It was a Thursday morning, only a few days after I had started at my new job at Atelier-Leo Burnett. I had worked at two other marketing agencies prior, but this was my first time working at a true advertising agency.
As I made my way to my desk, all I kept hearing people say to each other was, “You qualified! Congratulations!”, “I heard you qualified, how exciting!?”
Qualified? I was stumped…
My co-worker explained that our client had a set of test result scores that a concept had to meet before a commercial script could be produced. This was just the beginning of my experience with concept testing prior to production-and even commercial testing post-production for optimization opportunities. I have seen logos become bigger, end cards become longer, and some concepts have gone to die…all because concepts just didn’t hit the numbers.
A few years later I went to a new agency, and began working on a new brand, Covergirl. At the time, Covergirl was owned by Procter & Gamble, who like many other large consume packaged good companies, put a lot of weight on quantitative testing. That said, Covergirl was treated a little differently. Maybe because they were the only beauty brand in a mostly household-centric consumer packaged goods company, or maybe because Covergirl operated out of a Baltimore office vs. the Cincinnati headquarters. Either way, it worked to my team’s benefit. We were able to fore-go “qualifying” concepts prior to production. There was still importance on data analysis, but contextual, qualitative data worked hand-in-hand with quantitate data.
I am so fortunate that my clients understood the importance of context and feedback because it led to the creation of a piece of work that I am the proudest of.
As Anmol Rajpurohit said, “Almost all business problems have a qualitative aspect, and thus, quantitative analysis alone would never be able to tell the complete story.” We had a list of our “Covergirls” and their followers, stats and Q-Ratings (popularity scores). Instead of slotting in our top-rated celebrity talent, we considered the product benefits and creative concept and took a different direction.
After considering our audience was looking at bloggers and social media influencers just as much (if not more!) as celebrities, we reevaluated our roster for the creative concept we were planning to produce into a commercial. Again, it wasn’t just about looking at which makeup bloggers had the most likes, views and shares, but it was evaluating the context behind their numbers. Why were they trending? Did they have new techniques? What was different about them compared to other established makeup bloggers? What were people saying about them?
“We need to immerse ourselves in the substance that contextualizes big data and allows us to make sense out of it.” (Sean Donahue), and that is exactly what our team did.
This led to the launch of our So Lashy campaign-the project I am probably most proud of throughout my career thus far. We ended up with the perfect cast. We included 2 of our “Covergirls” with wide appeal, Sofia Vergara and Katy Perry. We balanced out our superstars with some up-and-coming young singers and trendy makeup artists with big social followings. Ultimately, this led to Covergirl being the first mass cosmetics brand to feature a male, James Charles, and a hijab wearing woman, Nura Afia, in a national television campaign.
While our team did heavily rely on data analytics like followers, shares and views, this concept would have never come to be without other context behind the numbers. We dug into comments, sentiments and even post content to craft the incredible, diverse So Lashy cast.
“It’s easy to rest on our laurels when the obvious answer might appear to be right in front of us. But disrupting the status quo is just as qualitative as it quantitative — in this case, it’s the only way that we make big data work.” And like Sean Donahue said, we were out on a mission to disrupt the status quo and breakthrough in the cluttered cosmetics market. This would not have been possible without analyzing big data by sifting through quantitative data and surrounding it with contextual qualitative sources.
Citations:
Anmol Rajpurohit; “Qualitative Analytics: Why numbers do not tell the complete story?”
https://www.kdnuggets.com/2014/02/qualitative-analysis-why-numbers-dont-tell-complete-story.html
Sean Donahue; “The Big Data Craze Is Just as Qualitative as It Is Quantitative”
https://www.huffingtonpost.com/sean-donahue/the-big-data-craze_b_5242788.html
Jennifer Layton 