Aruna Paramasivam, Head/Data Acquisitions & Partnerships at L'Oréal USA, discussed this subject at the Interactive Advertising Bureau’s (IAB) Data Symposium.
One key consideration for the company in this space, she reported, incorporates the quality of the numerical resources made available to its brands.
“For us at L'Oréal, data quality is about getting a very clear picture of who our consumer is, and being able to get in-depth datapoints on our consumers,” Paramasivam said. (For more, read WARC’s in-depth report: L'Oréal USA’s data-driven marketing formula.)
“We’re looking at data as directly from the source as possible, [and] being able to understand how it was sourced. And because we are a beauty company, 96% of our consumers are women. So, we are also very much into gender breakdowns.”
Another priority involves ensuring that the data provides compelling “signals” to follow, based on powerful insights about the behaviour of a given brand’s target audience.
“We obviously like very clear signals,” Paramasivam said. “We like to know how they’re sourced. We like them to be deterministic.”.
Hitting the right scale is essential, too. For programmatic campaigns, for instance, the aim is to find a million consumers per segment on an exchange, whereas a promising target cohort identified by L'Oréal’s data scientists may be far smaller.
“It is that age-old dilemma of reach versus scale that we’re looking to achieve. But, also, relevancy is really important to us,” said Paramasivam.
Among the company’s other considerations are a data feed’s “freshness” and “speed”, she continued – especially at a time when consumer habits are changing so rapidly.
“Speed is important, and that’s where the freshness of data comes into play,” Paramasivam said. “And how much work we are willing to put towards getting that [data signal] is also related to our basket size, and the type of consumer that we are looking to reach.”
Sourced from WARC