The limited release flavor unfortunately didn’t make it to this world. But the data collected about who bought it and who didn’t led to some lasting insights. That is, a group of researchers led by marketing professor Kellogg Eric Anderson discovered a segment of customers with highly unusual—and highly unpopular—tastes. If those customers buy your new product, the researchers found, it’s likely to fail.
This image is both useful and unexpected. “The failure rate of new products is incredibly high. It’s hard to know, will a product succeed or fail?’ says Anderson. Knowing that some markets, which usually signal success, actually signal the opposite could be useful for companies as they develop market research strategies or decide when to discontinue a product.
However, such ideas require you to think broadly about your entire business, rather than focusing on a narrow silo. They then require the collection and analysis of a lot of data. And today, most companies are simply not up to these tasks.
“One of the big challenges for companies today is that you have processes for almost everything. You have a process for … financial reporting, for managing a supply chain, for dealing with marketing. But if you go back and ask yourself, ‘Do we have an established process for AI and analytics in the company?’, the answer in most places is no,” Anderson says.
Learn more about artificial intelligence and analytics
Learn more about artificial intelligence and analytics from Professor Eric Anderson in the Kellogg Executive Education Leading with Advanced Analytics and Artificial Intelligence program.
Instead, many companies develop an ad hoc approach to using AI and analytics to solve isolated problems—which limits impact, making it unlikely that these tools will ever transform company culture or be used to drive its most critical decisions.
During a recent The Insightful Leader Live event, Anderson, who is also its director new MBAi programoffered advice for leaders who want to develop an analytics and AI process powerful enough to make a real difference in their business.
Develop Data Science Working Knowledge
The first step to success, he says, falls to leaders, who must develop a working knowledge of data science.
“That doesn’t mean you’re a data scientist,” he clarifies. While leaders don’t need to create their own algorithms, they do need to become sufficiently familiar with analytics and artificial intelligence to be able to gauge whether data is being collected and interpreted correctly and to understand the business problems these tools are addressing.
“You’d never be caught dead saying I don’t know anything about finance, but I have this very smart CFO who knows everything about finance,” Anderson says. The same should be true for data science. “You can’t make the right investment decisions until you know a little more about the science.”
Data-savvy leaders must also know enough to lead their data science teams and draw the information they need from those teams to make smart decisions.
“Do you have the resources to succeed here? Tell me more about how this will impact my organization. Tell me how this AI and analytics you’re proposing is deeply connected to my strategic priorities and is going to deliver on those priorities,” Anderson says. “You have to have the confidence to ask those probing questions.”
Support communication between entrepreneurs and data scientists
Along those lines, Anderson explains that it’s important for business leaders and data scientists to learn to talk to each other.
Data scientists are often trained using clean, simplified data or work on proof-of-concept projects. But real-world projects are much more complex, involving many people, processes, and of course meetings and discussions. “So data scientists have to get a lot better at communicating with non-technical experts to overcome some of these barriers,” Anderson says.
For their part, business leaders need to make sure these conversations with their data scientists take place using a common language and framework so everyone is clear about goals and expectations.
Don’t mix up what works
“In almost every large company we work with, there are pockets” of success in analytics, Anderson says. “Don’t mess with what works.”
Instead, he suggests building on that success and allowing that team to expand to solve specific problems in other parts of the organization. Then expand the team with those specific problems in mind. If you’re interested in, say, predicting how a product will do based on who buys it, you’ll want to bring in a computer scientist who specializes in predictive analytics. If you’re interested in influencing customer behavior, on the other hand, you can bring in a social scientist trained in conducting A/B testing.
“If you start with problems, you can identify what your needs are and hire against them,” Anderson says.
Even smaller companies can follow a similar playbook. Thanks to the proliferation of online AI and analytics training, updating existing workers in these skills is a real possibility.
“If you want to acquire skills, it’s not impossible to do,” he says.
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