Customer engagement has undergone a metamorphosis as of late, due in part to the prevalence of “big data.” This phrase is being used to describe the massive amount of data being generated through the increased use of social media and many other cloud-based services that capture data.
According to this Chief Marketer report, other data-capturing devices, such as smartphones and the apps associated with them, are only adding to the trove of information available for customer engagement. At the same time, companies such as Haddop and Vertica are further advancing the importance of the relationship between customer engagement and big data by their abilities to store all this data.
Analytic model execution, which often requires technology that can handle parallel processing, is not always the most optimal choice for analyzing big data. The parallel processing relies on hardware that must be high powered. Another drawback is that big data requires plugging data into the analytics.
Big data also poses some issues in master data management. A big part of customer engagement and customer data management is matching and mastering data. Big data, however, can cause some issues in this process as master data management generally looks at items such as income, age, and name, as opposed to the bigger ticket items such as financial transactions and phone calls that are related to big data.
A customer engagement area that sees promise is relationship analytics, which includes social graphs and influencer analytics. Finding out the relationship between customers and their influence can provide valuable insight that helps drive successful marketing campaigns.
Correlation and clustering analytics are especially relevant to big data. This sort of analytical correlation can help in the prediction process, which tells marketers how a consumer will respond to different pitches.
This type of targeting and messaging occurs when companies can analyze the data that shows how certain consumers operate on a specific website – how they navigate around it and how much time they devote to various parts of the site.
The information inherent in big data is often behaviorally oriented. Tracking behaviors includes tracking time, which gives an indication of interest of the consumer. It only makes sense that time-series analysis would help provide more accurate forecasting.
Tracking the time series can also show certain abnormalities in behavior that can be used in favor of those analyzing the data. When a customer’s behavior changes, the change is apparent in the data, and the way that customer is approached can change to more accurately appeal to that customer.
Data gathering and analytics is essential to optimal customer engagement, as long as the enterprise knows what to do with the data once captured. Plugging it into analytics and turning it into actionable business intelligence will go a long way toward building out valuable relationships.
Susan J. Campbell is a contributing editor for TMCnet and has also written for eastbiz.com. To read more of Susan’s articles, please visit her columnist page.Edited by Carrie Schmelkin