Any attempt you make at lead scoring is going to help speed up your sales cycle. However, once you’ve gotten started, lead scoring can be time consuming. If you want fine-tuned results without wasting time, HubSpot predictive lead scoring (available to HubSpot Marketing Enterprise Users) can make your life easier.
Manual lead scoring is a good way to toss around ideas. A brainstorming session can help your sales and marketing staff work together to create a lead scoring system that fits the goals of your business. Moving up to HubSpot predictive lead scoring lets you automate the process. It’s a game changer.
How HubSpot Predictive Lead Scoring Works
Predictive lead scoring in HubSpot uses the data you already have to identify your best leads. Predictive scoring uses your lead information — like what attributes your leads had in common, when they did buy and when they didn't — to more effectively (and automatically) assign positive and negative values.
Using this type of information, HubSpot helps you sort your leads and pick out the hot from the not. Simply put, the formula identifies your contacts and groups them so you know those who are likely to become customers (Sales Qualified Leads or SQLs), those who are still warm leads (Marketing Qualified Leads or MQLs) and the no-goes. Information this detailed ensures your sales team is only spending time working with the high-conversion-potential leads.
Meanwhile, your marketing team can create content to appeal to not-quite-there leads. Finally, the no-goes aren’t worried about unwanted sales contact and your sales team can remove them from their priority list.
The bonus to predictive lead scoring, which is difficult to duplicate in the manual process, is how the scoring becomes “smarter” based on every bit of data that is collected. Your scoring evolves with little effort from you. Your sales process will improve because you have access to qualified lead information that is more and more accurate with time.
To recap, lead scoring is assigning positive and negative points values to a lead based on their online actions with your business. Positive and negative attributes add to and subtract from a lead's hotness. We’ve talked about how to set up manual lead scoring to get you started. Now, here’s how predictive lead scoring can save you time. Here are some of the many data points that HubSpot predictive lead scoring can assign positive and negative attributes to (remember, when HubSpot does it, you don't have to think about it):
- contact properties
- company properties
- deal properties
- activity properties
- line item properties
- list memberships
- form submission
- marketing emails
- email subscription
- page view
- product properties
- ads interactions
This is not an exhaustive list. Each of these general properties can again be broken down into more defined actions that earn or lose points for a lead.
Here’s More Examples of Attributes to Better Define Qualified Leads
- Fit vs. Interest
- Fit = right customer, right product, right industry; interest = level of engagement. You can assign positive values to both attributes, pushing prospects higher up the qualified lead ladder.
- Multiple Personas
- If you have products intended for different buyers, you can identify separate lead scores for each persona that are still based on similar attributes you assigned for fit and interest.
- New Business vs. Upsell
- If you’re growing and looking to upsell or cross-sell while generating new business, you’ll need to define values for each of these goals.
This looks like a lot, and it is. That’s why a few clicks through the HubSpot lead scoring options can ease the stress of lead management. You can also easily edit your lead scores as needed. If your system doesn’t work for you on the first try, you can modify it to boost your conversion rates.
You can combine your own score customization with automated options and create more scale sheets as your company grows to specifically qualify leads in all areas of your business.
Want Better Sales Growth? Download the Lead Scoring E-Book.