The era of big data is alive and well. It is leading to better and faster decisions in a diverse set of industries – from disease detection and insurance, to stock trading, crime prevention and election forecasting. Big data applications are powering self-driving cars and winning at Jeopardy. Today, businesses have access to an unprecedented pool of insights but face a growing challenge: data abundance. With data production expected to be 44 times greater in 2020 than it was in 2009, the question becomes how to make sense of it all.
Thankfully, advancements in analytics technology are keeping pace with the data explosion. Even so, data scientists are becoming more important than ever for businesses that want to stay ahead of the competition. It’s critical to have both the technology and a team that understands what data to collect, how to best collect it and how to unearth insights from all that data. Perhaps the greatest challenge is the ability to communicate data findings in a way that is compelling. You need a data analyst and a storyteller. Good luck with that.
A McKinsey study predicted that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
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With such a shortage on the horizon, and recognizing the importance of building teams that can thrive in today’s data-driven world, organizations of all kinds are courting candidates with the tech knowledge and analytical capabilities to derive meaning from complex data sets.
Earlier this year, the White House hired its first-ever US Chief Data Scientist to help the government harness the power of innovation and big data to better serve the American people. Shortly thereafter, the University of Rochester assembled a team of top tech leaders to advise the University’s new Institute for Data Science. In private businesses as well, leaders are ensuring that skilled data experts fill the new hire roster and are investing in their talent. Results from a Burtch Works employment survey show that both demand and salaries for data scientists have been steadily rising over the past few years.
It’s clear that the need for data scientists is skyrocketing and that investing in this talent is a wise move. While your first thought when looking at data science hires may not be your marketing department, it should be. In marketing, effectively using data to understand customers and predict buying behavior can make the difference between a winning customer experience and a failing one that lives forever on social channels.
Data and the age of the customer.
We’re in the age of the customer. With buyers spending 57 percent of the buying process researching on their own, and 70 percent of the journey taking place before even engaging with a sales rep, it becomes the job of the marketer to accurately predict what content will best attract and engage buyers. Study after study has shown that today, broadly-focused marketing campaigns inevitably see less success than those that are focused and personalized. Fortunately for marketers, data holds the key to predicting buying patterns and responding to the needs of potential buyers with personalized content that influences purchase decisions.
With the right data, marketing teams can pinpoint the most promising leads and develop highly-targeted messages and campaigns. To start with, the foundation of any modern marketing program lies in predictive marketing and sales technology applications. These applications identify and prioritize leads at every stage of the buying cycle and provide real-time insights to help tailor outreach. Beyond the technology, though, it’s just as important to take the next step and translate data findings into business decisions.
This is where data science comes into play.
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Data scientists have both the technology know-how and business acumen to drive marketing teams to key discoveries about prospects and customers that can drive revenue growth. Their comfort with numbers and understanding of the complex mathematical equations behind the technology mean that they can recommend new or different analyses that will best guide marketing campaigns and help the sales team close more deals.
Data scientists understand that different data measure different buyer actions and qualities. Intent data is the data collected on prospects long before they engage with your brand (ex: keywords searched, sites visited), while behavioral data shows how prospects have engaged with your brand (ex: emails opened, content viewed). Fit data is demographic data collected about prospects (ex: company size, growth, credit score, industry classification). Separately, all three types of data are important, but it is when they are used together that they are most effective. Data scientists not only understand different types of data, but also how they interact together so that outreach can be based on carefully-constructed buyer profiles.
What’s more, data scientists have the knowledge to crunch data that informs and results from A/B testing – something that’s critical for optimizing marketing content.
For a modern marketing team to successfully execute on campaigns, they must be equipped to support the symbiotic relationship between technology and human intelligence. One cannot flourish without the other. Without technology, we would lack the clouds of data that inform outreach decisions. Without data science, we would lack the insights that escape algorithmic automation, and the ability to translate data insights into effective decisions.
Big data is bringing big changes to marketing. The companies and executives that truly move the needle for their industries are those that prioritize customer engagement and strive to achieve a contextual understanding of customers at every touch point. For that, data science is a must-have on your marketing team.
Related: Big Data Is No Longer Confined to the Big Business Playbook
