When Perez started the Orlando Magic's predictive analytics initiative in 2010, he miscalculated the time it would take to prepare the data. "All of us were thinking that it would be easier than it was," he says. Pulling data from Ticketmaster, concession vendors and other business partners into a data warehouse took much longer than anticipated. "We went almost the entire season without a fully functional data warehouse. The biggest thing we learned was that this really requires patience," he says.
"Everyone is embarrassed about the quality of their data," says Elder, but waiting until all of the data is cleaned up is also a mistake. Usually, he says, the data that really matters is in pretty good shape.
Iterate first, scale later
At Intuit, every project starts small and goes through cycles of improvement. "That's our process: iterative and driven by small scale before going big," says George Roumeliotis, data science team leader. The financial services company started using predictive analytics to optimize its marketing and upsell efforts, and now focuses on optimizing customers' experiences with its products.
Intuit developed predictive task algorithms to anticipate how users will categorize financial transactions in products such as Mint and QuickBooks. Based on the results of those algorithms, Intuit applications make suggestions as users enter new transactions. They also anticipate questions users might have and proactively provide content and advice that could help them.
"Start with a clearly articulated business outcome, formulate a hypothesis about how the process will contribute to that outcome, and then create an experiment," says Roumeliotis. Through A/B testing, analysts can gain the confidence of business leaders by creating parallel business processes and demonstrating a measurable improvement in outcomes.
Just be sure to start by choosing an existing business process that can be optimized with minimal risk to the business, he advises. Customer support, retention and user experience are great places to get started.
While predictive analytics projects can require a substantial investment up front, studies indicate that they can deliver positive returns on investment, as Cisco's experience shows. Ultimately, even small-scale projects can have an enormous impact on the bottom line. "Predictive analytics is about projecting forward and transforming the company," says Peri.
The risks are high, but so are the rewards, says Robinson. "Take it to the end," she says. "Be successful. And act on what you learn."
9 steps to success with predictive analytics
Follow these best practices to ensure a successful foray into predictive analytics.
1. Define the business proposition. What is the business problem you're trying to solve?
2. Recruit allies on the business side. Having the support of a key executive and a business stakeholder is crucial.
3. Start off with a quick win. Find a well-defined business problem where analytics can deliver measurable results.
4. Know the data you have. Do you have enough data -- with enough history and enough granularity -- to feed your model?
5. Get professional help. Creating predictive models is different from traditional descriptive analytics, and it's as much of an art as it is a science.
6. Be sure the decision-maker is prepared to act. An action plan alone isn't enough -- someone has to carry it out.
7. Don't get ahead of yourself. Stay within the scope of the defined project, even if success breeds pressure to expand the use of your current model.
8. Communicate the results in business language. Talk about things like revenue impact and fulfillment of business objectives.
9. Test, revise, repeat. Conduct A/B testing to demonstrate value. Present the results, gain support, then scale out.
Sources: Guy Peri, P&G; George Roumeliotis, Intuit; Dean Abbott, Abbott Analytics; Eric Siegel, Prediction Impact; Jon Elder, Elder Research; Anne Robinson, The Institute for Operations Research and the Management Sciences.
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