Business Analytics  Examples

When it comes to business analytics, success often depends on whether or not all parties of an organization fully support adoption and execution. Successful BA examples—and subsequent deployment of new predictive-based initiatives—include:

  • Predictive Maintenance: Shell

    Royal Dutch Shell PLC recently implemented predictive maintenance driven by artificial intelligence to cut down on time lost to machine failure. The AI-powered tools predict when maintenance is needed on compressors, valves, and other equipment, can autonomously analyze data to help steer drill bits through shale deposits, and will soon be able to identify and alert station employees of dangerous behavior by customers, reducing risks from the drilling platform to the gas pump.

    The systems can anticipate when and where more than 3,000 different oil drilling machine parts might fail, keep Shell informed about the location of parts at their worldwide facilities, and plan when to make purchases of machine parts. These systems also determine where to place inventory items and how long to keep parts before putting them into rotation or replacing/returning them. Shell has since reduced inventory analysis from over 48 hours to less than 45 minutes, saving millions of dollars each year thanks to reduced costs of moving and reallocating inventory.

  • Predictive Deliveries: Pitt Ohio

    Pitt Ohio, a $700 million freight company, was significantly impacted by Amazon’s same-day delivery initiative, which ramped up customer expectations. Customers also became more demanding, requesting up-to-the-minute tracking and estimated times of delivery that were much narrower than formerly acceptable windows. The company turned to data analysis to find a way to improve customer experiences.

    A cross-departmental project involving market research, sales operations, and IT was launched internally, leveraging data that was previously unused. The historical data, predictive analytics, and algorithms that calculated freight weight, driving distance, and several other factors in real-time allowed Pitt Ohio to estimate delivery times at a 99 percent accuracy rate. The company estimates that repeat orders increased its revenue by $50,000 per year, and customer churn reduction equaled retained revenues of $60,000 per year.

  • Predictive Banking: Axis Bank

    Axis Bank, the third-largest private sector bank in India, implemented robotics process automation and deep learning to identify customer behavioral patterns and recommend next best actions to prevent customer churn, including streamlining document processing, identifying “events” when customers were more likely to leave, and preemptively offering special promotions targeted to those segmented audiences to prevent churn.

    For better customer experience, 125 “customer journeys” were identified, analyzed, and retooled, and time spent verifying customer-provided data across multiple documents in the back office dropped from 15 minutes to 2–3 minutes. Axis is now developing a chatbot to speed customer interactions and reduce wait times for service at busy branches and during peak interface times.