Analytics rock stars are essential in the world of big data marketing and measurement. These big data heroes are in high demand, but supplies are low on the ground.
With 70% of marketers rating their ability to assess the impact of one channel on another as either "poor" or "fair," it is clear that although the technology exists to collect, integrate and process big data, analytics heroes boasting the skills to unlock and interpret the power of this data need to be retained, nurtured and developed.
As the number of marketing channels opens up, the availability of data is increasing exponentially. This big data needs taming, because when cracked and interpreted correctly, it can provide the crucial insights that lead to true marketing success. And data scientists - who have the sexiest job of the 21st century according to the Harvard Business Review - are the specialists able to do just that.
So what makes a marketing analyst great and how can brands nurture their data science talent to ensure retention?
The top traits of top marketing analysts
When a brand stores multiple petabytes of customer data and customer interaction data, marketers have a big data opportunity at their fingertips. Pulling the right information from this big data takes a specialist set of skills, including:
• Analytical prowess: It is vital for candidates to do more than just report their findings. True analytics rock stars study the information in front of them, question it, understand whether it is relevant, and then use it to make informed and effective marketing decisions.
• Industry expertise: Big data heroes have the industry knowledge to draw conclusions from the data within the context of the business, as well as the ability to spot and understand irregularities. Without this understanding, analysts often apply data incorrectly, resulting in inaccurate conclusions.
• Compelling storytelling: An often overlooked, but incredibly important element of a data analyst's job is the ability to present findings as a compelling and actionable marketing and/or brand story. Even the most significant findings can be overlooked if they are not presented in a clear and understandable way. Therefore, excellent communication skills are vital for any successful marketing analyst.
How to nurture this newfound talent
In an industry where demand outstrips supply, analysts with the necessary skills to unlock the marketing potential within an organisation's big data are hard to find. Brands, therefore, need to foster and retain the talent they already have. To nurture and develop the analytic superstars already walking the corridors, brands must:
• Provide access to learning: While doing actual data analysis is the best way to nurture and develop staff, providing mechanisms for continuous learning is another good way to further build upon the skills and knowledge your analytics team already has. Industry groups like the Digital Analytics Association (DAA) can help build knowledge and competency in marketing analytics, as well as enhance communication and presentation skills that are essential for delivering big data insights in a clear and understandable manner. Free online classes from Coursera and Code Academy can help too.
• Provide clear evaluation: Analytic team leaders need to clearly define the knowledge, skills and capabilities required of their team to ensure that all team members have a clear understanding of expectations, and improvements that may be required. This will both enable and encourage growth and development of analysts.
• Create case studies: Case studies can be a powerful tool for teaching core skills to analysts. But they provide much more than just education: case studies can help prove the value of analytics internally, whet people's appetites for more, and help to build the case for investing in new analytics hires.
Analytics rock stars may be elusive, but the talent they bring to the table is essential for harnessing big data so it can be turned into actionable marketing insight. And with the demand for big data heroes outstripping supply, brands must be willing to go that extra mile to retain and foster the great data scientists they already have.
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