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Approximately 96% of your website’s visitors are not ready to buy. The odds are stacked against you. If you attempt to convert every visitor that comes to your website, for every dollar you spend trying to convert them, 96 cents are wasted. So how do you know which leads to pursue and which to ignore? The answer is lead scoring.
We’re here to help guide you through the fundamentals of scoring leads and how to create an effective lead scoring model that helps find purchase-ready customers for your company.
Lead scoring is a data-driven ranking system used by marketing teams to determine a lead’s buying potential. While every company has its own lead scoring model (e.g., letter grades, number values, percentages), each model is designed to give a “lead score” that helps companies distinguish between how hot or cold a lead is.
A higher lead score means the lead is closer to becoming a customer, while a lower lead score means they’re less likely to become a customer. The ultimate goal is to determine all the potentially sales-ready leads through the lead scoring process.
Lead scoring allows marketing teams to more accurately rank their leads based on different data points. These rankings help marketing teams spend more wisely. Lead scoring prevents wasted marketing spend by redirecting precious working hours to customers with the most likely ROI.
Lead scoring prevents wasted marketing spend by redirecting precious working hours to customers with the most likely ROI. Click To Tweet
If your marketing team knows which leads are more likely to make a purchase, they can make decisions to target the best leads. Once you know which customers are most likely to make a purchase, you can also direct your marketing and lead nurturing efforts toward them.
Since lead scoring is a data-driven ranking system, you need to know what types of data point values you should be tracking. There are generally two types of lead scoring data to consider: explicit and implicit.
Explicit data encompasses information that you’ve collected from a lead, generally information they’ve provided through forms, phone calls, emails, etc. This data includes things like an individual’s job title, salary, location, and even their company’s size and industry.
Implicit data looks at different user activities and behaviors. Tracking information like a lead’s level of email, social media, or website engagement is important because it can tell you what activities and behaviors purchase-ready customers have in common.
Are they reading certain web pages longer? Are they commenting on social media channels? Are they opening company emails regularly? If a lead has a high level of engagement with your company, you can use that knowledge to target them more aggressively to make a sale because they’re already showing interest in your product, service or brand on some level.
Implicit data also includes negative data. If leads are unsubscribing from your newsletter or not responding to your marketing email offers, it means you’re dealing with a less engaged lead who is less likely to make a purchase.
For B2B marketers, an example of negative data might be someone who isn’t a company decision-maker and, therefore, less likely to make a purchase. That’s why you should also be keeping a close eye on the decision-makers at companies you’re targeting — a junior finance assistant may be an influencer or end user of a new product or service, but will have less authority to close a deal than a CFO.
The simplest way to score your leads is by setting different thresholds and assigning points to each, which will result in a ranking.
Let’s say you want your leads to come from large companies, and you’ve determined certain actions that a lead may perform on your website are more likely to convert. You can create a lead scoring model divided into three thresholds: hot, warm, and cold. A target persona from a company with 250-plus employees that downloads an eBook from your site may be considered “cold.” Someone in the same role at a similar sized company that schedules a demo of your product, however, may be considered “hot.”
Once you’ve decided on your thresholds, you can then assign points to each. For example, the target persona at a company with 250-plus employees that scheduled a demo would get a 100-point score if you’re using a 100-point system.
Below is a lead scoring model example put together by ActiveCampaign. They’ve attributed a certain amount of points to each type of lead using a 100-point system: 100 for sales qualified leads (SQLs), 75 for marketing qualified leads (MQLs), 50 for more general leads, and 10 for prospects.
While it’s obvious that the leads with 100 points are purchase-ready, it doesn’t necessarily mean the ones that don’t currently have 100 points can’t achieve that status at some point. Over time, a prospect, lead, or MQL can become an SQL and move up the rankings based on their actions. More engagement, such as email opens, social media comments, or eBook downloads, can move a lead up the rankings as well because it shows increasing interest.
Generally speaking, there are two ways to create a lead scoring model: traditional and predictive.
Traditional lead scoring refers to when a marketing team manually assigns scores to leads and ranks prospects to determine how hot or cold they are. The decision is made based on a comprehensive list of the explicit and implicit data.
The downside to traditional lead scoring is that it’s often seen as a solution that doesn’t necessarily track down great leads, but simply eliminates bad ones. That’s because traditional lead scoring is based on subjective opinions about what great leads and bad leads look like. It’s much easier to find leads who definitely aren’t going to make a purchase; it takes far more effort to find the great ones because there’s generally less data available about them.
While eliminating bad leads is a good starting point, it doesn’t achieve the ultimate goal of lead scoring — finding purchase-ready customers. It’s also a more time-consuming process that may not be as effective for lean marketing teams.
Predictive lead scoring is done using lead scoring software. This software implements algorithmic tools to automatically track different data points that identify purchase probability tracking patterns through conversion data. They also use marketing automation and machine learning to better predict what part of the sales funnel your leads are in and even get smarter over time to understand purchase motivations better.
Lead scoring software takes all heavy lifting of lead scoring out of the equation. You don’t have to worry about manually assessing all your potential leads and making a human error. The machine learning functionality of lead scoring software can tell you the good and bad leads based on how likely they are to become a customer. You can then deploy sales reps to follow up with the ones who are pre-qualified by the software.
However, this software does come at a cost. If you want to get the most out of your lead scoring software and all the best features, you may be looking at a significant monthly investment. Depending on how much you’re willing to spend, it may not be the most viable option for your company.
Lead Scoring Software You Can Try
Once you’ve scored all your leads, you can create better lead generation strategies that focuses on sending the right message to leads that are in different stages of getting to know your brand and what you offer. That can mean anything from offering gated content like an eBook or a research report to cold leads, or marketing events like webinars and workshops to warm and hot leads.
To come up with a lead generation strategy, you need to understand your target customer’s pain points. For example, if your target customer is looking to build up their in-house marketing team and streamline their SEO process with intuitive software, you could use an eBook to provide value on “The must-have marketing toolkit when scaling your marketing team.” The goal here is to nurture prospects into leads. By creating content that adds value, credibility, and trust, you are nurturing leads to find out more about your product or service.
It’s also important to use gated content such as an eBook to get the contact information of your leads. This allows you to add them to an email workflow to continue nurturing them toward a sales call or purchase. Doing so gives you the control of being able to communicate directly with leads — giving you more insight and data.
In essence, lead scoring comes down to customer psychology. Using distinct data points, you’re trying to understand as much about quality leads as possible. That information will be the difference between knowing who’s ready to make a purchase and who isn’t.
Alexa can also help with that. Our Target Audience Analysis tools identify keywords your customers use when they’re ready to make a purchase, and can help you find topics that make them more likely to convert. Try all of our tools free for 14 days with the Advanced Plan.
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