The predictive power of big data analytics (part 1)

 In Academy, Analytics Landscape, Big Data 101, Featured, Popular, Predictive Analytics, Tech News, Trends

You may value big data for its ability to help you see the big picture up to the present moment. Indeed, this is an important and powerful aspect of big data analytics. For example, Jumpshot uses big data from clickstream activity going all the way back to the beginning of 2014 to illuminate domain-specific activity for any website or marketplace, and cross-site activity among any audience or behavioral segment.

However, big data analytics can do more than just illuminate the past. It can help predict the future. This is done through predictive analytics whereby historical big data sets are incorporated into predictive models to estimate future outcomes.

Here are a couple of examples of companies that are using big data analytics to predict different aspects of the future for businesses and the public.

4 companies using predictive analytics

1. New Relic predicts leads most likely to become customers

New Relic is an application performance monitoring company that sells to business customers. The company’s sales team relies on predictive lead scoring from Infer, which put in place two levels of scoring. First, a score model identifies who is the most likely to convert into an active trial lead. Second, a score model determines who from the trial group is the most likely to convert into a qualified lead. The sales team then uses this data to prioritize outreach and sales engagement.

And the business metrics behind predictive lead scoring are fantastic. They include nearly 10x the conversion performance for top leads, 30 percent higher deal sizes, and faster average time to close.

2. Amplero predicts customers at risk of churning

Amplero provides a predictive customer lifetime value platform that helps managed service and subscription service companies leverage big data and machine learning to optimize to long-term key performance indicators throughout the customer life cycle, such as renewals. The company helped a major mobile carrier predict non-renewals 15-21 days in advance, which was a 14-20 day improvement over the carrier’s previous prediction models. With more time the carrier was able to re-engage customers that might leave. As a result, the carrier was able to keep 10 percent of predicted non-renewals instead of just 2 percent.

3. PASSUR Aerospace predicts airline arrival times

PASSUR Aerospace is an aviation intelligence company that’s using big data to increase the predictability of air travel gate to gate. The company has been collecting petabytes of airport, airspace, and flight data daily for 10 years and uses this data to supply airlines with an accurate flight arrival prediction algorithm. The algorithm increases the accuracy of predicted arrival times so that airlines can land more planes on time and potentially save millions of dollars each year. Its success shows in its market share: 53 percent of all US domestic commercial flights are managed with PASSUR.

4. Enigma predicts locations lacking smoke alarms

Enigma brings public data into decision-making workflows by ingesting it, enhancing it, and extending it into outcome-driven applications. The company’s Smoke Signals project serves as a powerful example of the predictive value of big data. The project combines data from the American Community Survey, which includes a question about whether residents have a working smoke alarm, with other predictive data sources to generate a risk score for each census block group. It then publishes these risk scores to a map that any person or community can view and consider when developing outreach strategies that could get more working fire alarms into more homes. Enigma estimates that its predictive model for predicting missing fire alarms represents a 15 fold improvement over the alternative.

Bottom Line: Companies that have access to big data can use it to make accurate predictions. You’ve seen how big data can be used to predict leads most likely to become customers, customers at risk of churning, arrival times for flights, and locations lacking smoke alarms. Now that you know the possibilities, you can consider how to best leverage big data to make predictions for your business.

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