And that\u2019s when you meet your first challenge: determining what your prospects should get points for and how to collect data and track the whole game; enter lead scoring. There are loads of lead scoring models to choose from \u2013 let\u2019s walk you through some examples to help you get inspired.<\/p>\n
What is lead scoring and what are the benefits?<\/h2>\n
Lead scoring is a system that lets you evaluate a prospect’s likelihood to become a lead and a lead’s readiness to turn into a customer. Like a character\u2019s class in a game, lead scoring models build on a variety of metrics and checkpoints with allocated numerical values \u2013 much like the points a player collects in a game.<\/p>\n
![\"Lead](\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2022\/04\/lead-scoring-definition.png\")
<\/p>\n
The game begins when visitors enter your site. Add prospects that you identify as a good fit and want to target actively. Once you’ve figured out what earns them points, you can start reaping the benefits of an improved lead generation process.<\/p>\n
Not only does lead scoring let you identify high-quality leads, it allows you to nurture them through the sales funnel<\/a>. This method helps you accurately define the funnel and create relevant content for various types of leads in varying funnel stages. In other words, you can stop wasting time and effort on leads with low potential. As a sales team, you receive truly qualified leads because SQL-status (sales-qualified lead) is measurable.<\/p>\n Different lead-scoring models prioritize different aspects. This makes sense because not all criteria carry the same weight for every business.<\/p>\n What does that mean? For some products, like cosmetics or footwear, gender, for example, is highly significant in determining whether a lead is a good fit or not. When it comes to something like a food-delivery service, however, that really doesn’t matter much. Instead, the location of the visitor may be significantly more critical in this case.<\/p>\n But audience demographics<\/a> are only one part of the picture and may not even be the most determining factor. Lead scoring models are typically a co-op made of demographics and other parameters, such as:<\/p>\n This is more than just a good tip. If you want your lead scoring system to be effective, marketing and sales teams need to collaborate. Both need to agree on the metrics to track and attribute scores to. As the sales team, you\u2019re closer to the actual customer and can help the marketing team<\/a> understand what to look out for in the early stages.<\/p>\n For example, the marketing manager has no way of knowing if the purchasing decision is usually made by a team leader or an executive, as it\u2019s not in their field of view. They passed them as SQLs a long time ago.<\/p>\n Investigate the journey existing customers took until they became customers. How long did it take? How frequently did they engage, and how many engagements took place before they made the purchasing decision? The teams should decide on the scoring threshold that establishes SQL status where a lead moves from marketing to sales.<\/p>\n Over time, leads will collect more and more points for behavior. Be aware that among them, there may be visitors who never intend to make a purchase. A freelancer won\u2019t acquire a complex marketing automation tool, but may still read your blog, for example. A medical student will visit your medical devices site for research, not because they need your equipment. And don’t forget, your competition is keen on knowing what you’re up to and may pop in frequently.<\/p>\n Set negative scores when you identify such visitors. Try to think of factors or behavior that show this isn’t a potential customer. For example, anyone visiting from a country to which you don’t sell could have 10 points reduced from their score. A competitor gets -20 and so on.<\/p>\n Find out which marketing activities or specific online behavior lead to sales. Again, this is where marketing and sales need to work together closely. By analyzing attribution channels, sales can identify successful marketing tactics<\/a> and help marketing allocate the appropriate numeric value to the scoring model.<\/p>\n This can also include content that you don’t own. For example, a large percentage of your customers could be using a related tool that made them realize your solution’s additional value. Or maybe they read a specific scientific article that featured your company. However they came across your name, it\u2019s worth looking into.<\/p>\n The action doesn\u2019t stop there. Keep monitoring how well your scoring system works after you\u2019ve built it. Get input from your customer success team and compare data from actual customers’ demographics to that in your lead scoring system. Keep your hands on the joystick. Lead behavior could change over time.<\/p>\n For example, as your marketing becomes more effective, the lead cycle may become shorter. Or there could be a trend towards social media engagement of potential customers. To keep getting winning results, you need to track and tweak your scoring accordingly.<\/p>\n The fact is, you\u2019ll collect a lot of data on a lot of leads. Duplicate data or outdated information can mess up lead scoring, and you’ll end up working harder with a lower outcome. For example, someone could be visiting your site using different devices, and you may end up creating two leads in your CRM for the same person when they sign up \u2013 not ideal. Your CRM should always be updated with the latest data.