Two things get called scoring and they solve different problems.<\/p>\n
Pipeline scoring<\/strong> ranks deals already being worked \u2014 opportunities with a value, a contact and some history.<\/p>\n Lead scoring<\/strong> happens earlier. An enquiry has just arrived. Nobody has spoken to them. The question is: is this worth a call, and how soon?<\/strong><\/p>\n This article is about the second one. If your problem is prioritising open deals, that is a different exercise.<\/p>\n Most businesses that market at all get more enquiries than they can properly follow up.<\/p>\n Some are serious buyers. Some are students doing research. Some are competitors. Some are people who downloaded a guide and have no intention of buying anything.<\/p>\n Without a filter, one of two things happens. Either everything gets called, which wastes your sales team’s time. Or the newest ones get called and older enquiries quietly rot, including good ones.<\/p>\n Lead scoring is a triage tool.<\/strong> It decides what gets attention first.<\/p>\n FIGURE 1: WHERE SCORING SITS<\/p>\n Enquiry arrives<\/p>\n Scored<\/p>\n Prioritised<\/p>\n Salesperson decides<\/p>\n Two categories, and both matter.<\/p>\n The second category is usually more predictive than the first.<\/strong> A junior person from a small company who has read your pricing page three times is often a better lead than a director who filled in a form once and never came back.<\/p>\n Behaviour is intent. Profile is only eligibility.<\/p>\n FIGURE 2: TWO KINDS OF SIGNAL<\/p>\n Who they are<\/p>\n What they did<\/p>\n You need enough converted leads to learn from.<\/strong><\/p>\n A model trained on a few dozen conversions is not learning your business. It is finding coincidences in a small sample and presenting them with confidence.<\/p>\n Two other things matter as much:<\/p>\n You must record what happened to leads that did not convert.<\/strong> If unqualified leads are deleted or left sitting forever, half the training data is missing. The model needs to see failures.<\/p>\n Your lead source data must be accurate.<\/strong> If half your leads are tagged “website” because nobody set up proper source tracking, the strongest available signal is unusable.<\/p>\n Ordering the follow-up queue.<\/strong> The main use. Which five enquiries deserve a call this morning.<\/p>\n Speed on the good ones.<\/strong> Response time matters enormously in most markets. A leadscored highly and called within the hour converts far better than the same lead called in three days.<\/p>\n Routing.<\/strong> Sending enterprise-shaped enquiries to a senior salesperson and smaller ones to a lighter-touch process.<\/p>\n Deciding what to nurture rather than call.<\/strong> A lead that is not ready is not a bad lead. Scoring helps separate “not now” from “not ever”.<\/p>\n Deciding who to ignore entirely.<\/strong> A low score means “less like your past conversions”. Some of your best customers will not look like your past customers.<\/p>\n Judging fit.<\/strong> A score cannot tell you whether you can actually serve this customer well. That is a human question and sometimes the more important one.<\/p>\n Working without volume.<\/strong> If you get thirty leads a month, sort them yourself. The overhead of a scoring model exceeds the benefit.<\/p>\n FIGURE 3: WHAT LEAD SCORING IS FOR<\/p>\n Good use<\/p>\n Misuse<\/p>\n The same self-fulfilling problem appears here as in pipeline scoring, and it is worth repeating because it is easy to miss.<\/p>\n Low-scored leads get no attention. They fail to convert \u2014 because nobody called them. The model observes this and becomes more confident that leads like that do not convert.<\/p>\n The score makes itself right.<\/strong><\/p>\n Two safeguards:<\/p>\n Work a deliberate sample of low-scored leads.<\/strong> Not all of them. Enough to keep the data honest and occasionally find something.<\/p>\n Check calibration quarterly.<\/strong> Of the leads scored highly, what proportion converted? Of the low-scored ones you did work, what proportion converted? If those numbers do not match what the score implied, it is not learning your business properly.<\/p>\n An uncomfortable point, and an important one.<\/p>\n For most businesses, response speed matters more than scoring accuracy<\/strong>.<\/p>\n A mediocre score with a one-hour response will beat an excellent score with a three-day response, in almost every market. Leads go cold fast, and the first credible response often wins.<\/p>\n If you are choosing where to put effort, fix response time first. Scoring helps you decide the order within an hour. It does not help if the hour is actually a week.<\/p>\n 1. Fix source tracking.<\/strong> Know where leads actually come from. This is the strongest single signal and it is frequently broken.<\/p>\n 2. Record outcomes properly.<\/strong> Converted, or not converted with a reason. Both are training data.<\/p>\n 3. Measure your baseline.<\/strong> Conversion rate by source, and average response time.<\/p>\n 4. Run it silently for a quarter.<\/strong> Score leads without showing anyone. Then check whether high-scored leads actually converted more.<\/p>\n 5. Present it as an order, not a number.<\/strong> A percentage next to a company name invites false confidence and arguments. “Suggested priority” is honest about what it is.<\/p>\n 6. Keep working some low scores.<\/strong> Permanently.<\/p>\n Conversion rate.<\/strong> Are more of the leads you work converting?<\/p>\n Response time on high-value leads.<\/strong> Getting to good leads faster is the main mechanism.<\/p>\n Wasted calls.<\/strong> Time spent on enquiries that were never going to buy.<\/p>\n Leads left untouched.<\/strong> Watch this. If it grows, you are not triaging \u2014 you are abandoning.<\/p>\n Lead scoring is triage. Used to order a follow-up queue, it saves real time and gets your team to good enquiries faster.<\/p>\n It needs volume, recorded outcomes, and accurate source data<\/strong>. Without those it is a confident guess.<\/p>\n And before investing in it: check your response time<\/strong>. For most businesses that is the bigger lever, and no amount of scoring compensates for calling three days late.<\/p>\n Getting more enquiries than your team can follow up properly?<\/p>\n Get in touch. We will look at your response times and your source tracking first \u2014 that is usually where the conversion is being lost.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":" A different problem from pipeline scoring Two things get called scoring and they solve different problems. Pipeline scoring ranks deals already being worked \u2014 opportunities with a value, a contact and some history. Lead scoring happens earlier. An enquiry has just arrived. Nobody has spoken to them. The question is: is this worth a call, […]<\/p>\n","protected":false},"author":3,"featured_media":9178,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-9177","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"\nThe problem it solves<\/h2>\n
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Who they are<\/h3>\n
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The requirement, again<\/h2>\n
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The trap, once more<\/h2>\n
What actually moves the needle<\/h2>\n
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