Big data is one of the most powerful tools available to develop and execute a marketing strategy. A robust strategy begins with well-defined goals, audience insights, buyer journeys and content mapping. Big data can help answer questions in all of these facets of a strategy, but the single most effective use of big data to help develop a marketing strategy is in deriving audience insights.
Prior to the availability of big data, audience insight development started with a review of secondary research and was supported with anecdotal stories from the sales team or customer service. As the need for deeper audience insights increased, existing findings were complemented by primary quantitative research and ethnography or other qualitative studies. These methods all lead to insights, but due to the sampled nature and often slow process, a true picture of each customer could never be painted.
With the introduction of big data analysis, patterns of action (and inaction) can be tracked, grouped and modeled to see which consumer traits are most correlated with product sales or other outcomes, with the ultimate goal of identifying the highest-value customers. These customer segments often break from the traditional view of customers because they have fewer biases and look beyond what might normally be seen. Insights that might never have been uncovered by the best researcher can be proven or disproven in minutes using big data.
Big data can quickly transition audience insights into action. By aligning data findings with data management platforms and demand-side platform (DMP/DSP) solutions, a connection can be made to the exact audience needed and the right message can be delivered to the right people. The audience insights from big data allow for narrow targeting, reducing marketing waste and improving business ROI.