data models

In this episode of Tech Sales Insights, Randy Seidl welcomes Brad Rinklin, EVP and CMO at Infoblox, to discuss the importance of understanding buyer personas and qualified opportunities in the sales process. He emphasizes the need to focus on the customer and their pain points, as well as the importance of aligning sales and marketing efforts. Brad also highlights the challenges of selling security solutions and the need for ongoing education and enablement for the sales team. He shares how Infoblox leverages data from tech target, Sixth Sense, HG Insights, and D&B to target prospects effectively. Brad also talks about the value of DecisionLink in creating data models and generating ROI proposals for customers.

KEY TAKEAWAYS

• Understanding the customer and their pain points is crucial in the sales process.
• Aligning sales and marketing efforts is essential for success.
• Selling security solutions requires ongoing education and enablement for the sales team.
• Leveraging data from tech target, Sixth Sense, HG Insights, and D&B helps target prospects effectively.
• DecisionLink helps create data models and generate ROI proposals for customer

QUOTES

• "You can't hit a target that you can't see." - Brad Rinklin
• "Focus on the customer and their pain points to drive success." - Brad Rinklin
• "Aligning sales and marketing efforts is crucial for effective go-to-market strategies." - Brad Rinklin

Find out more about Brad  in the link below:

• LinkedIn: https://www.linkedin.com/in/bradrinklin/

This episode of Tech Sales Insights is brought to you by: 

• Sales Community: https://www.salescommunity.com/
• Sandler: https://www.sandler.com/

This week we're joined by June Clevage (@gin__urso) and Cantante Cerdo (@cantante_cerdo) to talk about beer and food. Two things people like. Plus a whole lot of MN talk. 

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For this new season of Airline Voice Radio, we welcome two new hosts. Transformation pioneers and great storytellers Nancy Delgado and Tye Radcliffe take over the mic to explore the road to high-performance retailing by engaging with industry leaders. In this first episode, Tye talks to Melissa Skluzacek, easyJet’s Director of Trading and Revenue Management.

Melissa has over 30 years of industry experience and has been with easyJet since January 2020. Her current position is Director of Trading and Revenue Management at the low-cost, UK-based airline. Trading is a British term that speaks to an amplified vision for revenue management. It was that aspect that attracted Melissa to the role. 

For a more holistic and collaborative approach to revenue optimization, easyJet's trading team implements the pricing and revenue management strategy. Then the team works with other departments to ensure the revenue needle is moving in the right direction. In Melissa's view:

“Revenue management, digital, merchandising, and distribution are all going to morph into a much more of a combined function."

With easyJet operating in nearly 1,000 routes, its revenue management department works with an enormous amount of information. Using data models is the only way to handle this sheer volume. Melissa’s advice is that before implementing a data model, the airline should decide what the model needs to accomplish and what it can and cannot do. Most importantly, a data model’s output needs to answer a specific business question. Will data models replace revenue analysts? Not according to Melissa. She advises against basing decisions solely on the model's output. Instead, analysts should view the output as a recommendation that they can approve, reject, or influence.

In this episode, Melissa and Tye discuss if:

• Combining search data with competitive fare prices and event data will give revenue analysts a more comprehensive market view.
• Dynamic pricing has huge revenue implications for airlines as it allows them to reach the customer at a more personal level.
• Revenue management is a niche field with many nuances and requires specific skills that few vendors have.

Tune in for more Airline Voice Radio episodes coming soon with insightful airline leaders. Airline Voice Radio is available on your favorite podcast player, such as Spotify, Apple Podcasts, and Google Podcasts.

Swiss RE’s SVP of P&C R&D, Jerry Gupta is a firm believer in the revenue-generating potential and AI and machine learning. On this episode of The Data Chief, Jerry offers his view on innovative data model, frameworks for building and operationalizing these models, what makes a good data scientist, and why it will always be more challenging for data teams to maintain existing models vs. innovate new ones. 

Tune in to learn:

• The difference between ost cutting versus revenue-enhancing initiatives 03:20 
• More about the Data Model framework 07:50 
• Why the quality of your output depends on the quality of your input 11:35 
• How to address the shortfall of data scientists in the job market 13:48 
• How to set more clear value statements for models 27:56 
• More about informing the regulation of ethical AI and machine learning 45:33

Mentions:

• McKinsey Global Survey: "The state of AI in 2021" 
• "Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes" 
• Tom Davenport 
• International Risk Governance Center - EPFL

Get even more insights from data and analytics leaders like Jerry on The Data Chief.  

Mission.org is a media studio producing content for world-class clients. Learn more at mission.org.

Alex Ratner (@ajratner, Co-Founder/CEO @SnorkelAI) talks about Snorkel’s evolution from Stanford AI Labs, the challenges of labeling data for AI modeling, and simplifying how AI applications can be built. 

SHOW: 523

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SHOW NOTES:

• Snorkel AI (homepage)
• Stanford Spin-Out Snorkel AI Solves a Major Data Problem

Topic 1 - Welcome to the show. Tell us about your background, the origins of the company, and a little bit about the founding team. 

Topic 2 - Let’s start by framing the day in the life of a data scientist. There’s raw data, there’s a data sorting/organizing process, there’s model building, there’s results and analysis, and the cycle continues, etc. What parts are solved problems, what parts are commoditized, and where is there still room for improvement?

Topic 3 - Now that we understand today’s AI/ML/DataScience landscape, let’s talk about how Snorkel Flow and automated data labeling is able to evolve those environments

Topic 4 - Application Studio seems like the intersection of Low-Code and Industry-specific templates and the Python toolkit that data scientists understand. Walk us through the mindset of today’s data scientists in how they think about the “developer” part of their jobs.

Topic 5 - What are some of the frequent use-cases or business problem areas that you’ve seen drive early adoption of the Snorkel platform? 

Topic 6 - Where do you see Snorkel fitting into the broader ecosystem of AI capabilities that companies may already have in place? 

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