Artificial Intelligence

Most marketing teams are leaving a lot of money on the table. According to Sitecore, the average US brand collects eight pieces of data per user, ranging from address to behavioral insights. Brands are collecting an extensive amount of data at various stages of the customer journey. Data science helps us leverage this data into actionable insight that results in a greater return on investment.

Data science methods like machine learning, clustering, and regression have moved marketing from a creative domain to a scientific one. By leveraging data science, marketing teams can extend their top-funnel approach to a full-funnel one and uncover product and customer insights at scale in an unprecedented way. To do this, growth marketers should understand what data scientists can and cannot do as well as some of the methods and how marketing teams use data scientists.

Over 40% of digital grocery shoppers during the lockdown were new to online grocery shopping prior to the pandemic, according to a Business Insider report. According to emarketer.com, both “food and beverage” and “health, personal care and beauty” were the fastest-growing eCommerce categories prior to the pandemic. Forecast for food and beverage sales were raised from 23.4% to 58.5% and health, personal care and beauty sales from 16.6% to 32.4%. Of new grocery online-shoppers, 68% would continue to shop online in the future, according to another study run by Aki Technologies and TapResearch.

Travel Industry

Strong consumer brands (like Nike), that stayed emotionally connected with their fan-base during the pandemic, have witnessed a lift in online sales. That  trend isn’t expected to fade away.

Most marketing teams are leaving a lot of money on the table. According to Sitecore, the average US brand collects eight pieces of data per user, ranging from address to behavioral insights. Brands are collecting an extensive amount of data at various stages of the customer journey. Data science helps us leverage this data into actionable insight that results in a greater return on investment. 

Data science methods like machine learning, clustering, and regression have moved marketing from a creative domain to a scientific one. By leveraging data science, marketing teams can extend their top-funnel approach to a full-funnel one and uncover product and customer insights at scale in an unprecedented way. To do this, growth marketers should understand what data scientists can and cannot do as well as some of the methods and how marketing teams use data scientists.

What Data Science Is and Is Not?

There is a lot of confusion about what a data scientist does and does not do. Specifically, people often interchange the terms of data science and data analytics. The easiest way to differentiate between the two is that a data scientist looks to predict the future, while a data analyst looks to summarize the past. Data scientists make predictive models using regression, machine learning, and other advanced statistical methods, while a data analyst uses descriptive statistics to analyze past patterns. 

A data scientist is not a software engineer. Their programming ability is enough to run machine learning and statistical analyses they need using platforms like R, Python, and SAS, but not to develop software or manage infrastructure like an engineer would. Data science is the intersection between business expertise, programming, and statistics, where programming is simply a medium to derive insights using statistics and business or domain expertise. 

The data scientist toolbox uses artificial intelligence and mathematical modeling to unlock a new set of insights. A marketing data scientist can answer questions such as: Who are your most promising customers? What choice alternatives do consumers of your product have? How do people feel about your brand? What other products do your customers want to buy? By leveraging the data scientist, a marketing team can eliminate waste and target customers in ways that are cost-effective and personalized. 

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