A VentureBeat report (Cifuentes 2015) on the state of marketing analytics indicated that while many companies are significantly increasing spending on related analytics services and technologies, it was also true that "marketers aren't that advanced in their analytical approaches" and there was a "gigantic skills gap in most organizations around data science."

Further, the report looked at more than 800 marketing technologies related to multiple business use cases including audience insights, e-commerce analytics, and unstructured data analytics—finding the chief marketing officer (not the CIO) was typically responsible for these technologies and there was a pressing need for marketing organizations to demonstrate business performance improvement related to this technology expense.

One way data professionals can help marketers get better insights and business results is to mentor them about designing better marketing research that includes unstructured data. Too many marketers simply rely on scanning corporate social media feeds, filtering online panel discussion threads, and examining open-text field content in surveys. Although there are many useful unstructured data analytics tools, such as Provalis or IBM Watson, that can make some sense of this flow of unstructured data, enterprises miss the opportunity to design more thoughtful, useful, and productive digital marketing research methods.

Data to Help Innovate

This is particularly pertinent when an organization is looking for predictive insights from marketing data that will help it innovate more successfully in new product (or service) design. Studies examining new product failure rates vary significantly in methods and results, but findings can range from 35 percent failure (healthcare products; Castellion and Markham 2013) to 95 percent failure (consumer products; Nobel 2011). Clearly, better insights into hidden needs and tacitly held beliefs are required.

Yet, as marketing author and speaker Seth Godin (2016) explains, "Too often, marketers do surveys, not polls, or bother everyone with a census, poorly done. Worse, they then use these results as an accurate prediction of the future instead of a reliable snapshot of now.... A poll doesn't predict the future.... If, on the day the iPhone was announced, you had done a well-designed poll and asked, ‘Do you intend to ever buy a smartphone?,’ the yeses would have certainly been less than 5 percent of the result.”

As a result, we find marketers—hoping to glean insights about consumers' future needs—turning to the aforementioned unstructured discussion groups, online chats, focus groups, user groups, and open-ended questions as expressive alternatives to reductive polls and surveys. What marketers can learn is that by augmenting these insight-seeking activities with creative and immersive research design, they can help participants better express their unspoken needs and beliefs by using interactive play, analogies, metaphors, images, symbols, and storytelling (Choo 2000).

Playful Human Computation

One useful way to address both unstructured marketing data analytics and the need for better innovation insights is by introducing human computation activities into the marketing researcher's toolkit.

The term human computation was first used in 1838, but researchers attribute its contemporary usage in computer science to CAPTCHA / reCAPTCHA inventor and Carnegie Mellon professor Luis von Ahn (Quinn and Bederson 2011). He is also well-known for creating the ESP game, which was used by Google to improve results for their image search function (von Ahn 2006). One of von Ahn's definitions of human computation is "the idea of using human effort to perform tasks that computers cannot yet perform, usually in an enjoyable manner” (Law and von Ahn 2009). This can take the form of participants providing image tags, meta coding for narrative texts, and ratings on the quality of contributions.

Even with powerful data analytics tools, preprocessing large volumes of unstructured data is required before data mining can occur effectively. As Barbier et al. (2012) outline, typical preprocessing techniques can include trust assessment, stop-word removal, vectorizing, and feature selection or extraction. By incorporating human computation design, research participants can take part in gamified trust assessment of unstructured marketing research data (text and images) by applying "likes," performing simulated spending tasks, and rating the quality of other participants' contributions.

Content Tagging

A popular human computation activity is content tagging. Researchers can show photos of products or use cases and have participants list the "three key words" they would use to describe how they feel about the scene in the photograph or video. When large numbers of participants are included in this activity, often through crowdsourcing platforms, the richness of the resulting data describing emotional responses to products and use cases can be interesting and useful.

“Fill in the Blanks” Sentences

When designing storytelling activities, researchers can incorporate a "fill in the blanks" question design that, in effect, presents the participant with precoded data intake activity that also embraces the enjoyable, immersive quality desired in generating insights for innovation. For example, instead of (or in addition to) an open online discussion, a human computation activity of this style might look like the following:

If I could only take one digital device with me on a vacation, it would definitely be my _____. I would use it mostly for _____ and _____. When I think I may have misplaced or lost this device it makes me feel _____ because _____. But the one thing I wish my device would do even better is _____.

Clearly, analyzing the content from these delimited text fields will be much more straightforward than open narrative texts. Further, depending on the research design, other participants can rate this completed sentence on whether it is similar or dissimilar to how they feel (e.g., they can vote with a "most like me" button and be eligible for gamified rewards).

