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Using Predictive Analytics to Improve Decision Making and Business Outcomes: Part 2

April 11, 2013

In my previous blog, I explained how leading organizations are looking to improve business outcomes by predicting with confidence what will happen next. The challenge comes from the fact that there is so much data – in structured and unstructured formats –  whizzing through the ether.

The good news is that big data with predictive analytics, coupled with big data technologies, can help your organization:

  • Uncover hidden patterns and associations
  • Enhance customer retention
  • Improve cross-selling opportunities through personalized offers and experiences
  • Maximize productivity and profitability by aligning people, processes and assets
  • Reduce risk to minimize exposure and loss

In this blog, I want to share how organizations in automotive, chemical and petroleum, telecommunications, and travel and transportation are using predictive analytics and big data to improve business outcomes. (To learn how predictive analytics is helping energy and utilities, government, and healthcare, see my previous post).


In the automotive industry, both advanced condition monitoring and warranty claims can benefit from predictive analytics. Through dealership and telematic information, customer sentiment expressed through social media, and customer driving patterns, manufacturers can better predict maintenance requirements for specific drivers and forecast the success of aftermarket parts and services.

Manufacturers can better predict component failures and anticipate maintenance opportunities by analyzing warranty claims and defect trends and patterns.

Predictive analytics can help the automotive industry:

  • Develop new maintenance and aftermarket offerings
  • Accelerate vehicle launches
  • Reduce warranty cost and recalls
  • Improve future product quality
  • Lower fraudulent warrant claims
  • Identify the source of defects in the supply chain

Chemical and Petroleum

In the chemical and petroleum industries, firms are using predictive analytics of sensor, geological, diagnostic, environmental and seismic date to help automate drilling and production operations. They rely on analytics to help them monitor and manage assets to reduce the frequency of repairs and the chances of catastrophic failure by improving the speed and accuracy of problem diagnosis.

For example, to help improve productivity at its Arctic facilities, ConocoPhillips wanted to monitor and forecast ice floe movement at its Arctic facilities in or near real time. Big data technologies helped the company collect and manage thousands of data points per second from multiple sources. Through predictive analytics, ConocoPhillips can visualize in real time the position of ice floe near its facilities. The result is increased productivity and yield as the company has been able to extend the drilling season by weeks.

Predictive analytics can help the chemical and petroleum industries:

  • Optimize well production yield
  • Lower production cost
  • Mitigate risk
  • Operate in environmentally sensitive areas
  • Improve health and safety conditions
  • Ensure regulatory compliance

Learn more about the impact that big data is having on the chemical and petroleum industries.


Communications service providers (CSPs) can leverage predictive analytics to improve customer retention by combining customer profile, interaction and usage across all channels (mobile, call center, web, store and landline) to better understand customer needs and predict location-specific offers that consumers will find appealing. By analyzing streaming data, CSPs can identify and act on monetization opportunities in real-time.

For example, Ufone, an Asian CSP with 20 million subscribers, needed to increase revenue and profitability by reducing churn, reactivating churned subscribers and increasing revenue from value-added services. Real-time analytics for campaign execution and management can help Ufone identify subscriber behavior in real time and increase the number of monthly campaigns by 300 percent. Promotions can be run interactively to provide incentives to subscribers, which in turn can drive sales and revenue while reducing churn.

Predictive analytics can help CSPs:

  • Reduce customer churn
  • Monetize consolidated anonymous subscriber data by segment
  • Improve offer acceptance for advertising partners
  • Increase customer satisfaction with targeted location-based offers for opt-in subscribers

Learn more about the impact that big data is having on the telecommunications industry.

Travel and Transportation

In travel and transportation, predictive analytics is used to identify when infrastructure and assets are likely to fail or need service, and when to perform preventative maintenance. Data gathered from sensors, call center logs, maintenance and engineering specifications is analyzed to detect patterns and indicators of failure.

Predictive analytics can help the travel and transportation industries:

  • Reduce equipment downtime
  • Maximize asset value
  • Lower capital and operational expenditures
  • Increase service delivery and quality with less risk

Forrester: Big Data Predictive Analytics Solutions

Forrester, a leading industry analyst, recognize the impact that big data and predictive analytics are having in business. In its Q1 2013 report, Big Data Predictive Analytics Solutions, Forrester state: “Big data is the fuel and predictive analytics is the engine that firms need to discover, deploy and profit from the knowledge they gain.”

With the rise of big data, predictive analytics market plays an even greater role in decision-making. See how Forrester ranks the top 10 vendors in this space.

Learn how IBM can help you improve decision making through predictive analytics. Download our white paper: IBM InfoSphere Streams: Redefining Real Time Analytics.