ALEXANDRIA, Va. -- Like many emerging technologies, predictive analytics technology has a certain degree of coolness associated with it. When executives see that the technology can accurately predict which customers are likely to buy, they get excited.
But what good is the prediction if companies don’t do anything with the insight? Not much, according to Dr. Eric Siegel, president of consulting firm Prediction Impact and chairman of this year’s Predictive Analytics World conference.
In fact, said Siegel during his keynote address that kicked off the conference here, “I’m afraid predictive analytics is missing the boat.”
As predictive analytics starts to gain traction, the technology must tie insight to action to be truly effective, Siegel said. Companies must devise business rules that trigger specific action when a prediction is made.
Insurance companies, an early adopter of predictive analytics technology, are a good example of this, he said. Insurance companies use predictive analytics technology to determine the riskiness of taking on a particular customer. The potential risk of a customer is then tied directly to the price of insurance being offered.
At a retail organization, connecting predictive
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analytics to action could mean triggering marketing
campaigns based on a customer’s likeliness to purchase a certain item or service. At financial
services companies, the technology can be used to identify potential fraud and then prompt an
audit.
Whatever the industry, predictive analytics in isolation doesn’t do anybody much good. But that’s not all companies considering predictive analytics technology must keep in mind, according to other speakers at the conference.
Predictive analytics demands significant data prep work, user buy-in
There is significant prep work that must go into a successful predictive analytics initiative, said Paul Coleman, director of marketing statistics at retail giant Macy’s. He estimates that getting data prepped before even applying predictive analytics technology is about 80% of the job.
“Building [predictive data] models is at least as complex as your business,” Coleman told attendees. And “the models are only as good as the data.”
Jean Paul Isson agreed. Isson, vice president of global business intelligence and predictive analytics at Monster Worldwide, said data governance and data quality are key to successful predictive analytics.
At Monster, for example, company executives first had to decide on the definition of “customer,” Isson said. Initially, they came up with seven possible definitions. Not until they agreed could they move forward with predictive analytics, he said.
Isson also said change management is important when deploying predictive analytics technology. He said most predictive analytics initiatives fail not because of faulty predictive data models, but from a lack of executive buy-in and poor end-user training.
Marketing executives who are hitting their numbers, he said, will likely be reluctant to adopt a new technology like predictive analytics. It is important to show them how the technology will actually improve their success rates and then train them how best to use the associated tools.
Jason Fox, an information system and portfolio manager in Paychex’s enterprise risk management division, told attendees that finding subject matter experts was crucial to the company’s predictive analytics initiatives.
“We identified subject matter experts to ensure that business conditions were met,” Fox said. He also sought out champions of the technology in Paychex’s sales department staff, people who could tout the benefits of predictive analytics technology to their colleagues and boost end-user adoption.
Technical obstacles to predictive analytics
There are also technical factors to consider, Macy’s Cole said. Data contained in flat files, for example, is relatively simple to model but difficult then to change, he said. Data in relational databases, on the other hand, is more flexible to work with but can be limited by data volume constraints.
Companies should consider the type of data they plan to exploit and how it is stored before starting a predictive analytics initiative. This might also play a role in the type of workers a company hires to oversee predictive analytics.
In the end, however, all these efforts are worth it, the speakers said.
“Inside this data, there’s a customer in there someplace,” Cole said.