Data science and analytics will be the technical skills most needed at digital ad agencies worldwide in the next two years, according to a poll by Marketing Land.
As digital ad buying becomes more automated and data-driven, marketers need to improve their data skill sets. In a survey of US marketers by Blueshift and TechValidate, 54% of respondents said one of the main roadblocks preventing them from making better use of customer data was insufficient data analysis capabilities. And an Adestra and Ascend2 poll of US marketers showed that 43% of respondents outsource their data management.
Over the past five years, 67% of marketers have significantly increased their focus on data and analysis, according to research by YouAppi and Dimensional Research. However, competition for talent is still the second-leading challenge facing agencies, according to Marketing Land’s poll. (Clients moving services in-house was cited as the top challenge.)
Together, these studies indicate that marketers’ demand for greater data analysis capabilities is outpacing the supply of talent that can address those concerns.
According to eMarketer principal analyst Nicole Perrin, “The advances in marketing technology, including techniques like machine learning, don’t mean marketers can just collect data and have a computer tell them what media to buy and how. Data scientists play a big role in setting data-driven strategies, and we’ve heard for a while that big, rich tech companies have snapped up a lot of the top talent.
“But as advanced attribution techniques become more common, roles throughout the marketing department will require competence in understanding and manipulating data. Digital strategists should be educating themselves and their teams and becoming conversant in the types of data they collect and the models that data informs.”
Aside from talent issues, a few other challenges prevent marketers from investing more in data science. Nearly half of the US brand advertisers and agencies surveyed by Advertiser Perceptions and MiQ said that investing in data science is cost-prohibitive. And a Burtch Works study found that mid-level data scientists have a median salary of about $130,000.
A similar number of respondents reported that a lack of accurate measures of business impacts restricts their data science investments.