Article

The best lead scoring models have these seven factors

By Josh Bean, Director, Marketing

Published February 20, 2020
Last modified February 20, 2020

A business can’t thrive without lead generation. However, the more leads you generate, the more selective you have to be in your pursuits. Sales reps don’t want to waste time chasing a long list of dead-end leads. That time could be spent nurturing more promising leads.

Yet, when it comes to valuing leads, how do you separate the wheat from the chaff? Experience and gut instinct go a long way, but they aren’t enough. To consistently find strong potential customers, sales reps need a lead scoring model.

What the best lead scoring models have in common

A lead scoring model is a system for evaluating leads. You give points to a lead based on a number of different factors, such as the industry in which the lead works or their level of interest in your product. Qualities associated with past high-value leads are given more points.

With this model, you’re able to quickly identify leads that are the ripest for a potential sale and which leads should be considered a low priority. We’ve already examined how to score leads. Here we’ll look at the seven factors that all robust lead scoring models have in common.

1. Alignment between Marketing and Sales

If Marketing and Sales aren’t on the same page about your lead scoring model, some great leads might fall through the cracks. Or, some not-so-great leads that were mistakenly considered qualified will make their way through, sending sales reps on a wild good chase.

To ensure that there are no leaks in your sales funnel, ask Sales and Marketing to work together to develop the scoring criteria and lead scoring threshold (see the section below) for your model. If any team member makes changes to the model, communicate these updates clearly to both departments. With this communication, your Marketing department will be able to identify strong prospects and refer them to Sales.

2. A lead scoring threshold

A lead scoring threshold refers to the points value where a prospect is considered sales ready. When a lead’s score reaches or exceeds this amount, they become a marketing qualified lead, or MQL, and are passed from Marketing to Sales.

It’s important to get your threshold right. If the bar for entry is too low and leads are being qualified prematurely, sales reps will have a frustrating time going after prospects that aren’t ready to be pursued. But raise the bar too high and you risk sitting on valuable leads for too long and giving them time to be snatched up by a competitor.

You should determine your threshold in part by looking at what historical data has told you about which characteristics (or combination of characteristics) mark a lead as qualified. For example, if requesting a product demo is the top indicator that a lead will eventually be converted into a sale, your lead scoring threshold should be set so that any lead who requests a demo will be assigned enough points to immediately become an MQL.

Once you’ve set a threshold, you can set up your CRM to automatically notify you when a lead receives the requisite number of points.

3. Explicit scoring

With explicit scoring, you assign points to a lead based on specific objective qualities, such as firmographic or demographic details. Examples of explicit characteristics include:

  • Job Title
  • Role
  • Level of seniority
  • Experience in the industry
  • Industry
  • Company size
  • Company revenue
  • Geographical location

These clear-cut factors offer a simple way to evaluate leads. For example, if your ideal customer is a C-suite executive from a large tech company, you can check leads’ company sizes and industries to see if they are a good fit.

Sometimes, a lead will volunteer the information that you need for explicit scoring – for example, filling in a questionnaire to download gated content from your website. Or the information may be uncovered through research, which could involve checking a prospect’s LinkedIn page or company website.

4. Implicit scoring

On the other hand, implicit scoring refers to the points awarded to a lead based on their behaviour, such as:

  • Website visits
  • Social media interactions
  • Email opens/clicks
  • Newsletter subscriptions
  • Contact requests
  • Contact form submissions
  • Content downloads
  • Webinar
  • Free trials/product demos

Say, for example, that someone downloads an e-book from your company. You should award points for the very act of downloading the e-book, as you can infer from that interaction that the prospect has a certain level of interest in your company.

You can use CRM to track every interaction a customer has with your company.

Implicit scoring often contributes more to a lead’s overall score than explicit scoring. A prospect can only be scored once for their job title, but will be scored every time that they download a piece of content or open an email.

5. Negative scoring

Not every interaction that a prospect has with your company is a step in the buyer’s journey, and your lead scoring model needs to recognise this. Negative scoring is a way of removing points from a lead score based on actions or characteristics that indicate a waning or complete lack of interest, which could include:

  • Unsubscribing from your email list
  • Visiting your careers page (implying that they are interested in becoming an employee, not a customer)
  • A job title (such as 'student' or 'retired') or industry that has nothing to do with your product or service, suggesting that they are interested in your content for purely academic reasons
  • A rival company (suggesting that the person is just researching the competition)

Negative behaviour is especially important for avoiding deceptively high lead scores. A lead seems to have a healthy score based on their qualities, such as their industry, but their actions show that they’re increasingly losing interest in your brand. With negative scoring, sales reps can recognise these weak leads and focus on nurturing stronger potential customers instead.

Marketing and Sales must work together to create a list of all the red flags that indicate a prospect that isn’t likely to convert. Both departments should have valuable insights, and collaborating can help them remain aligned. Assign a negative points value to each of the traits and actions, based on how common they are across past leads who have left your pipeline, and dock a lead points when they display the characteristics or behaviour in question.

6. Score degradation

When it comes to prospect interactions, no news is bad news. Ideally, you want to see leads moving through your sales funnel, not getting stuck at a certain stage and never progressing.

Score degradation helps you track stagnant leads. You lower a lead’s score if they haven’t interacted with your brand for a significant period of time. For example, you might lower a lead’s score if they stop opening your company’s emails or download one piece of content but never interact with the site again. Like negative scoring, score degradation helps you pick out the bad eggs and focus on more valuable leads.

To use score degradation, decide which actions should warrant point deductions when the lead stops doing them. As a starting point, consider reversing the points system that you use for implicit scoring. If a lead gets 10 points for subscribing to your newsletter, they should lose 10 points for unsubscribing. Or, consult your marketing team to understand which actions promising leads perform consistently.

7. Regular refinement

The motto of a good lead scoring model is not 'Set it and forget it' – it’s 'Design it and refine it'.

To keep your lead scoring model as accurate as possible, update your scoring methods continually, based on the most recent customer data. For example, if a lot of leads are qualifying but very few are being converted by the Sales department, it’s very likely that the lead scoring threshold is too low. Or, if you notice that sales from one type of persona or prospect's behaviour are increasing or decreasing, the way you weigh these variables may need to be changed.

How do you know when you need to update your lead scoring model? Check to see whether your MQL-to-conversion rate is declining. If it is, there’s a good chance that your target customer profile has shifted and your scoring model needs to be adjusted.

Making the most of your lead scoring model

Lead scoring allows sales reps to work smarter, not harder, when it comes to pursuing prospects. Focusing solely on qualified leads saves you the time and frustration that comes from trying to contact and convert prospects that just aren’t ready, and perhaps never will be. But remember, a lead scoring model requires some maintenance. If it seems as if your leads are being over- or under-valued, check your customer data to see if your model needs to be adjusted.

Don’t have time to create a manual lead scoring model? Consider using a CRM that automatically creates and updates lead scoring models.