Studying Online & Mobile Behavior: Q&A w/ Luth Research (Part One)

Here at Dialsmith, we’re in the business of helping researchers gather and analyze in-the-moment feedback, so when we cross paths with other innovative research methods and technologies that do the same, we get pretty excited and want to share. Such is the case with digital market research service provider Luth Research. Luth offers a wide array of research services, but what got our attention was their web and mobile-tracking tools called ZQ Intelligence™. These tools help researchers monitor people’s behavior—as they browse the web on their PCs as well as their browsing and app activities on their mobile devices.

To learn more about their methods and tools for doing this, we chatted with Luth vice president of market research and new products, R. Scott Evans, PhD.


Q: Can you tell us about ZQ Intelligence and how it works?

Scott: One of the unique capabilities of ZQ is the ability to seamlessly track all the devices an individual uses to engage in the digital eco-system. This includes the ability to trigger research based on predefined behaviors and locations.

Digital tracking, for Luth, means collecting all of the browser activity on PCs and laptops. For tablets and mobile phones, collection includes activity on all apps and mobile web, plus all other functionality metrics for phone calls, SMS, Camera and Email. In the mobile environment, this coverage extends across the two dominant operating systems: iOS and Android.

A second unique feature is the ability to recruit for specialized digital audiences using Luth Research’s proprietary panel, composed of over three million panelists. These panelists are tethered by confirmed physical addresses and require double opt-in.

The ability to recruit high quality research participants with specific characteristics enables Luth to execute targeted digital tracking for clients trying to understand digital behavior associated with specific products or brands. Specialized targeting enables clients to focus their research dollars on customers, prospective customers, and those of their competitors without having to deal with the over-generalizations and irrelevant data associated with syndicated reports.

Q: In-the-moment research and data collection is certainly a hot topic these days. What do you think is driving that?

Scott: Expectations about the real time availability of metrics and data insights has changed dramatically with the advent of more sophisticated e-commerce and digital advertising systems, and the integration of inventory and POS platforms into standardized data structures. Similarly, the rapid expansion of SaaS and cloud enablement has meant that new tools and their rapid deployment has greatly decreased the time it takes to set up and activate news ways of collecting and analyzing data. All of this has contributed to new expectations from those needing insights to make decisions.

In this context, the emphasis on the “quantifiable self” has created pressure to track the multiple ways in which an individual engages with their world. In practical terms, this means being able to integrate an individual’s use of multiple devices and make the connection between device usage and digital paths. If conventional surveys are going to continue to be relevant they need to be able to work in this multi-device universe, and be linked to the amassed behavioral data that makes up any consumer’s digital self.

In many respects, the question is really referring to what is commonly described as big data. More specifically, it is the large amounts of transactional and behavioral data that are accumulated in real-time and readily available to a vast array of reporting tools in near real time.

The vision that was emerging in the late 1990s with the development of business intelligence platforms is the same today: capturing the near real time pulse of business and commercial activity. The major difference today, however, is that the tools and skill sets needed to manage the growing volume of data and pull relevant insights have matured. The exciting part of this development for the market research industry is the extent to which linking surveys and big data provides an unprecedented ability to make the connection between the individual’s immediate behavior and the reasons and emotions underlying that behavior. In the growing complexity of the omni-channel world, linking reason, emotions and actual multi-device behavior is a very real need for businesses trying to understand how their customers connect with products and services.

Q: What types of research projects, and what types of clients, have been embracing the ZQ methods and tools?

Scott: Typical research projects can be categorized under three rubrics: path to purchase, audience insights and ad effectiveness.

The retail and manufacturer brand sectors have been very proactive combining ZQ and surveys to understand path to purchase and generate audience insights. The fundamental goal of both approaches is often to understand the digital in-store nexus. For retailers, this is about understanding how to invest in an omni-channel strategy that accepts the notion that the digital world impacts in-store behavior, both indirectly and directly. Mapping path to purchase that links digital and in-store behavior is critical to understanding what needs to be the client’s investment priorities. Audience insights provides foundational understanding for targeting shoppers and building effective marketing segmentations that address devices, content, and channels.

For the manufacturer brand, path to purchase research and audience insights are more often focused on understanding which of their channel partners are optimizing the impact of digital activity on purchase behavior, and knowing the types of shoppers that are engaging with each of their channel partners. They need to understand how best to guide their channel partners and which partners merit proactive backing.

Finally, agencies and marketing teams are increasingly seeing the advantages of studying the impact of digital advertising in the “wild”. For Luth’s ad effectiveness research there is a well-developed method for capturing non-intrusive ad exposure. This involves querying unique ad tags, which enables our researchers to identify consumers that have been exposed to a particular ad, across all their devices. In turn, this permits the comparison in a natural environment of those exposed with those who have not been exposed. The advantage of accessing the digital history of a participant means that the analyst can identify the lead effects and mediating factors influencing the impact of ad exposure on a range of digital behaviors, without having to rely on conventional recall methods.

Thanks Scott! That’s a ton of good background and we’re only half-way through, so we’ll save the rest of our discussion for Part 2.