Dialsmith

Image courtesy of Luth Research

In Part I of our discussion, we covered a lot of ground with Scott Evans from Luth Research on how their ZQ Intelligence™ tools enable researchers to track the online and mobile behaviors of consumers and who’s using it. In Part II, we dive deeper into the methodology and results.

Q: How have you combined ZQ with other complementary technologies to expand the offering or meet specific client needs?

Scott: Luth Research continues merging complementary technologies with its digital tracking to meet client needs. One example is the inclusion of traditional UX tools. This is done in instances where a client needs to understand the intricacies of deep website navigation, in the context of ongoing digital behavior. We combine passive tracking for a picture of a participant’s behavior throughout the broader digital eco-system with a more detailed analysis of a single website experience using user experience research technology. The combination of passive digital tracking and user experience analysis provides the client with a highly contextualized understanding of how a customer arrives at a website, and what they experience when they interact with a single website’s content, and where they go once they have left the website. Another example of complementary is the linking of Luth’s passive digital tracking technology with geo-fencing tools. These tools work alongside Luth’s digital tracking and trigger surveys when shoppers engage in in-store activities. Geo-fencing works with tracked mobile devices and identifies when a participant comes into close proximity to a predefined location, like a store. When the participant’s mobile phone location information triggers the geo-location software, a short survey can be served, asking the participant details on their in-store activities and perceptions.

Q: What are the biggest similarities and differences between the mobile tracking and desktop tracking results? Do you test for the same behaviors?

Scott: Understanding the difference between devices is a critical for developing an effective omni-channel strategy. For example, there are critical differences in how PC’s are used with deal sites, compared with how they are used in mobile devices. The obvious difference is how mobile apps are used in store or service locations to redeem coupons and discounts. Another difference is in gaming. While hardcore gamers are still using PCs and gaming consoles, there are a vast number of games that are being accessed via mobile apps. If gaming channels are an opportunity for a client then it is absolutely critical to understand the channel’s reach on mobile devices and the particular characteristics of this population. It is also important to understand that mobile data is more continuous and more personal than data from either PCs or tablets. The fact that mobile phones are always on and have a particular affinity with location sensitive activities provides an insight into the individual that is often missing on PCs. These differences in the usage of devices have implications for different points in the purchase funnel. There are also important differences in the role of devices for product categories. It is incumbent on marketing managers to understand these differences and to build in device relevance for their particular product category. A salient example of a pattern bucking conventional wisdom is toy shopping. When adults shop for children’s toys they predominantly use their PC – despite using their mobile devices to shop for many of their personal items. For large ticket items, PC research is frequently an initial step because it is easier to engage in longer text-based sources and explore video and images. However, when a shopper visits a store their phone is often used to search reviews and compare prices, providing “on-the-fly” evaluations which may be a critical factor in the last leg of the purchase funnel.

Q: It seems as though you could get very different results from people who use devices (e.g., Chromecast, Roku, Apple TV) to stream online videos, compared to people who only use their computers. Do you do anything to address these potential differences?

Scott: Our latest initiative is tracking Netflix usage at the title level. While there may be differences in how viewers use alternative devices like Apple TV or Chromecast, the more important question is whether or not viewing is influenced by more mainstream devices like tablets and mobile phones (iOS or Android). This is where the Netflix platform is particularly relevant. While capturing title level data for streaming video on many of the mentioned alternative devices remains universal challenging, Luth has been able to advance its position in this space by figuring out how to capture title-level data from the devices of Netflix viewers. The preliminary results suggest some important differences in viewing patterns. Looking at the top 20 titles for PCs, tablets and mobile phone, there is very little overlap. There are also cohort factors influencing this lack of congruence on device viewing. For example, one of the few device cross-over titles is “Family Guy”, which has definite generational implications. When ranking title visits “Family Guy” is number one for the 18 to 24 cohort. It is number two for the 25 to 34 cohort and drops out of the top ten for ages greater than 34. However, this does not detract from the basic insight, that different devices had dramatically different viewing patterns.

Q: What are clients able to learn from ZQ that they may have trouble getting to otherwise?

Scott: One of the key advantages of combining digital tracking technology and having a proprietary panel is the ability to recruit for market niches. We can target individuals that own specific products or are likely to purchase a particular product, so that a client can cut away all of the superfluous noise of the digital world and focus on individuals who are most relevant to their business. Product managers want to track their natural audience and do not want to invest in data or studies that only tangentially touch on their primary business concern. While the customized niche is critical for some, clients working in large markets can use Luth’s historical data and mine the patterns and paths that show how their market behaves. In this context, for example, advertising effectiveness takes on new twist. Clients can access historical digital data to identify ad exposure by querying unique ad tags. The advantage of this approach is that it is easy to identify those that were exposed and compare their entire digital path with those that were not exposed. The advantage is that the leading effect of a particular campaign can be analyzed over a longer time period, without risking the distorting effects of recall bias. Finally, the digital in-store nexus remains vitally important for the entire retail sector (notwithstanding Amazon). This means combining passive digital tracking and with in-store surveying with geo-fencing technology provides the necessary insights needed by marketers and shopper teams to harmonize their digital and in-store strategies. That does it for the Q&A.

Thanks to Scott and Luth Research for making us smarter about this new and exciting mode of research. You can keep in the loop with news and updates on ZQ Intelligence and Luth Research by following them on Twitter at @luthresearch or checking in on their blog.