How Predictive Lead Scoring Improves Marketing And Drives Sales

By Dave Orecchio

Inbound marketing, with its customer-centric focus and data-driven approach, has allowed companies to vastly improve the effectiveness of their marketing efforts.

Automation, in particular, has made lead generation more cost-efficient than ever.

But the influx of leads also comes with its own challenges.

A personalized, one-on-one follow up is the most effective way to close a sale. However, it’s simply not practical or cost-effective for sales reps to reach out to every single subscriber on your list.

How can you determine which leads to contact? This is where automated lead scoring comes into play.

Predictive Lead Scoring

Lead scoring is a strategy that helps businesses identify the most qualified leads by employing a strategic scoring system.

Companies have been using lead scoring techniques for a long time to determine if a lead is worth pursuing.

Traditional lead scoring is often done manually and utilizes only a handful of easily identifiable qualifying factors (e.g., email address, company size.)

The process is time-consuming and doesn’t have the capability to utilize all the customer data we have at our disposal, made available to us by the latest marketing technologies.

Thankfully, the technologies that allow us to gather a large amount of customer data also give us the tools to analyze that data.

Predictive lead scoring uses an algorithm to process the data and determine which leads in your database are most qualified.

With this information, you can tailor sales and marketing strategies to each segment of prospects so you can maximize your ROI.

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Predictive lead scoring better and more accurate at qualifying leads and opportunities than point-based attribution

Applying Predictive Lead Scoring To Sales & Marketing

Different companies adopt different algorithms for predictive lead scoring. One of the most common methods is using data from past leads to create the scoring system.

To generate accurate results, you’d need a good number of engaged and unengaged contacts, as well as enough of these contacts that have turned into customers, in order to understand the data’s correlation with the quality of the leads.

We don’t lack data these days. The key is to identify the ones that matter. Here are 6 most commonly used types of data:

  • Demographic information
  • Company information
  • Online behavior
  • Email engagement
  • Social engagement
  • Email domain (for filtering out “spam” emails or email addresses that indicate low-quality leads)

How would I use HubSpot’s predictive lead scoring app?

HubSpot’s Predictive Lead Scoring App is available to all Enterprise customers and now also to teams who own Sales Pro. Here are some guidelines to run your custom Predictive Lead Score:

  • You have been storing both engaged and unengaged contacts in your HubSpot databased.
  • 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.

If you do not have a large enough database of customers and non-customer contacts in your database, then you can start with manually applied lead scoring factors.

After you have set up the right software to analyze the right data, you can use the insights Go to the full article.

Source:: Business 2 Community

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