{"id":20587,"date":"2015-12-22T10:00:04","date_gmt":"2015-12-22T15:00:04","guid":{"rendered":"https:\/\/crazyeggblog.wpengine.com\/?p=20587"},"modified":"2026-01-06T15:15:14","modified_gmt":"2026-01-06T23:15:14","slug":"secret-ingredient-lead-scoring","status":"publish","type":"post","link":"https:\/\/www.crazyegg.com\/blog\/secret-ingredient-lead-scoring\/","title":{"rendered":"Web Analytics: The Secret Ingredient in Lead Scoring"},"content":{"rendered":"

Lead scoring is the ultimate conversion optimization<\/a> hack: if you want to make sure leads get delivered to your sales team on time and that they\u2019re truly qualified, you need to standardize the conditions of the hand-off. Call it automated quality assurance.<\/p>\n

A high score means prospects are very interested and intend to buy; a low score means they aren\u2019t. Simple, right?<\/p>\n

<\/p>\n

If you\u2019ve done any work with lead scoring, you know that isn\u2019t true. B2B marketers constantly test and adjust their scoring criteria as they learn more about the buying process and what factors correlate with higher conversion rates. It\u2019s a never-ending struggle.<\/p>\n

If your lead scoring consistently disappoints, you might be neglecting one of the lesser-known, but critical, components of lead scoring \u2013 web analytics. It\u2019s time to use web analytics to work smarter, not harder.<\/p>\n

Scoring Without Web Analytics<\/h2>\n

Although it\u2019s a fairly advanced (and new) marketing automation skill, lead scoring has already gained serious clout. According to a report<\/a> by Lattice and Decision Tree Labs, 44% of B2B companies use a lead scoring system.<\/p>\n

\"utilizing<\/a><\/p>\n

But, of that group, less than half use web analytics as a scoring criterion. That might explain why 59% say \u201cincomplete or inconsistent prospect data\u201d shakes their confidence in scoring, and 43% say the same about a lack of \u201cinsight into which attributes indicate buying behavior.\u201d<\/p>\n

A lot of marketers get hung up on firmographics (company name, industry, etc.) and BANT (budget, authority, need, timeline) without realizing:<\/p>\n

    \n
  1. Even these data points take a long time to collect without web analytics.<\/li>\n
  2. They\u2019re missing out on a deeper level of insight that makes lead scoring truly valuable.<\/li>\n<\/ol>\n

    When scoring is too elementary or unfairly skewed, your sales team ultimately takes the hit. They waste time following up with leads shipped over from your marketing automation platform that are nowhere near qualified. According to SiriusDecisions, only 40% of salespeople<\/a> say they find lead scoring valuable.<\/p>\n

    To be effective, your scoring framework must incorporate both explicit and implicit data about your prospects \u2014 not only who they are, but how they behave. Web analytics does both.<\/p>\n

    Let\u2019s take a look.<\/p>\n

    Identify Your Prospects<\/h2>\n

    Identifying prospects is where the \u201cwho they are\u201d comes into play. In the old days (like 5 years ago), you had to wait for prospects to fill out a web form on your site to collect basic demographic and firmographic information.<\/p>\n

    This is still a valid approach, but it\u2019s not the only way. Think about it like this: if you\u2019re trying to figure out what fish are in a body of water, you could drop a hook and wait for the fish to bite, or you could use a sonar transducer (i.e., \u201cfish finder\u201d) to scan the lakebed and provide real-time visuals.<\/p>\n

    That\u2019s kind of what web analytics do \u2014 or at least that\u2019s one of their capabilities. It\u2019s called reverse IP lookup<\/b>, which means using the IP address of your site visitors to identify the company they work for and geographic location. Typically, you\u2019ll need a marketing automation platform or a dedicated web analytics app to get this kind of visibility because Google Analytics provides only an overview.<\/p>\n

    \"companies<\/a><\/p>\n

    An example of reverse IP lookup in Act-On: site visits from users at three separate companies, by location<\/i><\/p>\n

    As you collect more information about prospects, you can match names with organizations, decide how each company fits into your target market, and assign points based on the number of hits you\u2019ve received from that company.<\/p>\n

    Use Digital Body Language to Paint a Clear Picture<\/h2>\n

    Firmographic data is a necessary part of the vetting process, but it doesn\u2019t always indicate interest or intent. Let\u2019s say you\u2019re an accounting software vendor and you see that a prospect from a mid-size law firm in Boise is visiting your site. Based on previously collected information, you know the prospect\u2019s name and job title, and everything seems to be a good fit.<\/p>\n

    Not so fast. Just because they land on your site and match a persona doesn\u2019t make them a qualified lead. You need to know whether or not the prospect is interested in your product and whether they intend to purchase an accounting solution in the near future.<\/p>\n

    The answer to these questions can be found in the behavioral data provided by (you guessed it) web analytics. This is where the \u201chow they behave\u201d comes into play. By tracking how prospects engage with your website and digital content, you can determine where they are in the buying process. This collection of data is often referred to as digital body language<\/b> or DBL.<\/p>\n

    To collect digital body language, your marketing automation platform (MAP) assigns each prospect a unique ID and records their actions using tracking cookies. Your job is to assign point values to each digital action based on how it indicates interest and intent.<\/p>\n

    For example, downloading a how-to guide about implementing your solution indicates fairly strong interest, so you might assign it 15 points. Reading a blog post, on the other hand, indicates only passive interest, so you might assign it 1 or 2 points.<\/p>\n

    \"acton<\/a><\/p>\n

    As you can imagine, there are a lot of different factors that go into behavioral lead scoring. Here are some examples of common web activities you can track and score:<\/p>\n

    1. Pages Visited<\/h3>\n

    Google Analytics will show you which pages on your site receive traffic during a specified date range. But, if you combine web analytics with marketing automation, you can identify specific users and log which pages they view.<\/p>\n

    \"Jonathan<\/a><\/p>\n

    In the above example, a prospect made 1 visit to our site on November 10 and viewed 6 different pages. These page visits in themselves were not enough to score points, but they did lead to a whitepaper download, which we\u2019ll address in a moment.<\/p>\n

    In addition to tracking views, some software products let you drill down even further to see how prospects interact with a page. For example, \u201cheat mapping\u201d software shows you where a visitor scrolled, what they read, and what they clicked on, even if it doesn\u2019t link to a new page.<\/p>\n

    \"bitcurrent<\/a><\/p>\n

    This \u201cconfetti\u201d map from CrazyEgg shows page clicks segmented by referral sources, search terms, and more.<\/i><\/p>\n

    If your lead scores are in a slump, it could be that visitors aren\u2019t clicking on the right pages and CTAs. Use a tool like CrazyEgg<\/a> to see which areas of your site are performing below standard.<\/p>\n

    2. Downloads \/ Form Completions<\/h3>\n

    Using a similar back-end process, you can track which gated assets your prospects download (\u201cgated\u201d meaning the content is tucked behind a web form). Common examples include:<\/p>\n