Predictive Analytics: Application in Lead Qualification

In this video, we explore the basics of predictive analytics and its three core elements: historical data, business objectives, and statistical models. Using a demand generation scenario as an example, we demonstrate how predictive analytics can improve lead quality by predicting the likelihood of new leads converting into sales-qualified leads. Stay tuned for the next video where we'll dive deeper into the logic and math behind this process. Thanks for watching!

Predictive Analytics: Application in Lead QualificationPredictive Analytics: Application in Lead Qualification

New mobile apps to keep an eye on

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What new social media mobile apps are available in 2023?

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Use new social media apps as marketing funnels

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Try out Twitter Spaces or Clubhouse on iPhone

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What app are you currently experimenting on?

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Hey everyone, I hope you’re doing fantastic.

Today, let’s discuss how predictive analytics works.

We are not going to go too deep into the technical details; We’ll discuss those in future videos.

Ok, so there are 3 core elements that form the foundation of predictive analytics.

  1. First, is historical data - that’s our fuel. 
  2. Second, is a business objective - that’s the destination we want to navigate to.
  3. Third, is a statistical model - that’s all of our routes to get to our destination.

Let’s look at a specific example.

Say I’m a demand generation, or growth leader, and my sales team complains about my lead quality.

In this case, I can use predictive analytics to perform a deep qualification of our leads – to ensure that each qualified lead is indeed highly likely to convert, and that I can back this criteria with hard data.

For this scenario, our historical data will be all of our historical leads and their respective attributes – such as demographics, firmographics, engagement activity, and many more parameters.

The objective will be defined in the same way we define a sales-qualified lead in our data – in this case, the attribute ‘sales qualified’ = ‘true’. 

And our statistical model is a set of statistical assumptions that will attempt to surface all the relevant attributes of leads that have previously converted into sales-qualified leads.

Once we build a model, we will use it to predict the likelihood of new leads converting into SQLs, and only the leads with high likelihood will be passed further to sales.

This method can help us realize that, in a specific region, when a VP HR opens more than 4 marketing emails, it significantly impacts that prospects’ propensity to convert.

That’s it for now.

In the next video, we’ll drill down further and review the logic and the mathematical aspect of the example we covered here today.

Thanks for watching, and I’ll see you in the next video!

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