The Four Pillars of Big Data for the CMO
CMOs are continuously focused on carrying out their company’s vision of acquiring, growing and retaining customers. The transformative power of big data can help them achieve this. To develop a winning marketing strategy, CMOs extract meaningful data points from every customer touchpoint, including mobile and social media.
However, they are challenged with cutting through the data noise to find what makes their customers tick. Ultimately, CMOs need to understand how to manage the volume of customer data before implementing a marketing strategy that will benefit both customers and company profits.
Below are four attributes to consider in a data strategy:
Unifying Structured and Unstructured Data
The introduction of mobile and social into consumers’ daily lives contributes to the confusion and unprecedented volume of big data because it is classified as unstructured data. Structured data refers to data that has a defined length and format, can be categorized by numbers, charts and value in a pre-defined model.
For example, point-of-sale (POS) data, browsing history and number of clicks are all examples of structured data. Unstructured data, on the other hand, lacks a data model or pre-defined structure.
Consider the sentiment of a social media post, the meaning of a ‘like’ or a call center conversation. CMOs must pinpoint the data points – structured or not – that are the most critical to achieving company goals. This requires merging inputs from structured and unstructured data sources and translating that data into meaningful insights.
Personalization Strategy Marketing: Tools and Technology
CMOs that are truly looking for a competitive advantage and revenue growth must deploy data analytics with the right tools.
Data analysis highlights patterns, relationships and anomalies that help marketers build a customer’s profile, and provide CMOs with a better understanding of customers’ needs, wants, preferences and behaviors.
Take for example that 43 percent of shoppers in a recent study prefer shopping via desktop versus mobile phone because of the screen size. Having this insight about customers enables marketers to develop more targeted campaigns that engage shoppers via their device of choice.
After all, what good is it to send a promotional text to convert a shopper that prefers to purchase from a PC? This type of data would be lost without analytics tools that extract the most meaningful information.
Predictive Analytics
After analyzing data for historical trends and patterns, the natural next step is to use it to predict future behavior.
Understanding customer behavior through all touchpoints informs CMOs how well a product or campaign will fare, how the company measures up to the competition and whether or not customers will jump ship or stay loyal with a new initiative.
Marketers need the ability to assess how customers will react to a particular product or offer before it happens. Predictive analytics empowers CMOs to mold the company’s marketing strategy as customers’ needs and behaviors change.
Real-Time Insights, Real-Time Actions
We live in an ‘always on’ economy, so it’s important for brands to understand the intersection of big data analytics with real-time systems.
Combined with a flexible decision engine and contextualization, marketers can harness the power of real-time behavioral profiling and targeting while maintaining control over their personalization strategies.
CMOs can use real-time analytics and big data analysis to identify new customer segments and design the customer experience in a way that is meaningful and relevant for each individual.
CMOs embarking on a big data strategy should consider the attributes above to fully understand customers and expand profitability.
By monitoring, analyzing and testing each interaction with a consumer through underlying big data analytics, brands are better poised to deliver personalized experiences and results-driven marketing strategies.