Why ask 20 people for their opinion when you can get insight from hundreds continuously?
The problem with traditional market research is that it's based on approximations, extrapolations, and conjecture. The typical output is a blended average answer. When such an answer is used to guide the development of a product or service, the product or service is average as well. Great companies don't have this problem because they don't perform market research using flawed and inaccurate techniques like surveys or focus groups. Instead, they do continuous market and customer research by listening intently and constantly to what customers tell them. Then they build internal processes that enable them to turn these ideas into new features and functionality and rapidly roll them out.
These days, with social networks, digital tools, and users' habits of conversing online, there's absolutely no need to be stuck asking 20 people when hundreds upon hundreds of users provide feedback constantly. It's just a matter of knowing where to look and correctly interpreting what you see.
Metrics, Metrics Everywhere
Now that many consumers are comfortable talking online, new and incredibly detailed metrics are available to any marketer to derive insights from. These metrics reveal actual behavior and preferences, not answers made up by participants in the artificial settings of focus groups and surveys. For example, on Lifehacker metrics reveal how many people viewed a blog post and how many commented. These are indications of actual interest by community members. Twitter's trending topics and Google Hot Trends reveal what people are actually tweeting about or searching for any moment in time. Twitter intensity indicates what people think are worthy of sharing. Search volume shows what people are interested in.
Such metrics as the number of reviews for one product compared the number for another product with identical technical specifications reveal consumers' informed choice. On Newegg, for instance, two times as many visitors purchased one 20-inch LCD monitor as another 20-inch monitor. Data from Amazon.com reveal what consumers perceive as equivalent or related products by showing which related products people also looked at: people who looked at this digital camera also looked at these other digital cameras.
Tying Metrics to Business Objectives
The wealth of metrics and information online has created a new challenge: figuring out which metrics to use and what business objectives they can shed light on. For example, if our business objective were to get physicians to read our scientific content, we could use time spent on the article's URL to determine if they read it. If users spent an average of 15 seconds with an 80-page document, we know they didn't read it. Once we know that, we can ask why they didn't read it. Could they not find it when they searched? Did they not understand our choice of words? Or did they not even start to read it because it was one solid block of text?
With the digital medium, we can run experiments and use real-time metrics to see if our hypotheses were right or wrong immediately. In one example, eye-tracking studies revealed that by adding paragraph headers and layering to a long article, users skimmed the headers and then read further down the article, compared with no one even starting to read content without headers and layering. Such a rapid hypothesize-experiment-optimize cycle enables companies to incorporate fixes and new ideas from customers nearly as rapidly as they come in. The speed of innovation is key to not only staying ahead but also to surviving.
Insights for Innovation and Business Opportunity
Beyond insights that can lead to improvements in current products and services, metrics can also yield new ideas and business opportunities. For example, in Brazil Fiat is not only turning to consumers to help design its Mio car but also seeking input from customers on how to best market and communicate the new car, according to this article. New business opportunity is also revealed by search volume trends. For example, "chocolate covered cherries" are more searched for in December while "chocolate covered strawberries" are more searched for in February, indicating that a chocolate store owner can show appropriate marketing for each or adjust inventory levels in their respective seasons.
These publicly available metrics are generated by hundreds, if not thousands, of people. Customers generate these data by way of their actions, not what they say. Such observed data is always more accurate than surveyed data.
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Dr. Augustine Fou is the senior digital strategy advisor to CMOs, marketing executives, and global brands. Dr. Fou has over 15 years of Internet strategy consulting experience and is an expert in social media marketing strategy, data/analytics, and consumer insights, with specific knowledge in the consumer packaged goods, financial services/credit cards, food/beverage, retail/apparel, and pharmaceutical/healthcare sectors.
He is a frequent panelist, moderator, and keynote speaker at industry conferences. Dr. Fou is also an Adjunct Professor at NYU in the School for Continuing and Professional Studies and at Rutgers University at the Center for Management Development, where he teaches executive courses on digital strategy and integrated marketing.
Dr. Fou completed his PhD at MIT at the age of 23. He started his career with McKinsey & Company and previously served as SVP, digital strategy lead, McCann/MRM Worldwide and group chief digital officer of Omnicom's Healthcare Consultancy Group (HCG). He writes a blog "Rants, Raves about Digital Marketing" and can be found on Twitter at @acfou.
December 12, 2013
1:00pm ET / 10:00am PT