Predicting the Future, Using Real-Time Big Data Analytics
In our previous blogpost, we looked at 4 ways that real-time, big data analytics can give your business a competitive advantage. One of them was that you could use real-time big data analytics to make dynamic changes based on user behaviour. This brings us to the realm of Predictive Analytics, which, as Eric Siegel says, gives you “the power to predict who will click, buy, lie or die”. Let’s dig deeper. Think about it – one of the earliest instances of putting Predictive Analytics into use, was (like most of the other modern marvels) during the World War 2, by Norbert Wiener. While he tried to predict the path of warplanes, the closest he could get was to tell where it would be in 1 second from now. You’d need at least 20 seconds to load the artillery and bring the plane down, so a 1-second head-start was nowhere near adequate. As fate would have it – it didn’t end up creating much of an impact, but surely laid the foundation to some of the successful use cases we see now, thanks to the emergence of big data and cloud computing capabilities. If you’re still wondering if predictive analytics is just another fad, ask the Banks that successfully predict which of their credit card users are likely to default. Or ask the insurance companies who can predict the probability of a claim, better, by correlating lifestyles and diseases - and therefore price their policies better. Of course, there have been a few infamous ones as well. “How Target figured out a teen girl was pregnant, before her father did” is, surely, a case in point! Given these hits and misses, it becomes all the more important that a business picks up the right problem to solve and employ the right infrastructure and resources that help them get there, rather than taking up a me-too project that sounds like the next best thing since sliced bread. As Gartner puts it, the most valuable Predictive Analysis solutions are those that are faster, more relevant to businesses and easier to use; while being increasingly focused on the prediction aspect rather than description or classification. Here are a few commonly seen applications of Predictive Analytics:
- 1. Sentiment Analysis – Analyse tonnes of unstructured data coming in from the notes of your call centre representatives or those lovely social media posts to see how people relate to your brand. You can then use this data to plan campaigns that reinforce or change the sentiment.
- 2. Churn Analysis – Acquiring a new customer is always going to be more expensive than retaining existing ones. Find out which of your customers are likely to leave, and stop them from leaving by creating targeted offers and loyalty management campaigns.
- 3. Healthcare & Diagnostics – From analysing health records, finding patterns, diagnosing ailments and predicting probable complications in the future to prescribing preventive medicines to combat those; Predictive Analytics does it all.
- 4. Sales Opportunity Analysis / Lead Scoring Models – Understand which of the leads are likely to convert and focus your attention on them while also learning when to intervene to get the maximum impact. Add some AI and Machine Learning on top of it – it’ll even tell you what kind of leads you’ll need to look for, where you can find them and how you can engage with them.
- 5. Anomaly Detection – Build dashboards that monitor the most important metrics and tell you if there’s a dip or a spike as soon as it occurs, or even pre-facto in some cases. It’ll also tell you what led to the anomaly and help you prevent such occurrences in future.
- 6. Risk Management & Underwriting – Predict the chances of fraud, illness, bankruptcy or accidents and prevent, or be prepared for unforeseen events that are likely to leave a lasting impact.
- 7. Pricing solutions – Build elaborate models on how your costs and inputs vary and forecast the best ways to alter pricing dynamically on a real time basis.
- 8. Predictive Marketing Analytics - Helps predict the right customer cohort to go after, thanks to better abilities to find patterns in user behaviour, break them down into clusters and segment, refine targeting mechanisms, make the right pitch and always stay relevant.