The real utility of big data comes to life when we are using it to analyse hidden patterns, analyse correlation and predict the possibility of future occurrences. This has direct implications to how marketing can use big data as a powerful tool to enhance the consumer experience, drive efficiency in spends and improve their effectiveness.
 
The promise of Machine Learning is to help duplicate the human experience of learning by extrapolating from past behaviors to predict future behaviors. The difference will be the scale/ speed / accuracy and consistency – all of which will improve exponentially. Machine learning, because it works towards determining patterns (Vs pre-set algorithms that are set to solve a specific problem logically) is especially useful in ‘real-time’ scenarios (the better way to talk ‘real-time’ is actually ‘right time’ given that business cycles vary depending on category) where the time available to discover the pattern and attempt predictions is extremely limited. It works best when you want to understand what happened in the last week and what is likely to occur in the next week.
 
Before talking any further, I’d like to step back and underline the key imperative of big data – actionable insights. Insights that are actionable within the business cycle of the predictions. Insights that are deterministic, help generate clear conclusions or hypotheses and will be accepted by the broader organisation as action-worthy.
 
Here are some pointers for marketers towards helping big data predictions :

1. Start small : Building a robust big data model and making it stable (wrt data feeds/ predictions) is part art, part science. It will take a few experts within the organisation (Data Science/ Analytics/ Category Experts/ Sales) to provide insights to help stabilise the model at accuracy. Learning is messy and the initial parts of gaining momentum can be confusing. You don’t want large teams to be consumed by the learning curve.
2. Point North : Are the predictions aimed at helping achieve the top business goals ? Identify the prediction results to the goals, have a clear template for analysis (combining the prediction with human expertise to creating solutions), the process (frequency, time to market, responsibility, budgets).
3. Right Time Vs Real Time : Every business cycle varies and has a different rhythm of going to market. Organisations have unique challenges in decision-making, approvals processes and executing investments/ making changes.
4. Right Data Vs Big Data : In finding the signal you need to help eliminate “noise”.  Big data does not mean all data. This is where the internal expertise within the company can truly add value in helping teams make decisions to ignore certain datasets for specific predictions.

 
Predictive analytics for marketing decisions is an emerging art, enabled by science. While its engine is driven by the past the true potential lies in taking the leap from predictions to imagine new possibilities.
 

Assuming you have worked the right target and the right context how do you deliver the right message that’s exactly relevant to the consumer and that has a higher certainity of converting ?
 
Given that achieving relevance in a mass precision setting is a complex task the following steps can help :
 
1. Segment Understanding : The inputs into creating programmatic creative start with an understanding of the consumer segments that will be targetted and their specific behavior/ desired behavior changes. Understanding their consumer journeys and the inherent opportunities for behavior change within each episode of the journey becomes the opportunity statement that creatives are trying to solve for. The different journeys and triggers per segment creates the first level of personalisation.
2. Context of the Segment / Targetting : Context creates the second level of personalisation. Context is information regarding the platform, time, device, location, weather and editorial environement in which the creative will interact with consumers. Especially, understanding the unique platform specific interactions and preferences of users is key in improving relevance.
3. UX : The third and sometime the most important level of personalisation is with the UX design of the creative and its possible iterations based on A/B Testing and optimisation. This includes technical considerations like image compression, kight-weight animation and reducing custom fonts. Reduction in load time is a parallel goal for optimal UX.
 
A few examples to showcase the simplicity of the creative solutions within programmatic :
 
1. The Economist – Personalised Display : As The Economist looked to add new subscribers it wanted to target people who wanted more value / analysis when reading the news.  First it chose content that was most popular with its current user base and understood the context of the article related to the user profile. Second it targetted news websites based whose context was similar to the ones preferred by its loyal consumers. Third, to create relevance it created witty and humorous content and placed it next to the relevant news articles (always linking its page context to user profile). The campaign saw The Economist increase prospects by 650, 000 and an ROI of 10:1.
 
2. O2 Refresh – Personalised Video: The goal was to increase conversion for 02’s latest refresh campaign amongst the 3 segments of early adopters, out of contract and users with upcoming contract expiration. It used data (device, location) in order to effectively target its segments with personally relevant messages (recycle value for their current device + upgrade options + popular preferences of users like them + store locators). O2 recycled its TV ads to digital to benefit from precision targetting and developed programmatic creative to help them achieve significantly higher results (CTR increased by 128%).

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.
 
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.

1. 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.

 
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 (Content Mapping, Programmatic etc) but the list will help start the right practices to help ensure early wins within the orgainsation.