How to improve the relationship between marketers and data scientists

How to improve the relationship between marketers and data scientists
Coralie launched and leads the data science and research and development team at Sublime Skinz. She is responsible for data collection, data wrangling, and data enrichment for both first and third-party data, but also the design of complex algorithms for analysis, classification, and interpretation of data to enable data-driven decisions. Coralie is in charge of international partnerships in the domain of research and innovation including CIFRE, grant applications, and academic collaborations.

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A decade ago, few businesses had heard of data scientists, let alone employed one. 

Yet as an invisible revolution in technological tools has transformed every aspect of modern marketing, their skills have become essential to decode complex audience behaviour and turn disordered information into effective strategy.

Data science expertise is now in high demand. The vast majority (83%) of data scientists believe there aren’t enough of them in the profession, and a substantial increase (29%) in data scientist salary offers over the last 18 months illustrates how demand is outstripping supply.

Microsoft has even launched a data science curriculum in its degree programme in an attempt to close the skills gap.  

Data scientists are fast becoming an essential member of any marketing team, with the insights they generate used to enable customer centricity, personalisation, and a granular view of the customer.

Data is also vital in making marketing more accountable and being able to quantifiably measure the impact of a campaign. But interactions between traditional marketers and data scientists are not always smooth, frequently characterised by miscommunication and misunderstanding. 

So how can the relationship between marketers and data scientists be improved to achieve optimum business results?

From a marketing viewpoint

There is a common misconception that marketers are unable to understand the work of a data scientist, but this is far from the case.

Given their knowledge of the industry in which they work, the majority of marketers should be able to perform very simple data analysis themselves, and are more than able interpret the insights gleaned by data scientists who use more complex analysis.

Communication is key for marketers to understand the work of data scientists and regular meetings are essential to make sure both elements of the team are working towards the same businesses objectives.

All too often the people who know what the business needs are do not understand the data, and the people who do understand the data don’t know what the business needs.

Marketing data comes from real people and so inevitably needs a flexible human mind capable of devising creative solutions to analysing it 

Marketers must be specific about the questions they want answered through the use of data – after all data science is about what the data reveals and how this can be used to make marketing more effective, rather than the actual data itself.

Marketers and data scientists must also work together to ensure they are making best use of the data available, that the data is of a sufficient quality, and that the right tools are available to deliver the necessary level of insight.

From a data science viewpoint

While the key skills necessary in data science are mathematics and data-analysis, this cross-functioning, inter-disciplinary role also requires development of a wider skillset.

Successful data scientists need a level of business acumen, and are as likely to be looking at profit and loss statements, market-share predictions, and churn rate reduction plans, as they are statistical models and algorithms.

Perhaps most importantly, data scientists need to work on their ability to communicate effectively. The capacity to convey complicated concepts in terms marketers will understand is a vital skill data scientists must acquire, for the partnership to succeed.

Data scientists must be storytellers and use the insights they generate to tell a compelling tale that resonates with marketers and answers their pressing questions.

Unlike traditional business analysts – who rely largely on rigid processes – data scientists must also embrace a degree of creativity when working in marketing teams.

Marketing data comes from real people and so inevitably needs a flexible human mind capable of devising creative solutions to analysing it and producing the required insights. Data scientists must be willing to look at data from a fresh perspective to meet the needs of the team.  

As marketing becomes ever more data driven it is vital to forge strong relationships between marketers and data scientists as this partnership will only continue to grow in importance.

As long as marketers attempt to understand the work of data scientists, and clearly define the questions they need answering, and data scientists develop a skillset that includes business acumen, communication, and creativity as well as mathematics, the relationship between marketing and data science can continue to flourish. 

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