Table of contents for IBM Business Analytics Summit 2011
- Charting an analytics course to better outcomes with IBM
- IBM Analytics customer stories
- IBM and delivering differentiated client value
- Predictive Analytics spotlight from IBM
- Social Media Analytics with IBM
- Smarter Customer Analytics and Decision Management with IBM
- IBM thoughts on Predictive Analytics Futures
A number of customers participated in a panel of analytics success stories. Each one was interesting and powerful in its own right and each prompted a Decision Management suggestion for someone at that stage in developing and deploying analytics.
Argos Risk is a web-based online subscription risk management service. Argos offers actionable risk insight for business to business relationships – allowing companies to see the financial health of the companies with whom they want to do business. Analytics is critical to the service which involves making presenting information gathered about a company from a financial perspective in an easy to use dashboard. Based on Cognos this SaaS offering is both used to directly support customers and to support “white labeled” versions for banks to offer to their customers. Interestingly they plan to move to delivering a predictive score using SPSS Modeler – offering something like a credit risk score but for B2B credit not B2C. They have a real focus on making the dashboard actionable – presenting suggestions on how to use the data displayed, what actions to take given the range of decisions being made by subscribers as well as generating alerts when significant changes happen.
Decision Management suggestion – move to Decisions as a Service by wrapping predictive score with the rules defined in the recommended actions. Let customers get decision outcomes not just information that helps them make decisions.
Cincinnati Zoo is a Zoo and Botanical Garden with 1.3M visitors annually. Cincinnati Zoo uses analytics as part of a strategic plan to deliver the highest service and maximum revenue. Their industry is not well known for using analytics so this is a real outlier. They have some great results having gone from being very unaware of their business and what their data could tell them to being a real leader. They got an ROI within one quarter with some rapid wins, for instance by driving a 30.7% increase in food and 5.9% in gifts as part of a focus on “in-zoo” spending. Their use of geospatial analytics showed that national programs were driving attendance from people who lived nearby, setting themselves up for significant savings in marketing. They have used analytics to engage better with sponsors and to understand the impact of weather on their business – allowing them to schedule resources based on predicted weather for instance. Success in this project was highly dependent on their executive sponsorship which in turn led to a need for executive-friendly interfaces to engage those same executives.
Decision Management suggestion – take the planned loyalty program to the next level by focusing on micro decisions for customer treatment, personalizing every marketing interaction
Next customer is a world leader in digital TV and entertainment services. Their focus is on providing the best video experience for customers in the US, both in home and out. They use analytics to see how they are doing (scorecards), identify root causes for business hot spots and prevent or encourage certain actions through information and insight. Critical issues were common definitions of business rules and metrics. They got increased productivity through central planning and found that only a real analytics solution could deliver insight from millions of subscribers and 50 attributes!
Decision Management suggestion – embed insight on subscribers in your call center and email marketing systems by building predictive models and wrapping them with rules to make for actionable customer treatment actions.
Merial is huge animal heatlh company producing pharmaceuticals for wildlife, livestock and pets. While a young company, Merial found silos of data quickly. They focused on analytics to get information about to their sales team, reducing the need for reps to call in with questions and increasing their trust of the data. Over 1,000 users wants “the Cognos numbers” delivered daily before 6:00am. Interestingly they report against the BI usage data to understand how people are using BI allowing them to improve their use of their analytic platform.
Decision Management suggestion – begin with the decision in mind. Understand explicitly what decisions sales reps are making and apply predictive analytics to improving that decision. You do need to understand first and predict second but you don’t have to understand EVERYTHING to predict SOME things.
OMERS is a large Canadian pension plan with $53B in assets and 400,000 members. OMERS invested in analytics as part of improving its management of financial information for consolidation, financial reporting etc. In particular they needed to be able to do analytics for various operating entities with different charts of accounts from a single source system. This increased flexibility had to be matched with governance so that, for instance, unmapped segments could be identified before generating statements. The increased flexibility and ability to drill down into details allow for more analysis rather than basic data management. They are also revising some of their processes now they see how the tools allow different people to participate in some of these processes.
Decision Management suggestion – think about the decisions that impact the business and see if the financial numbers could be used to improve those decisions.
XO Communications is national B2B telecommunications using a nationwide IP network. They adopted analytics because they need to resolve a churn problem. They had tried to address churn issues using “soft” analytics like Excel but needed a serious predictive analytics capability and went to SPSS. They focused the use of SPSS on improving targeting, controlling costs by focusing on the right customers, enhance revenue through better campaign yields etc. But reducing and managing churn is the most critical. They built a predictive model that generated a score that predicted how likely a customer is to churn in the next 90 days. They used this to divide customers into 10 deciles where the highest risk group was nearly 5x as likely to churn. They were able to reduce the churn rate in this group by 35% and improved billed revenue by 60%! Millions of dollars in annual benefit. Overall they have halved their churn rate since the project started. A staff of 5 is making the difference here, showing how “pervasive” analytics does not mean everyone doing analytics but everyone using it
Decision Management suggestion – look into embedding predictive analytics into operational systems to make analytics more consumable.