All leads are not created equal. Some are almost ready to buy when you find them, but most are in an earlier stage of the buyer’s journey. The beauty of lead scoring is that once you’ve identified the characteristics and behaviors of leads at the various stages of readiness, you can apply a point system to those factors. As leads come into your sales funnel and interact with you, they essentially define where they are and how fast they’re progressing. Lead scoring lets you see that definition, and helps indicate what the next step for that lead should be. Here are seven steps to getting started:
1. Understand lead scoring
Most scoring systems use ranking criteria that fall into two categories:
Demographic/firmographic. This may include a lead’s job title and department, as well as their company’s size, revenue and industry focus.
Behavioral. What sort of “online body language” does a prospect exhibit? Examples include website visits, responses to email offers, marketing content downloads, and a willingness to complete online registration forms.
There are two ways to gather your data: “Explicit” information is what the contact tells you, such as job title. “Implicit” data is gathered by observation, such as tracking which web pages a contact visits.
2. Gather your lead scoring team
Create a lead scoring team that includes both sales and marketing stakeholders. Your sales managers and field reps can tell you which traits define their ideal prospect – and which traits suggest a poor one. The marketing team, in turn, can work this real-world feedback into a lead scoring model.
3. Define your scoring criteria
Go back through your historical deal data and existing sales pipeline data. Which traits can you identify that are typically associated with closed deals? Which ones tend to indicate deals that either fall through or take longer to close?
Also take a close look at your marketing automation and website analytics. Even a basic analytics system will show you that successful deals often begin with certain types of behavior – pages visited, content downloaded, forms filled out. Keep it simple!
4. Build your lead scoring system
Decide what matters: Some scoring criteria are far more important than others. Viewing a product demo, for example, is more important than just visiting your website home page when it comes to identifying a hot sales lead.
Decide what doesn’t matter: Do certain scoring criteria tell you that a prospect is less likely to buy? That’s just as important.
Set your scoring thresholds: Your lead scoring team should decide how to segment your leads based on their scores. Your process could use letter grades or a point-based system, or it could simply separate leads into groups like “hot,” “warm,” and “cold.”
5. Create your action plan
Define your process for delivering hot leads to sales reps. How quickly should these leads reach a sales rep? Who is responsible for approving and routing leads? How is the actual lead data transferred to the sales pipeline?
Decide what to do with warm leads. Lead nurturing is one good answer, and for many companies is one of the biggest long-term benefits of a lead scoring system.
Decide what to do with cold leads.
6. Measure your progress
Compare your lead scoring models to actual results. Are “hot” prospects leading to a consistently high number of closed deals? Is there a disconnect between what the scoring data tells you and what the sales reps actually see?
When your sales reps spot problems with the process, you need to know about them. It’s also important to review the data from your marketing automation, CRM and sales force automation systems, looking for patterns that show whether your current scoring model is delivering the goods.
7. Refine your process
Your testing and tracking process should constantly identify new ways to refine your lead scoring. If your sales pipeline data suggests that certain “hot” scoring criteria don’t matter, for example, then your next step is to figure out which data points will be more useful for identifying hot prospects.
The most important part of the process is to keep it simple. Experiment with a few factors until you find what works best, and follow the data where it leads you.
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