machine learning basics

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.
 

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