Analytics in Action
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Join us as we explore what is new in analytics, what are the key opportunities and challenges, and learn from relevant case studies.
- When should we ask, when should be measure?
Gil Oliveira, Behavioral Sales Manager and Benet Boix, Managing Director NA & Canada, Netquest
- A Discrete Choice Take on Uncovering Priorities of US Citizens
Megan Peitz, Founder, Numerious Inc. USA
- Digital Marketing for 2020: Letting Consumer Needs Drive Personalization
Ben Joosen, Global Solutions Leader, Market Strategy & Understanding, Ipsos, Belgium
- Making Experience Text Analytics Actionable: The Human/A.I. Balance
Alison Bushell (U.S.) and Jason Bryce (U.K.), Confirmit
- Who, me biased?
Andrew Grenville, Chief Research Officer, Maru/Matchbox, Canada
Synopses
When should we ask, when should be measure?
Gil Oliveira and Benet Boix, Netquest
Through a study conducted by Netquest to its panelists, we developed an initial hypothetical approach regarding their navigation “People perform quite badly when remembering and evaluating their online activities. In particular, the survey questions that involve remembering past activities are difficult to answer because of the limitations of human memory.” The results obtained from the approach of 3 key questions left us surprised with how bad panelist memory really is!
A Discrete Choice Take on Uncovering Priorities of US Citizens
Megan Peitz, Founder, Numerious Inc. USA
For decades, researchers have struggled with how to ask respondents about topics like gun control, income inequality, climate control, immigration, etc. And as the political landscape in the United States becomes more and more divided, it is necessary to understand what citizens truly value so that Washington can focus on the priorities of the entire nation. A common approach to this research question is the 5-point scale, but one issue is that everything is either very or extremely important, offering little discrimination among the results. We will share a unique application to this problem by using Best-Worst Scaling, or MaxDiff Analysis and examine different outcomes when using scaled data versus choice-based data when it comes to key priorities among US citizens.
Digital Marketing for 2020: Letting Consumer Needs Drive Personalization
Ben Joosen, Global Solutions Leader, Market Strategy & Understanding, Ipsos
Marketers are turning to digital micro-targeting to reach consumers with personalized messages – but are they breaking through the clutter? Marketers may know who their consumers are and what activities they engage in, but do they know what drives their behavior? We will discuss an approach that leverages segmentation for enhanced digital micro-targeting. How? By enriching the behavioral data marketers typically use for micro-targeting (what people do) with their underlying needs and motivations (why they do it). By linking behavioral data from databases to survey data about needs and motivations, the approach lets marketers develop more needs-based messages, optimize reach , and anticipate responses to new communication.
Making Experience Text Analytics Actionable: The Human/A.I. Balance
Alison Bushell (U.S.) and Jason Bryce (U.K.), Confirmit
If you’re not analyzing your customer speech and text, you’re getting an incomplete picture which could be misleading your decisions. Furthermore, discovering the top topics from unstructured data is certainly important, but has anyone noticed that top topics alone are not actionable?? It takes strong human intervention to align text topic categories to your business decision areas. Additionally, a great text analysis should be living/breathing–ensuring it stays relevant as your business and markets change. In this presentation, we’ll help you understand what steps you need to take to make your text analytics truly actionable according to your own stakeholders.
Who, me biased?
Andrew Grenville, Chief Research Officer, Maru/Matchbox, Canada
When we think of cognitive biases, we typically focus on what it means for respondents. But what about our analysis? It’s all too easy to fall for confirmation bias or to develop tunnel vision. The problem is we can’t see our biases—just like we can’t see our blind spot.
The good news is that we are not the first group to realizes that bias is a problem. Other sense makers have led the way. In this session we’ll see how detectives, intelligence analysts, lawyers, and doctors use teamwork, doubt and structured analytic techniques to detect and defeat bias.