,&#8221; &#8211; Shinda Shivaji, Inside Sales Head @ Instamojo.<\/p>\n<p>&#8220;This pushed our team to engage with the merchants at the right time. It helped improve conversion significantly &amp; accomplish our GMV\/revenue goals.&#8221;<\/p><\/blockquote>\n<h2><b>The future of lead qualification at Instamojo<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">As we gather more data on contacted merchants, we would want to model for the incremental value of contacting a merchant and send leads that score highest on this metric.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hypothetically, this can be done by having 2 models for predicting the metric of importance (e.g. sales), one trained on contacted users, and the other on uncontacted population.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">New leads can be scored on both models and the difference in the metric would indicate the incremental value of contact.<\/span><\/p>\n<p>We are excited about the possibilities that machine learning can make viable for teams across Instamojo.<\/p>\n<hr \/>\n<p style=\"text-align: center;\"><a class=\"mb-button mb-style-flat mb-size-default mb-corners-default mb-text-style-default \" style=\"background-color: #4e4e91;\" href=\"https:\/\/www.instamojo.com\/?utm_source=imblog&amp;utm_medium=interlink&amp;utm_campaign=home_page\" target=\"_blank\" rel=\"noopener\">DISCOVER INSTAMOJO<\/a><\/p>\n<p><em><strong>This article was contributed by Kaustub Rao, a lead data analyst at Instamojo. If you have questions or want to give Kaustub kudos, <a href=\"https:\/\/www.linkedin.com\/in\/kaustub-rao-b13480a5\/\">connect with him on LinkedIn<\/a> or comment on this post.<\/strong><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Hundreds of small businesses sign up on Instamojo every day. As a data analyst in the team, one&hellip;\n","protected":false},"author":6,"featured_media":15017,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[6],"tags":[3903,3902,1850,3904],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Machine Learning Concept That Simplified Lead Scoring At Instamojo<\/title>\n<meta name=\"description\" content=\"See how the data team at Instamojo built a lead scoring mechanism to identify high potential leads and help the sales team achieve revenue goals.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.instamojo.com\/blog\/machine-learning-concept-that-simplified-lead-scoring-at-instamojo\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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