Maximizing Digital Advertising impact and measuring social sentiment
1. Consumer Goods | Digital Market Intelligence
MAXIMIZING DIGITAL
ADVERTISING IMPACT AND
MEASURING SOCIAL SENTIMENT
The client
Our client offers sun protection products such as
oils, lotions, after-suns and lip care to sun lovers
around the world.
Situation
The global sun-care market is growing rapidly.
More and more holidaymakers are seeking out
the sun, and they are increasingly aware of the
risk of not protecting their skin. This €7 billion-a-
year category has grown at an annual rate of 5%
since 2008, and competition between the leading
brands is fierce.
Our client wanted to analyze and understand how
effective its marketing and advertising campaigns
were in the Italian market. The company needed
to know whether sunbathers understood the
brand’s promise of helping them develop a tan
that is the envy of everyone else on the beach.
Approach
We conducted our research in two stages. The
first aimed to paint a picture of how Italian
consumers perceive the brand.
We tracked exposure to the company’s digital
campaign and asked people who were exposed to
the advertisements about their awareness and
perceptions. We interviewed 450 people, with
women representing 60% of our sample. The high
number of female respondents alone gave us
valuable insight into the demographics of the
campaign audience.
Our research empowered this
brand to better target existing
and new audiences in Italy, and
to engage with them more
effectively.
We set up test and control groups for side-by-
side analysis among people who regularly use the
brand products, and those who don’t. We also
compared the people who were exposed to the
advertisements against those who were not.
In the second stage of the research, we employed
our RPL Social Media Intelligence methodology to
gain a view of the brand’s digital position and
reputation within the Italian market.
To get an accurate picture of what Italians
were saying about the sun-care brand on the
Internet and in social media, we analyzed and
evaluated current and historical search terms. We
used human coding (social data cleaned and
categorized by skilled people) to identify and
classify both positive and negative sentiment
about the brand.