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Data Science and Predictive Analytics Strategy for Marketing

By developing a data science and predictive analytics strategy for marketing, organizations identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance. Data science helps uncover new business opportunities and delivers new insights that help an organization increase marketing campaign effectiveness, maximize customer and product profitability, minimize customer churn, and detect fraud. 
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Data science and predictive analytics can help marketing pros know where to find the new revenue opportunities and which product or service offerings are most likely to address the market requirement. The goal is to leverage both internal and external data - as well as structured and unstructured data - to gain competitive advantage and make better decisions. To reach this goal an organization needs access to professional data scientists and new data analytical technologies.
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Data science refers to the scientific study of the creation, manipulation and transformation of data to create meaning. Business analytics is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. Predictive analytics turns data into valuable, actionable information. Predictive analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring and encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.
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In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
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An example of using predictive analytics is optimizing customer relationship management (CRM) systems. They can help enable an organization to analyze all customer data therefore exposing patterns that predict customer behavior. Another example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. 
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A recent study from the Economist Intelligence Unit (EIU) suggests that business leaders rate creating a data strategy for marketing and predictive analytics as one of the most important priorities. Regarding marketing skills, 37% of executives said that "using data analysis to extract predictive findings from 'big data'" is most important. Yet marketing pros often lack the data science and business analytics skills to get valuable, actionable insights from both large and small data sets. Further, many organizations lack an information, technology and data science strategy.
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"This change in the required skill set ... has created a challenge for marketers as 45% of executives now view marketers' limited competency in data analysis as a major obstacle to implementing more effective strategies -- second only to inadequate budgets for digital marketing and database management." The EIU report also suggests that firms are underestimating their customers' privacy concerns: 33% of consumers say they are "very concerned" about the privacy of their information stored in companies' databases yet only 23% of executives say their organization's customers are very concerned about privacy.
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The solution is to engage expert professionals to help develop an information, technology and data science strategy. Further, training marketing pros to use technology, work with data scientists (both internal and external) and developing business analytics skills to get valuable, actionable insights from data is critical.
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Hiring both internal and external data scientists is optimal. Internal data scientists can develop specific domain expertise and help train staff. External data scientists can bring objectivity and fresh perspectives to business challenges as well as create a check and balance or auditing function. 
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If an organization is unable to hire internal data scientists, engaging external data scientists is a viable option to quickly form a data science team and scale-up big data projects without the upfront CapEx of hiring data scientists in-house. Knowledge transfer and training may be included. Data scientists can be engaged on a time or fixed fee basis and be responsible for deploying, managing and scaling the data science and predictive analytics projects. 
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