Predictive Analytics for 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!
New mobile apps to keep an eye on
Hendrerit enim egestas hac eu aliquam mauris at viverra id mi eget faucibus sagittis, volutpat placerat viverra ut metus velit, velegestas pretium sollicitudin rhoncus ullamcorper ullamcorper venenatis sed vestibulum eu quam pellentesque aliquet tellus integer curabitur pharetra integer et ipsum nunc et facilisis etiam vulputate blandit ultrices est lectus eget urna, non sed lacus tortor etamet sed sagittis id porttitor parturient posuere.
- Lorem ipsum dolor sit amet consectetur rhoncus ullamcorper ullamcorper
- Mauris aliquet faucibus iaculis dui vitae ullamco
- Posuere enim mi pharetra neque proin vulputate blandit ultrices
- Posuere enim mi pharetra neque pellentesque aliquet tellus proindi
What new social media mobile apps are available in 2023?
Sollicitudin rhoncus ullamcorper ullamcorper venenatis sed vestibulum eu quam pellentesque aliquet tellus integer curabitur pharetra integer et ipsum nunc et facilisis etiam vulputate blandit ultrices est lectus vulputate eget urna, non sed lacus tortor etamet sed sagittis id porttitor parturient posuere.
Use new social media apps as marketing funnels
Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat commodo sed egestas aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod eu tincidunt tortor aliquam nulla facilisi aenean sed adipiscing diam donec adipiscing ut lectus arcu bibendum at varius vel pharetra nibh venenatis cras sed felis eget.
- Lorem ipsum dolor sit amet consectetur fringilla ut morbi tincidunt.
- Mauris aliquet faucibus iaculis dui vitae ullamco neque proin vulputate interdum.
- Posuere enim mi pharetra neque proin bibendum felis donec et odio.
- Posuere enim mi pharetra neque proin aliquam mauris at viverra id mi eget.
“Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat.”
Try out Twitter Spaces or Clubhouse on iPhone
Nisi quis eleifend quam adipiscing vitae aliquet bibendum enim facilisis gravida neque velit euismod in pellentesque massa placerat volutpat lacus laoreet non curabitur gravida odio aenean sed adipiscing diam donec adipiscing tristique risus amet est placerat in egestas erat imperdiet sed euismod nisi.
What app are you currently experimenting on?
Eget lorem dolor sed viverra ipsum nunc aliquet bibendum felis donec et odio pellentesque diam volutpat commodo sed egestas aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod eu tincidunt tortor aliquam nulla facilisi aenean sed adipiscing diam donec adipiscing ut lectus arcu bibendum at varius vel pharetra nibh venenatis cras sed felis eget.
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.
- First, is historical data - that’s our fuel.
- Second, is a business objective - that’s the destination we want to navigate to.
- 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!
