Lead Generation with Data Science
Sales Prospecting 09.21.2021

Lead Generation with Data Science

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In this episode, Rik Walters discusses data science for executives. We learn a new take on data science that talks about projecting and not predicting data. Rik dives deeper into the nuances of anonymous ID tagging, explaining how it’s being used by companies and brands today to provide a better customer experience and to skyrocket sales!

About the Guest

Rik is currently the VP of Demand Marketing at Cradlepoint and has held marketing leadership roles in multiple startups and at global enterprises over the past 20 years. His journey in-demand marketing started many years ago with a passion for data science and marketing analytics that led to early predictive B2B demand generation models for pipeline and revenue.

Connect with Rik Walters

Key takeaways

  • Data science matters in every type of company, be it programming, software, marketing, or sales, because even sales need prediction – to know when they get a lead or prospect.
  • Data science eases your daily spreadsheet processes and analyses by providing better projections – it takes the analysis from an anonymous user profile and attaches it to a known user profile, simplifying the whole process.
  • The ideal customer profile looks at current customers and analyzes similarities. However, there are flaws in that as there could be missing data. When you build it from the start, you can then do an ideal opportunity profile.
  • There is no such thing as perfect data. You have to take the data and keep pressing it.


“That passion for getting the right answers never stops. It’s open for debate, and debating is a healthy version of what data science should be.”

Highlights from the interview

What is your definition of data science?

Data is anything you want it to be, and you can use that in any way you need to. But the science of it always has a testing approach. You use data and analyze it from multiple viewpoints.

What is anonymous ID tracking?

Any platform like Google ID or Bing is anonymously tracking you. Adobe recently acquired platform takes all the touchpoints and attributes them to an anonymous ID, which keeps what stays with you. It’s like carrying a big empty bag of potential. And when you walk into a store and give them your name, you’re a known person. And that bag gets filled with all the stuff that you did in the past. This new platform consumes anonymous ID tracking. So while they’re anonymous, I can’t do anything with them. But we all eventually convert into a potential lead and a potential prospect or buyer. Once you get to that point, you can start making determinations to further your journey to purchasing any platform, software, or product. And when you do that, you build cohorts of data.

How do you turn anonymous people into known people later on?

You identify people from their anonymous identity by first looking up your CRM. You look at a company that’s currently an opportunity and pull up that name. Then, look at the attribution inside that record—the attributions listed with these long 10 to 20-digit codes that are touchpoints or attribution points. You can click on those to know about any interactions made by the person. You can then extract that in Snowflake. You can pull that by querying that data and asking it to list all the people in opportunities that touch this web page.

Predicting vs. Projecting data

Predictive analytics is a term coined for different reasons. If it worked, then we’d all be billionaires. But there are some reasons why projecting is a better term to use with data analytics – you would never tell an accountant to predict what the following year’s revenue will look like based on the data sets. If that were the case, then the stock market would be a different game. They’re projecting and always do.

Is there a book, blog, newsletter, or video that you would recommend our listeners take away from this podcast?

I follow Ronald van Loon on Twitter. He has his finger on the pulse of everything from AI to ML to VR and AR. I’d recommend the podcast Data Science Imposters as it’s very insightful. Some book recommendations would be Multipliers: How the Best Leaders Make Everyone Smarter by Liz Wiseman, Never Split the Difference by Chris Voss, and Trillion Dollar Coach by Alan Eagle, Eric Schmidt, and Jonathan Rosenberg.


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