Photo Elicitation

When researchers need to tap into participants' emotions and hidden beliefs, images are an excellent tool. The technique of photo elicitation can include having participants take their own photographs or they can select from a set of images provided to them to illustrate their ideas. However, Stedman et al. (2013) note that "photo elicitation is rarely used by social scientists" and acknowledge the irony because images are "more effectively tapping into informants' tacit, and often unconscious, consumption of representations, images, and metaphors," while producing "different and richer information than other techniques.”

There are many ways for marketers to combine photo elicitation and human computation. One simple method is to present participants with an image of a product in use as the main visual for a print advertisement, targeted at an audience very different from them (e.g., their grandparents). By providing fields for the participants to create an advertisement headline, compose a delimited text product description, and use two words to describe the main product benefits to this dissimilar audience, the researchers are tapping into powerful psychological projections about the product and producing data that is already categorized.

Summary

These are just a few examples of how data professionals can advise marketers to creatively delimit unstructured data collection with the impact of (1) more engaging user experiences, (2) tacit need elicitation, and (3) improved data analytics and innovation insights.

Sharon McIntyre is an innovation researcher, adjunct professor, and marketing strategist. She currently consults on open innovation methodology for crowdsourcing pioneer Chaordix, whose clients include LEGO, IBM, and Ford. Sharon is also advising the Cameroon, West Africa, energy utility company Eneo with a scope that encompasses frugal innovation and technology marketing mentorship. Her doctoral research focus is on entrepreneurial innovation process and policy. Visit shazzmack.com or follow Sharon on Twitter: @shazzmack.

References

Barbier, G., R. Zafarani, H. Gao, G. Fung, and H. Liu [2012]. “Maximizing Benefits from Crowdsourced Data,” Computational and Mathematical Organization Theory 18(3): 257–79. doi:10.1007/s10588-012-9121-2

Castellion, G., and S. K. Markham [2013]. “Perspective: New Product Failure Rates: Influence of Argumentum Ad Populum and Self-Interest,” Journal of Product Innovation Management 30: 976–79. doi:10.1111/j.1540-5885.2012.01009

Choo, C. [2000]. “Working with Knowledge: How Information Professionals Help Organizations Manage What They Know,” Library Management 21(8): 395–403, http://choo.fis.utoronto.ca/lm.

Cifuentes, J. [2015]. “The State of Marketing Analytics: Insights in the Age of the Customer,” VentureBeat, December 16, http://venturebeat.com/2015/08/21/new-research-companies-plan-to-massively-increase-spend-on-marketing-analytics.

Godin, S. [2016]. “What Does the Poll Say?,” blog entry by Seth Godin, October 31, http://sethgodin.typepad.com/seths_blog/2016/10/what-does-the-poll-say.html.

Law, E., and L. von Ahn [2009]. “Input-Agreement: A New Mechanism for Collecting Data Using Human Computation Games,” CHI 2009, April 4–9, Boston.

Nobel, C. [2011]. “Clay Christensen's Milkshake Marketing,” Harvard Business School Working Knowledge, February 14, http://hbswk.hbs.edu/item/clay-christensens-milkshake-marketing.

Quinn, A., and B. Bederson [2011]. “Human Computation: A Survey and Taxonomy of a Growing Field,” CHI 2007, May 7–12, Vancouver, BC.

Stedman, R. C., B. Amsden, T. M. Beckley, and K. G. Tidball [2013]. “Photo-Based Methods for Understanding Place Meanings as Foundations of Attachment,” in Place Attachment: Advances in Theory, Methods, and Research, ed. L. Manzo and P. Devine-Wright (London: Routledge), 112–24.

von Ahn, L. [2006]. “Human Computation,” GoogleTalks, July 6, https://youtu.be/tx082gDwGcM.

Additional Resources

Alvesson, M., and K. Skoldberg [2009]. Reflexive Methodology: New Vistas for Qualitative Research (London: Sage).

“IBM Watson Analytics for Social Media Tools,” available at: https://www.ibm.com/marketplace/cloud/social-media-data-analysis/purchase/in/en-in.

IDEO [n.d.]. “Design Thinking for Educators Toolkit,” available at https://www.ideo.com/post/design-thinking-for-educators.

Mankoff, R. [2008]. The New Yorker Cartoon Caption Book (Kansas City: Andrews McMeel).

Michalko, M. [1991]. Thinkertoys: A Handbook of Business Creativity for the ‘90s (Berkeley, CA: Ten Speed Press).

“Provalis Research Text Analytics Tools,” available at: https://provalisresearch.com.

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