The lines between market research and marketing data are blurring. This really comes as no surprise, however. The convergence of these two disciplines was bound to happen at some point. While both industries are grappling with the implications of what this phenomenon means for the way they conduct research, startups shops are seizing the opportunity.
The good thing about change is that it opens a dialogue about what needs to happen next. So I wonder, as data gets bigger, will it become more inclusive or has Hispanics and other minority populations, yet again, been left out of the conversation?
And here’s why I ask. The infrastructure for the future of marketing and marketing data is being built right now via big data. However, the construction of this infrastructure is being built by non-Hispanic whites who are sourcing tremendous amounts of data but excluding large segments of the population, like minorities.
So how are minority populations being left out? Let’s take a look at Hispanics specifically. Mobile is the largest generator of big data for marketing purposes. We know that Hispanics over-index in mobile consumption. So, there should be a sizable amount of marketing data available on them, right? Wrong.
And here’s why:
1. Location-based targeting One of the most promising marketing tools to come out of the big data revolution. The ability to geofence locations and send appropriate marketing messages to consumers in real-time has transformed the consumer experience. Consumers can now, for example, walk down the street, pass a local coffee shop, and receive a push notification for a coupon to purchase a hot chocolate.
Yet, while promising, geo-fencing requires investments in time and money that some minority-owned business may not have access to or be aware of. The coffee shop owner in the example would not only have to be registered on Google maps (or a similar technology) but also work with a provider of mobile advertising well-versed in proximity marketing campaigns. If you count the fact that many Latinos shop at mom and pop stores that don’t have the budget for this type of marketing, you end up with a segment of the population underrepresented in the data collected from this marketing channel.
2. Spanish Search Search trends, SEO, and search targeting are nothing new but coupled with big data analytics, search is becoming more powerful than ever. Spanish search in the U.S., however, is still largely overlooked. While the multicultural team at Google, for example, is trying to change that, just looking at the CPCs (cost per click) for Spanish search terms vs. English search terms will give you a sense of where the market is.
Businesses have been slow to invest in Spanish search marketing and more broadly Hispanic digital efforts. This lack of investment has led to an underdeveloped pool of Spanish search data to pull from. In other words, English search is robust and ready for big data to take it to the next level from a marketing perspective. Spanish search data, by comparison, is not.
3. Transactional Data Android, Apple, and Samsung Pay are generating extremely valuable data for marketers as we can now close the loop in the path to purchase from an insights perspective. And while Hispanics over-index in smartphone usage, they also over-index in cash transactions vs. other cohorts which means their transactional data isn’t being recorded by mobile payment or credit cards. This leaves a huge gap for marketers and can lead to issues for market researchers relying on transactional big data for retail marketing.
While the implications of big data decision making in society at large are significantly more consequential outside marketing (such as criminal justice and financial sectors), as multicultural marketers, we must be aware of the potential hazards of relying on big data marketing products that do not accurately represent minority populations.
To do so, in my opinion, steers us right back to a time when we relied on stereotypes and overused tropes, which resulted in culturally irrelevant marketing messages. That may be overstating it a bit, but you get my point. Big data is only as good as the sum of all its parts.