Attributes of a successful lead scoring model
Any business needs a continuous flow of leads to truly prosper. However, the more leads you generate, the more judicious you must be in your pursuits.
Sales reps don’t want to waste time chasing a long list of unproductive 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 robust lead scoring model.
1. Define your scoring threshold
A lead scoring threshold is the point value at which a prospect is considered sales-ready. When a lead’s score reaches or exceeds this amount, they become a marketing qualified lead (MQL) and are passed from marketing to sales.
It’s important to get your threshold right. If the bar for entry is too low, leads will be qualified prematurely and sales reps will have a frustrating time pursuing prospects that aren’t ready to be pursued. But if the bar is raised too high, you risk sitting on valuable leads for too long and giving them time to be snatched up by a competitor.
2. Explicit scoring
With explicit scoring, you can tailor your lead scoring to better reflect your ideal customer by assigning points to leads based on specific objective qualities. For example, you could consider firmographic or demographic details as explicit characteristics when scoring a lead.
Other examples of explicit characteristics that you may want to take into account include:
- Job title
- Industry experience
- Company size
- Company revenue and;
- Geographical location
Clear-cut factors like company size and industry can help you quickly evaluate leads to see if they are a good match for your business. For example, if you are looking for C-suite executives from large tech companies, you can use this information to score leads and see if they are a good fit.
Sometimes, leads will volunteer information that you need for explicit scoring. For example, they may fill out a questionnaire to download gated content from your website. Or, you may be able to uncover this information through research, such as checking a prospect’s LinkedIn page or company website.
3. Implicit Scoring
On the other hand, implicit scoring refers to the points awarded to a lead based on their behaviour, such as:
- Site visits
- Social media engagement
- Email opens/clicks
- Newsletter subscriptions
- Contact requests
- Contact form submissions
- Content downloads
- Webinar interest
- Demos/free trials
If someone downloads an e-book from your company, for example, you can award points for that interaction. This is because you can infer from the act of downloading the e-book that the prospect has a certain level of interest in your company.
You can use your CRM to track every interaction a customer has with your company. This includes implicit scoring, which 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.
4. Negative scoring
Not every interaction with your company is a step in the buyer’s journey, and your lead scoring model needs to recognise this. Negative scoring removes points from a lead score based on actions or characteristics that indicate waning or complete lack of interest, which could include:
- Visiting your careers page (likely to want a job, not your product or service)
- Job titles such as student or retired or from an industry not currently or expected to be served by your product or service. This suggests they’re likely interested in your content for purely academic or informational 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 may seem interested in your brand based on their qualities, such as their industry, but their actions show that they’re losing interest. With negative scoring, sales reps can recognise these weak leads and focus on nurturing stronger potential customers instead.
5. Score degradation
Collaboration between marketing and sales is key to identifying prospects who are unlikely to convert. Both departments have valuable insights that can help keep them aligned. Assigning a negative points value to common traits and behaviours across past leads can help keep your pipeline on track.
Ideally, you want your leads to keep moving through your sales funnel. Score degradation helps you track leads that have become stagnant, by lowering their score if they haven’t interacted with your brand for a significant period of time. This helps you focus on the leads that are still active and engaged.
A good lead scoring model is not static — it’s constantly evolving. Keep your model accurate by continually updating it based on the latest customer data. For example, if you see that a lot of leads are qualifying but few are being converted by Sales, it’s likely that the lead scoring threshold is too low. Or, if you notice that sales from one type of persona or prospect are increasing or decreasing, you may need to change the way you weigh these variables.
When your MQL-to-conversion rate starts to decline, it’s a good indication that your lead scoring model may need an update. This is usually because your target customer profile has changed and your current scoring model is no longer accurate.