What separates a qualified lead from an unqualified one? That's the burning question we all have.
We want to make sure we know the key factors that make someone qualified so we can focus on the creating and delivering the right content through the promotional channels that make these most sense. Once we establish that framework, we can then help our sales teams make the most of their time by providing them with the means to prioritize leads.
And while that sequence of events is ideal, it's often easier said than done.
That's where lead scoring comes in.
Lead scoring aims to simplify the way businesses identify the best leads for their salespeople to connect with through the use of a strategic scoring system. Wondering if leading scoring makes sense for your business? And if it does, where do you start? For answers to these crucial questions, keep reading.
Is Lead Scoring Right for You?
Lead scoring is a great way to handle growth at your company. As your business grows and you generate more leads, it doesn't always make sense to get in touch with every single one. You want to make sure sales is prioritizing their time based on the most qualified leads. Here are a few questions you should ask yourself before you implement lead scoring.
Do I have enough leads? - If you are a new business starting out and you're not generating many leads, it's likely that you don't need to implement lead scoring just yet. At this point, you're probably still figuring out what makes a lead qualified, and part of doing that is talking to your leads to figure out what the most qualified ones have in common. Focus on this first.
Does my sales team call the leads I send them? - Implementing lead scoring is not always a quick and easy process. There is a lot that goes into figuring out what properties to include in your lead score and how many points to assign them. Before you spend time doing that, you need to find out if your sales team is actually calling the leads you send them. If they aren't, you may have a sales-marketing alignment issue on your hands that needs to be solved before implementing a scoring process.
Do I have enough data to implement lead scoring? - There are a few components that you must have in order to start lead scoring. First, you need to make sure you have a lot of data. You need data about your leads that didn't close, and you need data about your leads that did close. This will help you figure out what properties will serve as a good indicator that someone is qualified (or vice versa). That said, if you're just starting out, give yourself time to generate more detailed contact insights before you consider lead scoring.
What's the Difference Between Traditional and Predictive Lead Scoring?
Traditional Lead Scoring
Lead scoring is a tool that marketers use to figure out which qualified leads they should send to their sales team. The way it works is the marketer will identify a series of qualifying factors that aim to indicate whether or not a lead makes sense for their business to pursue.
For example, if you are a B2B business, you may know that if someone fills out your form with an email that ends in @gmail.com or @yahoo.com, it's likely that they aren't qualified. On the flip side, you may find that leads in the software or technology industry serve as a better fit for your product or service. These are examples of the properties that need to be identified to run lead scoring.
The second part to lead scoring is assigning a value to how important or unimportant these properties are. For example, if someone has an @gmail.com email address, you may want to deduct five points from their lead score. But if someone belongs to the software industry, you may want to add 10 points to their lead score because that shows that they are more likely to close.
As you add and deduct values based on the information you collect about your leads, you will find that some have a higher score than others. Those are your most qualified leads based on the information you laid out and should be prioritized by your sales team.
Predictive Lead Scoring
Predictive lead scoring is a tool that uses an algorithm to predict who in your database is qualified or not qualified. Different providers take different information into account when predicting your score, including but not limited to: property information your leads fill out on your website, behavioral data, social information, demographics, and media written about your company.
The beauty of predictive lead scoring is you do not have to figure out what properties should be included or how much to weigh each property. Nailing down a consistent formula for this can be really difficult for many marketers, and often comes down to a "try and check" process. However, with predictive lead scoring, the algorithm looks at what information your customers have in common, as well as what information your leads that did not close have in common. From there, the lead scoring algorithm then comes up with a formula that will automatically bucket your leads for you so you can easily identify the most qualified ones.
How can I use HubSpot's predictive lead scoring app?
You have been storing both engaged and unengaged contacts in HubSpot. You have been marking contacts as customers for at least three months. You have at least 500 contacts in HubSpot that are marked as customers. You have at least twice as many contacts that are marked as non-customers.
Please note: These are general guidelines and may differ between HubSpot accounts.
If you do not fit into the criteria above, we will provide a default model based on patterns we have identified from running the tailored predictive lead scores across customers with good data. Though it won't use personalized account information such as IP country, state, or business type, it will give you a head start to using lead scoring.
To access your lead score, go to Contacts > Lead Scoring. You will then see a Predictive Lead Scoring tab. Once you are in there you can begin to run your model.
Do you plan on implementing predictive lead scoring? Let us know in the comments section below.
Originally published Sep 14, 2015 6:00:00 AM, updated August 27 2017