Marketing analytics is evolving to accommodate big data and artificial intelligence (AI). It is important to understand how big data and AI contribute to the value of marketing analytics for firms and organizations. Firms can gain competitive advantages through improved problem-solving and decision-making performance.  Increasing the competitive advantage for a firm can be achieved by effectively using the various skills and competencies of the firm’s marketing managers performing the insight generation tasks.  Firms can gain advantage by generating insights and intelligence that can answer a variety of important marketing questions.  So, the vast quantity of data becomes both a monumental challenge and a tremendous opportunity for the firm’s marketing managers. 

Marketing professionals need to understand the various tools and techniques used in the marketing analytics process leading to big data and artificial intelligence (AI) driven marketing analytics.  Big data, AI application, machine learning algorithms, marketing analytics toolbox can play increasingly important role in marketing analytics generation, interpretation, and application.  Marketing managers need to attain data and analytics driven marketing competencies and deep understanding of the detailed step-by-step process of generating, applying, and utilizing marketing analytics, as well as become more confident in making data and analytics driven marketing decisions.  Practical knowledge of generating and applying user-generated marketing analytics to various marketing decision problems with big data becomes critical for modern marketing professionals. 

Big data adds massive amounts of data and data types from various sources which can be used to support a varriety of actions over varying time-frames for the marketing managers.  Firms need to ensure that the marketing analytics are generated for fact-based decision-making and problem-solving and the generated insights from the various explanatory, predictive, and prescriptive analytic models are actionable.  To become actionable, the generated insights need to be connected to all aspects of the process used to enhance marketing problem-solving and decision-making in the firm.

Trends Favoring Marketing Analytics

The trends that facilitate or support the use of marketing analytics are emerging. These include the following trends.  First, there exists the availability of large amounts of data.  The sheer amount of information created in a day is huge and the pace of information creation is increasing.  The current amount of data created every day exceeds 2.5 quintillion (2.5 million trillion) bytes of data.  A second trend is the increasing realization that data is a valuable resource and should be managed as an asset.  A third trend is the well-established linkage of marketing strategy, data, and marketing performance.  High level strategy is translated into a concrete set of performance metrics allowing the implementation of the firm’s marketing strategy based on metrics.  Cultural change towards evidence-based management is becoming common in many firms as is the realization of the importance of fact-based decisions at every level of firm.  Access to large databases and user-friendly tools provided analysts and marketing managers with self-service analytics and sophisticated algorithms.  Yet, there exists the need for better marketing decisions.

Focus of Marketing Analytics Process

To fully grasp the marketing analytics process, managers can take unique tasks for practical illustration of big data and AI driven marketing analytics. 

• Focus on the big data environment of marketing analytics.  Managers need to learn the various sources of big data for marketing decision and what types of structured and unstructured data marketing professionals need to understand. 
• Focus on working knowledge of various analytical tools and techniques in the analytic toolbox.  These analytic tools can be used by marketing professionals to generate user-driven marketing analytics.  The same tools can be utilized by AI algorithms and AI service firms to provide AI driven marketing analytics to marketing managers.  Managers need to understand and evaluate analytic models and analytic contexts for practical understanding of those tools and techniques application. 
• Practical marketing decision application focus.  By linking the analytics solution to analytic tasks and analytic questions, marketing professionals can learn practical steps to follow in identifying big data sources, creating input data, generating appropriate analytics output, interpreting analytic solutions, and generating marketing insights for marketing decision making.  In addition, they need to learn how AI driven analytics can be applied and utilized to improve marketing productivity and performance with AI use. 

Analytics Driven Marketing Decision Perspective

Managers must maintain practical and application perspective.  Full understanding of the analytic tools and the models can ensure managers a position of being able to apply the tools and techniques to gain insights regardless of the specific software used by the firm.  Managers can utilize various statistical techniques, several programming languages, and databases.  The needed perspective is not running specific or available techniques or using certain specific software.  The new perspective is using the most appropriate tools such that the marketing professionals generate actionable insights for the firm.  For this purpose, marketing managers must have meaningful understanding of the fundamental concepts, analytic tools and techniques for marketing analytics, and how to generate, interpret, and apply marketing analytics to marketing decisions.  They also need to understand how AI powered analytics and machine learning algorithms can improve, extend, and enhance marketing actions.  The ultimate test is whether marketing managers can gain and apply valuable insights from marketing analytics solutions and make analytics-based marketing decisions Managers need full understanding of how big data and AI driven marketing analytics can be generated and applied to making market exploration, segmentation, and targeting, and forecasting decisions.  In addition, they must have working knowledge of how big data and AI driven marketing analytics can be generated and applied to tactical marketing decisions such as customer relationship management, product development, promotion optimization, customer post-purchase behavior management, and digital marketing decisions.