We are living in the age of data, and all savvy marketers know that leveraging data effectively is key to better understanding current customers and attracting new ones, driving towards long-term relationships and ultimately staying competitive.
The amount of data available to marketers is vast and will only increase. At some point or another, marketing leaders are likely to have thought about whether they need a data scientist on the team to help them unify, manage and analyse data. Making this decision isn’t- or shouldn’t be- straightforward. There are several considerations for marketers before they seek out data science talent- something that, to begin with, is in short supply in many markets around the world.
The decision to hire a data scientist is likely the result of an evolution. The first step might be to hire analysts, whose job is to make sense of the data, identify patterns and generate basic insights. Next, you may look at hiring analysts with slightly more advanced technical skills, who know how to integrate data from different sources, and perhaps work with more unstructured data sources.
At this point, some marketing teams will consider hiring an experienced data scientist. A data scientist will be able to apply advanced artificial intelligence (AI) technology, including machine learning, deep learning, and optimization, to not only uncover deeper insights, but also automate and optimize certain business decisions. Therefore, an effective data scientist typically has fairly strong business acumen that allows them to align their work with the goals of the business. However, you won’t be able to attract or retain this talent without the right infrastructure for them to work with- they must have good data, and the tools to build and experiment.
For marketing teams without the resources to hire a data scientist, what about technology? Technology has proven it can support many other business functions, and there is technology available that strives to replace- or at least automate- some of the work that data scientists do.
In marketing, many of the challenges are standard across industries and organizations, and this opens the door to implementing technology. Marketing leaders need to look at the challenges they are facing and clearly assess which ones are likely to also be faced by other organizations. For example, all marketing teams want to gather and slice and dice data, and this is easily something that can be offloaded to technology. A good AI platform, for example, can help marketing teams jumpstart their data science capabilities by gathering data into one place and applying pre-existing AI models that can help predict churn, uncover ‘lookalike’ customers and identify new customer segments.
If your business challenges are unique and you require customized solutions, then you’ll need someone who can build specific things for you. There will be things within each company that are special to them, and you will want to dedicate resources towards the things that differentiate you- and not towards things that other people have already solved.
Eventually, parts of data science jobs will also be replaced by technology, but there will still be things data scientists will be useful for. For example, something AI doesn’t do very well is tie all the information and insights generated by data analysis to the business. Marketers will still need someone with a good sense of what the business needs who can ‘translate’ outcomes and next steps to the management.
Ultimately, CMOs don’t necessarily need to choose technology over talent- the best results will likely come from a combination of the two. Some companies will amass or collect different technologies and piece them together to solve problems, others will select people to work with technology to make it perform best for them, and those people could be data scientists, analysts or marketers themselves.
Charles Ng is vice-president of Enterprise AI at Appier.