Data analytics can work wonders. Highly data-driven organizations are three times more likely to report significant improvement in decision-making, according to PwC research.1 Yet, 62% of executives still rely more on experience and advice than data to make decisions.2
Why? We all like to believe in our own instincts, but while that may be natural, it's not sensible. Today's true leaders in marketing and data analytics are ignoring hunches and using advanced technology and machine learning to increase speed to insight—and to action. And those organizations that have committed to turning data into action are transforming their businesses.
Progressive, the 79-year-old insurance company known for its fictional spokesperson "Flo," is one company where data doesn't just talk, it drives action. "Data is really the bread and butter for us," said Pawan Divakarla, data and business analytics leader at Progressive. "It's not a person or a thing; it's virtual bits and bytes. But we have a reverence for data and when you think of it that way, you treat it with respect."
Harness the right data
Critically, that reverence for data must begin at the very top. At Progressive and elsewhere, executive support is an essential component for selling the value of data analytics throughout an organization. In a recent McKinsey Global Survey3, participants ranked senior-management involvement as the factor contributing the most to their effectiveness with data and analytics.
But welcoming a deluge of data and getting support from the top still isn't enough. If it's not the right information, it won't support big decisions. Companies must harness the right data to uncover insights. Only then can business decisions made previously on instinct be guided by data.
"Traditionally, we have relied on experts to gather these insights from data," said Sagnik Nandy, distinguished engineer at Google. "A data-driven organization wants this to happen automatically." And now it can.
To learn how top marketers like Progressive and Macy's overcame their biggest challenges with an insights-driven approach to decision-making, access the full white paper from MIT, "How Analytics and Machine Learning Help Organizations Reap Competitive Advantage."
Putting the focus on data can be daunting at a time when consumers have real-time expectations for companies, and brands need to be there in the moments people need to know, go, do, or buy. "It's challenging to be in control of your data universe because there's so much happening," Sagnik said. "There's application data, customer-survey information, attribution, advertising. There are millions of pieces of data floating around."
Mobile takes the lead
Mobile is just another source of data that can be integrated to provide a more holistic view of the customer journey. More Google searches are taking place on smartphones than on desktops and laptops—globally.4 And across the millions of websites using Google Analytics, more than half of all web traffic is now coming from smartphones and tablets.5
But many companies struggle with exactly how to manage and integrate mobile data. "People are still thinking of mobile as something different," Sagnik said. "But we're at the point where mobile is the status quo."
At Progressive, data insights helped improve customers' experience with its mobile app. "When we launched our mobile application, it was just quote-only," Pawan said. But the team recognized its mobile users wanted to do more than simply get information. "We said, 'It looks like, from the data, people are attempting to buy, and so we should put buy-related software up there,'" Pawan explained. "It was a really big 'aha' moment."
To lead successful data-driven initiatives like Progressive, analytics executives must overcome challenges in three areas: accumulation, analysis, and action. Put another way, analytics leaders need to be able to easily integrate more data sources, harness advanced technology for faster and more sophisticated analyses, and extract insights that lead to improved business performance.
By using analytics solutions, including Google Analytics 360 Suite, to integrate data sources and machine learning to analyze the data trail that people constantly create, organizations like Progressive can gather more (and more valuable) insights and improve the customer experience—often without human intervention.
"People have very fundamental needs when it comes to analyzing information, and machine learning can help them focus on what really matters," Sagnik said. "Rather than just reporting information and telling you what's wrong, machine learning technology can help you fix it." And if things are going well? "Machine learning can help you do more of what's working—and do it automatically."
Businesses like Progressive and Macy's that successfully value data over intuition tend to focus on three A's: accumulation, analysis, and action. For more on all three, access the full white paper from MIT, "How Analytics and Machine Learning Help Organizations Reap Competitive Advantage."
1 PwC's Global Data and Analytics Survey, "Big Decisions™," Global, base: 1,135 senior executives, May 2016.
2 PwC's Global Data and Analytics Survey, "Big Decisions™," Global, base: 2,106 senior executives, May 2016.
3 McKinsey Global Survey, Global, base: 519 executives representing the full range of regions, industries, and company sizes, Sept. 2015.
4 Google Internal data, Global, Oct. 2015.
5 Google Analytics Data, U.S., Q1 2016.