Demystifying Data

This is the second part of a two-part podcast featuring an interview with Andy Plax (the first part is here). In this second half of the interview, Chris and Andy discuss how agencies get going with measurement, where measurement platforms are headed, and what the role of AI/ Machine Learning can or should play.

You’ll find a brief yet in-depth discussion around the following:

• Agency Barriers to Getting Started. Andy suggests the most common hurdle any agency has when working to get things started with a solid campaign management system is how to make things easy for their field staff. This idea is explored.
• How Different Agencies Organize Their Data. The question of data hierarchies is discussed with Chris and Andy sharing their perspective as consultants working with dozens of different agencies, all with different styles and areas of expertise.
• The Role of AI and Machine Learning. As Andy puts it, “No one has ever come to the table saying they want less insights from their data.” But hear why now might not be the right time for AI functions on the front end of Campaign Management Dashboards but may be past-due when it comes to back-end processing.

You can read more about Andy and the team at NetworkNinja online at https://networkninja.com/ and read about MainEvent at https://maineventsoftware.com/.

On Episode 3, Andy Plax joins the podcast to talk about how cloud-based data management systems are used to give agencies a competitive advantage. Chris and Andy talk about experiential and field marketing campaign data management systems, how they’ve changed, and where they’re going. 

Across this two-part series, Chris and Andy talk about…

• How brand expectations for experiential data and insights have changed over the past 10 years and what drove the demands we see today for real accountability and actionable data.
• The most common challenges new teams or agencies have when it comes to making a commitment to centralized data collection and processing. But also, the huge upside for those who work to overcome these challenges.
• The role of AI and machine learning in experiential data. Is it a back-end data processing tool for greater efficiency or is it a front-end solution for greater insights?
• And Andy gives us a little insight into where things are headed for the MainEvent platform and what we can expect to see in the years to come.

You can read more about Andy and the team at NetworkNinja online at https://networkninja.com/ and read about MainEvent at https://maineventsoftware.com/.

On Episode 2, Chris opens with some business process recommendations to assure data quality. Replication is at the heart of how we assure quality as marketing analysts and data scientists in general. We open the podcast with some business process methods that can be followed to assure quality and bring these values to your own practice.

Then the heart of this episode discusses the idea that any marketing is about creating a compelling message and delivering that message to the consumer target most ready to hear it. Therefore, brands develop consumer profiles and as a marketer, your program will deliver when it reaches the right consumer. Experiential marketing is no exception. 

This idea is broken down into three sections:

• “Message to Market Match” applies to Experiential Measurement Too: There is no marketing theory more important than “message to market match” and when experiential campaigns are working well and generating value for a brand it more often than not has to do with the fact that there was alignment between the message and market. This idea is broken down and examples given.
• Why Venue Selection is Key to Reaching the Right Consumer: Our tool as experiential marketers when targeting one consumer over another is venue selection. And while the lines may be blurry, you’ll find different types of people at Air Shows and Home Shows. The profile at the Hot Rod Association drag race will be different than the state fair. Chris talks about why it’s important to keep this in mind when analyzing your experiential data.
• Case Study--How Using Venue Selection to Improve Targeting Resulted in 4x Stronger ROI: Two case studies are given based on direct PortMA research that highlights these outcomes. The first covers how to apply this idea when analyzing data from music festivals, the second describes how one brand saw their campaign ROI vary by over 1,500 percentage points based on the type of venue they activated at.

Comment and tell us what you think by following then tweeting @portma #ExperientialROI

On Episode 1, Chris Clegg takes a few minutes to introduce himself. A sociologist who plays with numbers in a commercial setting is a marketer. And in this first podcast episode, Chris takes a few minutes to introduce himself as such. You’ll hear him confess to his biases and reveal the four principals he uses to tell the future.

If you listened to this short introduction, you’ll hear about the following:

How is it that numbers are figments of our imagination yet follow rules? And more importantly how do we harness the power this represents? 

There are four principles that, if accepted, will let you tell the future:

• Anything in the known universe can be counted
• Anything you can count, you can graph
• Graphs of human behavior or perspective will always contain patterns
• Patterns by their nature show us the future

And so, data scientists who practice marketing practice the art of telling the future. Chris breaks this idea down with some personal stories and philosophies by way of introduction.

On this pilot episode, Chris tackles the challenge of defining the dollar value of impressions.  This is the most popular blog post on PortMA's Experiential ROI blog and garners the most activity when they post on Twitter @portma.  

Chris breaks the problem down into three business challenges:

• How to collect experiential marketing impressions reliably: Turns out, that consistency is more important than accuracy.  Chris breaks down this critical data science principle and provides simple solutions to avoid this common mistake.
• Methods for recording the five primary impression categories: In experiential marketing there are five impression categories that every campaign should be tracking.  Don’t sell yourself short by missing one of these key areas of value.
• Assigning a legitimate dollar value to experiential marketing impressions: Finally, Chris reveals the industry standard for impression valuation modeling and provides several examples that allow the non-data minded marketer a clear roadmap to assigning the right dollar value to experiential impressions.

Links of Interest

Portland Marketing Analytics (PortMA) is a marketing research firm specializing in the develop of marketing communication strategies and measuring the return-on-investment of experiential marketing campaigns.

You can follow Chris on Twitter @portma.

For a more compressive review of Ad Value Equivalency Modeling techniques including criticisms of the approach visit PR Week’s article, The AVE debate: Measuring the value of PR.