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Traditionally, organizations are tempted to start by gathering all available data before beginning their analysis. Too often, this leads to an all-encompassing focus on data management — collecting, cleansing and converting data — that leaves little time, energy or resources to understand its potential uses. Actions they do take, if any, might not be the most valuable ones. Instead, organizations should implement analytics by first defining the insights and questions needed to meet the big business objective and then identify those pieces of data needed for answers. (see Figure 6.)
By defining the desired insights first, organizations can target specific subject areas, and use readily available data in the initial analytic models. The insights delivered through these initial models will illuminate gaps in the data infrastructure and business processes. Time that would have been spent cleaning up all data can be redirected toward targeted data needs and specific process improvements identified by the insights, enabling iterations with increasing levels of value.
Companies that make data their overriding priority often lose momentum long before the first insight is delivered. By narrowing the scope of these tasks to the specific subject areas needed to answer key questions, value can be realized more quickly, while the insights are still relevant.
Organizations that start with the data or process change first often end up with unintended consequences — such as data that is not extensible or processes that are ultimately eliminated — that require rework and additional resources to solve.
Speeding insights into business operations Compared with other respondents, Transformed organizations are good at data capture. Additionally, Transformed organizations are much more adept at data management. In these areas, they outpaced Aspirational organizations up to 10-fold in their ability to execute.
(see Figure 7.)
Enterprise processes have many points where analytic insights can boost business value. The operational challenge is to understand where to apply those insights in a particular industry and organization. When a bank customer stops automatic payroll deposits or remittance transfers, for example, who in the organization should be alerted and tasked with finding out whether the customer is changing jobs or planning to switch banks? Where customer satisfaction is low, what insights are needed and how should they be delivered to prevent defections?
To keep the three gears moving together — data, insights and timely actions — the overriding business purpose must always be in view. That way, as models, processes and data are tested, priorities for the next investigation become clear. Data and models get accepted, rejected or improved based on business need. New analytic insights — descriptive, predictive and prescriptive — are embedded into increasing numbers of applications and processes, and a virtuous cycle of feedback and improvement takes hold.
IBM CASE STUDY: Shifting Gears From Vehicle-centric to Customer-centric Marketing
As turbulence struck the auto industry, a small group of executives at one automotive company decided to focus its attention on orphaned owners — customers whose current car brands were being discontinued. They determined to use analytics to try to salvage these customers, who were at risk for significant attrition.
A marketing approach focused more on the life cycle of the vehicle — service reminders, warranty notices and upgrade reminders — meant that the company knew very little about what could impact these customers’ future buying decisions. In a tough market environment and constrained by competing priorities, the company quickly fielded a new analytics approach. Instead of organizing and sifting through the terabytes of data across the organization, it quickly identified a relatively small number of key data needs, created a customer sample, then used analytic algorithms to forecast attrition probabilities, pinpoint at-risk customers and recommend precise retention strategies. Analysts uncovered a double-digit retention opportunity within a single brand worth hundreds of millions of dollars.
This prototype, initiated to uncover a specific customer insight, set off an analytics revolution. Brand managers across the organization quickly signed on to an enterprise effort to leverage analytics to shift from vehicle-based life cycle marketing to a customer-centric approach, targeted at improving both loyalty and retention.
This is part 4 of 10 from the 2010 New Intelligent Enterprise Global Executive Study and Research Project.
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