If you are starting your journey as a marketer using big data and wondering about the specific applications of predictive analytics and how it could impact your marketing decision making process here are a few ways to think about it.
 
The key imperative of embarking on the journey into big data and predictive analytics is “actionable insights that help achieve marketing goals”.  Predictive analytics using machine learning will help you extrapolate past behaviors, reveal patterns and help predict future behaviors.
 
A. Simple Applications  
1. Lead Conversion : The life-blood of sales is lead conversions and its common sense to conclude that not all leads have equal potential to convert. Predictive analytics can help you identify from past conversions the ideal demographic/ behavioral characteristics of a strong lead vs a weak lead. Prioritising leads in this way will help sales teams create a plan for engagement based on immediacy of conversion.
Note : Weak leads might be dropping off in a consistent pattern that might require a different set of actions to be performed to help convert them into strong leads.

2. Churn Prevention : Retention is always more profitable Vs acquisition. Customer Lifetime Value is the cornerstone of consumer centric marketing. Therefore, reducing churn by determining the factors that lead to churn of specific segments of consumers and creating plans to proactively prevent churn is one of the most useful applications of predictive analytics. Likelihood to churn can be based on visible factors like complaints lodged, frequency of interaction with the product, time of year, weather, demographic shifts or by a series of complex factors and invisible factors like competitive pricing, in-market innovation in features etc.. Based on the reasons for churn we can create the right ‘nurture’ programs to address the segments. 

B. Strategic Applications

1. Identify High Value Segments : Identifying high value customer segments is important in two ways – helps you grow the profitability per customer metric improving your ROIs and it helps you understand success metrics that can be scaled across segments. Predictive analytics will help you understand the likely behaviors/ spend capacity/ spend propensity per product within the high value segments. It will also help you identify the tipping point for high value segments allowing for scaled conversions.
2. Up-sell / Cross-sell : Data of consumers who have recently traded-up and across offer insights of who/ when/ how they have done so. Consumers choosing a time of year (sale or festive season), usage of loyalty benefits, increased usage frequency etc will point to certain trigger points that lead to their change in behavior. This could come from a change in lifestyle, lifestage, season, income shifts etc.. Matching internal consumer data with external 3rd party data might be necessary to provide a clear picture of when/ how they have traded-up/ across. 

 
There are more than 4 applications for predictive analytics (Continuous Multi-Variate Content Testing, Post Trade Programmatic Audit etc) but the list will help start the right practices to help ensure early wins within the orgainsation.