Most B2B marketers feel flustered as they struggle to meet targets. They face the pressure of ensuring the effectiveness of their campaigns and making productive use of their time. Big data can make their life easy on both these fronts.
Big data holds the potential to apply new approaches to old problems, especially where there has been no viable solution in sight due to lack of information.
Banks and financial institutions, for instance, have used big data successfully to develop credit scores, which offer insights on a person’s financial habits, thus allowing them to make informed decisions on loan applicants. Marketers can apply the same methodology to their prospects, to predict their purchase or decision-making habits, and engage with them more effectively.
Here are a few areas that marketers have identified that can help your marketing efforts:
· Big data can identify the need for specific products and services, to shortlist leads who would most likely convert.
· Big data throws insights into the prospect’s buying quantity, probable buying times, time taken for conversion, purchases at different price ranges, and much more.
· Big data helps to score leads, to determine which prospects are ready to advance to the next stage of the customer lifecycle.
· Big data help marketers prioritize which prospect to engage first, not just on the “worth” of the prospect in terms of purchasing size or capacity, but also on other factors, such as whether the prospect would likely move away if not engaged immediately, and so on.
· Big data allows the marker to predict the type and nature of engagement that would click with a prospect. For instance, some prospects may appreciate a white paper or a case study to gather information about the product before engaging with the brand for additional information. Another prospect may prefer all the information upfront and during one interaction.
All these possibilities exist only when the specific marketing objectives are decided in the beginning. The marketer needs to be sure what exactly they are looking for, and ensure that the big data analytics are structured in this direction. Failure to do so may result in irrelevant analysis, making it an exercise in futility.
Interested in learning how data can predict who your customers will be?
