Win with Advanced Business Analytics: Creating Business Value from Your Data

ADVANCED DIGITAL ANALYTICS

Digital analytics and traditional marketing analytics (such as media mix modeling) are well-suited and highly compatible disciplines—in the same way that machine learning and statistical analytics are complementary and symbiotic with each other. In the digital age, analysis is both an input and an output of research. Several macro tactics exist for the advanced analysis of digital data:

• Longitudinal analysis, where the business examines data during a number of time periods and compares the variables (the input) in each of those time periods to one another.
• Cross-sectional or segmented analysis, where the same data are compared side-by-side against the same or different input and evaluated against each other.

Each of these tactics is useful and applicable for advanced digital analysis—and a combination of both methods, in the context of statistical and machine learning and exploratory data analysis (EDA), is applicable to digital analytics.

Analytics, when functioning to generate revenue or reduce cost, is symbiotic with traditional research methods as well. Digital analytics and traditional analytics, when integrated together, can inform business, brand, product, and marketing strategy in a grounded way that is based on empirical, observed, and direct evidence, which can be used to generate analytics insights that answer the following questions—according to Joel Rubinson, the leading global researcher and founder of Rubinson Partners:

• How do I grow ...