The unexpected benefits of data analytics
Successful IT projects require clear goals, and data analytics is no exception. When conducting analysis, data teams seek to discover useful information about customers, to support decision-making on a project, to enhance productivity, and a host of other outcomes.
But given the exploratory nature of data analytics, sometimes benefits or insights gleaned are out of the blue, completely unexpected and not part of the original business plan. These happy accidents support the idea that all data analytics is worth the effort, because you never know what might turn up.
Here are real-world examples of organizations that have seen unexpected benefits from their analytics efforts.
Allegis Global Solutions developed its analytics program, ACUMEN Workforce Intelligence, with the goal of understanding three things: How are programs performing? How are they performing against other programs? What should we do next?
“While the analytics platform was created to develop a historical perspective that can be used for planning, we discovered that the analytics used to answer these three questions had a domino effect in our organization,” says Tim Johnson, executive director of business intelligence at Allegis.
“Because data is pulled from all of our programs and [we] update it on a daily basis, we have a very fresh view of operations,” Johnson says. “This resulted in the recent rollout of a new data application that helps analyze day-to-day activity, both at the program and enterprise levels.” Operators don’t need to pull a report and analyze information to gather program insights. Instead, they can just take action on information that they’ve already structured and presented.
“As an added benefit to using data operationally, analytics adoption rates are much higher than initially projected,” Johnson says. “Now we have set our sight on a goal of 100 percent internal adoption on a weekly basis. Our end users, because they are using the data on a day-to-day basis to improve performance, are also keeping a closer eye on the information, driving data quality to a new level of excellence.”
For the company’s initial analysis purposes, data only needed to be 90 percent to 95 percent accurate. But for operations it needs to be 98 percent to 99 percent accurate. “With the buy-in across the organization, we’re there,” Johnson says. “It’s funny to think those three little questions started this organization-wide data transformation.”
A data analytics effort helped online real estate resource provider Trulia fine-tune its email strategy for one of its campaigns, which has ultimately increased traffic.
The company had been sending multiple emails a day, which increased the number of customers who unsubscribed, says Deep Varma, vice president of engineering. “Then we shifted our approach and aggregated emails so that we were only sending one email per day,” he says.
At first, Trulia saw a decline in engagement from its users, so it pulled back on the new approach. But the company then decided to test the new format for a longer period and saw better results, which it didn’t initially expect, Varma says.
“In this example, initially, data proved something was going wrong because we didn’t broaden our scope in the testing,” Varma says. “Once we did, we experienced an unexpected benefit in the sense that we never thought the traffic would increase because the data in one week showed traffic declining. However, by running it longer, analytics proved the opposite was true.”
In another example, data analytics inspired a new product, which was not the original intent.
“We saw consumers dropping after submitting an inquiry to an agent, so we created a post-lead experience where we started showing them the recommended properties that are similar to the listing that they had just inquired about,” Varma says. “As a result, consumers began returning and continue to return more frequently due to this new recommender profile.
