Big data has opened up a world of possibilities and among them is identifying the ideal buyer profile. Marketers can compare prospects to an ideal profile to prioritize engagement and increase conversion rates. Modeling an ideal buyer profile is not a new practice; however, it contains a large element of guesswork, with the marketer going more by instinct and past trends.
Big data allows the marketer to obtain a 360-degree view of all the customers, extracted from in-house customer records, social media platforms, blogs, websites, public and paid databases, and other sources. The big data analytical engine automatically extracts the common characteristics associated with such customers, to develop an ideal profile that would most likely purchase.
Marketers can whet a new prospect against such an ideal profile or generate a targeted lead list with only relevant entries that match or closely match the ideal buyer profile, and, therefore, maximize the ROI of the marketing initiatives. The use of a big data-based ideal customer profile doesn’t need to be confined to generating “super-optimized” lists alone.
The big data engine captures details of the engagement with prospects and empowers the marketer with predicative analysis. Big data analytics use the results of an engagement initiative to predict how the same or other prospects would most likely respond to a similar engagement strategy. The analytical engine also scours the demographic, economic and other characteristics of such prospects, and prompts the marketer to replicate the same successful strategy with other prospects having the same characteristics.
Marketing automation is a great way of combining the ideal customer profile and big data into one place. What facts are you missing about your customers and prospects that a marketing automation solution can help you identify?