<\/p>\n Each lead scoring system is unique. First, you need to define your B2B buyer persona<\/a> profile to pick which demographic factors make them a good fit. You also want to analyze sales engagement<\/a> data of potential and existing customers. Get a good understanding of the buyer journey and identify behavioral patterns that signal a high-interest level.<\/p>\n Let\u2019s look at some lead scoring examples that rely on similar models.<\/p>\n We’ll start by looking at a relatively simple demographic lead scoring model. Looking at this example, you’ll understand the complexity of determining what matters most and how various criteria affect the evaluation.<\/p>\n The idea here is to determine the best fit based on information collected directly from the lead. B2B companies often use sign-up or download forms.<\/p>\n The list can go on, and you would allocate the number of points according to relevance to your product and target audience<\/a>. In the above example, the company offers something that is most useful for fairly large financial companies and targets high-level executives.<\/p>\n It’s a numbers game that you\u2019ll want to think through to the end. For example, should a CEO of a company of 800 employees, but in an industry for which your product is of little interest get a higher score than a system administrator in an insurance company with over 1000 employees?<\/p>\n To make life easier, companies use a broader lead segmentation. For example, leads with a demographic score of 3-10 points are not fitting, between 11- 25 points are somewhat fitting, 25-30 are a good fit, and above that are the best fit.<\/p>\n Now let’s apply the scoring idea to engagement-related website metrics<\/a>. Based on your marketing strategy and content funnel, you’ll assign the highest score to the type of engagement that reflects the lead is close to the purchasing decision. This is where the game begins.<\/p>\n You can add any type of touchpoint or engagement with your content. A company could, for example, raise the score for visitors who remain more than average. The numerical value you choose for each type of engagement is up to you. You need to ask yourself how valuable it is compared to other data you collect and assign points to.<\/p>\n Combining the scores will get you a more accurate picture and allow your leads to level up in the game by collecting more points. Put the demographic score on the long axis and the behavior score on the vertical axis.<\/p>\n This gives you a matrix that lets you evaluate the likelihood of leads turning into customers. Leads who appear on the upper right of the graph are most likely to become customers; leads on the lower left are the least likely.<\/p>\n You can now apply the traditional dead, cold, warm, hot lead structure to the matrix.<\/p>\n There are different variations of this type of matrix. Instead of behavior, you could set scores for interest level. Here, you\u2019d give out points for specific websites that indicate interest in your product, including competitor sites. Prospects could be members of a community that represents the same value as your brand, i.e., sustainability or diversity, for example.<\/p>\n If you have several leads from one company, this could earn it additional points. That in mind, leads who engage more frequently on social media could earn a higher score. Analyze the behavior of existing customers to determine which behavior is significant.<\/p>\n In this combination, you try to determine the prospect’s need for your product. You could also call this a pain score. This model is less common because it’s a lot harder to determine how much a person or company needs something \u2013 there\u2019s no one metric to measure it. You would first have to define what could represent or indicate a stronger need. This can be very similar or even overlap with interest level.<\/p>\n If you think you have enough information to start creating your lead scoring game, make sure you also have accurate data and analysis to support the scoring system. From generating a list of prospects and creating a buyer persona<\/a> profile to measuring engagement metrics and monitoring online behavior, Similarweb Sales Intelligence<\/a> boosts your performance at every level.<\/p>\n What should you measure in your lead scoring model?<\/h2>\n
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5 tips for building an efficient lead scoring model<\/h2>\n
Make it a joint effort of marketing and sales<\/h3>\n
Add negative scores<\/h3>\n
Analyze attribution<\/h3>\n
Optimize your scoring system<\/h3>\n
Maintain a tidy database<\/h3>\n
5 lead scoring examples<\/h2>\n
1. Demographic lead scoring model<\/h3>\n
![\"A](\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2022\/04\/lead-scoring-table-scaled.png\")
<\/p>\n2. Website data-based lead scoring model<\/h3>\n
![\"Website](\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2022\/04\/lead-scoring-game-scaled.png\")
<\/p>\n3. Combined lead scoring model – demographics and behavior<\/h3>\n
![\"Lead](\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2022\/04\/lead-scoring-heat-map-scaled.png\")
<\/p>\n4. Combined lead scoring model – interest and fit<\/h3>\n
5. Combined lead scoring model – need and fit<\/h3>\n
Ready to start the game?<\/h2>\n