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Lead Scoring

Lead scoring can help make it clear when a visitor intends to take a specific action or assist your sales team by preparing marketing qualified leads.

What is lead scoring?

Lead scoring is a method of ranking sales leads in order to understand where leads are in the sales cycle.

Scores are based on multiple attributes to gauge how interested the prospective client is in your business and where they fall in terms of sales readiness. Scoring sales leads helps a business determine how to prioritize and respond to leads on a case-by-case basis so that prospective clients are more likely to complete the sales cycle, increasing business for the company.

Lead scoring models are unique to each company because the values assigned to each attribute will vary by business.

What information is used to score leads?

Leads are scored by using a combination of demographic and behavioral information. Demographic information, sometimes called explicit information, is supplied to you by the prospect.

An easy way to think of demographic information is to remember that it includes all the information that determines how interested you are in the lead, rather than the other way around.

For example, if you are a B2C company that only operates in the Midwest, you would assign a higher value to a consumer who lives in Missouri than one who lives in Florida, outside your area of operation. You can collect demographic information by using landing page forms, gated content, or tracking registration information for events.

Behavioral information, or implicit information, is used to measure how interested the lead is in you. And since leads aren’t always forthcoming about their interest level, you have to gather this information using analytics, reporting tools, or a customer relationship manager (CRM) system.

Scoring behavioral information is trickier than demographic information. For example, someone who visited your website twice in one week should not be given the same score as someone who visited your website the same number of times over a span of two months.

Frequency of visiting your website is not the only attribute to consider though. It also matters which pages of the site they accessed. Someone who is only looking at your careers page is less likely to be a sales lead than someone who visited your blog, so even if the job hunter visited your careers page ten times, he shouldn’t outscore someone who visited your blog page only twice. 

Why should every business have a lead scoring model? 

Lead scoring is not a practice only best suited for a certain type of business. Lead scoring can increase sales and benefit every company, regardless of industry, size, or customer base.

Without any sort of method to determine where your sales leads fall in the sales cycle, you have no way to know which leads need to be actively pursued versus nurtured a while longer.

When you understand where a lead is in the sales cycle, you have a clearer path to close that sale. Scoring leads also increases the efficiency of your sales team by cutting down on resources spent pursuing sales that aren’t far enough along in the sales cycle or that don’t belong in the demographic you’re targeting.

Seems simple enough, right? Here comes the hardest part: developing a model.

How do you develop a lead scoring model?

First things first! You need to know your current lead-to-customer conversion rate. This can be calculated by dividing your number of new customers by your total number of leads. Once you know your conversion rate, you can use this as a baseline to gauge how effective your lead scoring model is down the line and make adjustments as needed.

Next, identify the attributes you believe make up a quality lead.

  • What do your existing customers have in common?
  • What does your buyer persona look like?

Leads with those attributes should be the highest scoring. Assign numerical values to attributes based on the quality of lead those attributes suggest. If you aren’t sure whether or not an attribute deserves a high score, you can calculate the close rate of individual attributes by slightly modifying the formula you used to find your lead-to-customer conversion rate.

Remember to score demographic attributes as well as behavioral ones, and don’t be afraid to assign low or even negative values when necessary.

Once sales leads are scored by all their attributes, you can respond to them based on their lead score.

Maybe a score of 70 points or higher is considered sales-ready and a higher focus should be placed on converting those leads into customers. Leads with scores below 70 points would be nurtured until their scores increased to the sales-ready threshold, at which point you can pursue them as customers with a much higher likelihood of success. This streamlines your sales process and reduces the amount of time and resources wasted on leads that weren’t viable at the time.

Over time, you can use the lead-to-customer conversion rate formula to refine your scoring, increase your conversion rate, and continue to cut out inefficiencies in your sales process. 

Why should you hire Carefully Crafted to score your business leads?

Lead scoring is becoming more and more of a necessity in order to evaluate a lead’s sales readiness in a digital age, but developing a lead scoring model can be a frustrating process that takes a lot of time.

As a business owner, you probably don’t have much time to spare! We also understand business owners have to juggle competing priorities, looming deadlines, and a host of other responsibilities every single day.

We have the know-how to score your leads manually, but we also have the technology to score them predictively. Our predictive approach uses machine learning to sort through attributes and identify not just what your customers and best leads have in common, but also what your leads who don’t become customers have in common.

Let us crunch the numbers for you and develop a lead scoring model that allows you to maximize your leads and minimize inefficiency without the stress of adding another task to your to-do list. 

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