Rising Star: Leigha Hornstra, CMG

Leigha Hornstra is trying to see the future.

She wants to know what predicts human behavior, what events are coming and how to intervene. She doesn’t have a superpower or crystal ball. Instead, she has data.

The Rising Star is the business intelligence manager at CMG Atlanta. She majored in accounting with a minor in information management at the University of Tennessee. She started her career at a gas company but went back to school to study business analytics.

From there, she moved to Atlanta, hunting for a job with data as a predictive element. She was enticed by the data challenges CMG presented. She was originally hired as an analyst.

“You can’t look at a subscriber history and say, “Wow, they read three-fourths of the morning paper,” she says. “The ambiguity of the data was fascinating.

Leigha leads teams working on dynamic subscriber pricing and consumer targeting activities in support of CMG’s 500,000 print and digital subscribers.  She tries to predict actual behaviors and the propensity to renew. “We want to tailor our business to an individual instead of assuming a trend across our subscribers.”

Her team discovered in a lot of cases, they can use data gathered from the number of delivery complaints from subscribers to identify renewal prospects.

“The more times people complain, the higher chances of renewing. They’re more engaged with the paper.”

When the data tells a story, it provides her a creative outlet. Leigha is at the forefront of using predictive analytics and data-driven processes to grow consumer revenue and consumer engagement.

But you can’t know all the variables, she warns.

“There’s always that burning question of why a subscriber who has read for the last 30 years suddenly stops. Those are the groups of people that are the most challenging,” she says.

There is also the case of anonymous people visiting the website, and the looming question of what drew them in.

“If I could be omnipotent, that would be great,” she laughs.

Leigha designs predictive analytics to determine optimal pricing and predict the likelihood of stopping a subscription, including understanding digital engagement, content preferences and more. She would like to continue to grow in this career field to a point where she can push strategy and go after different targets using exploratory data.

Her advice to those new in data is to be patient — something she is still working on, she admits. “With data, the only way to predict the future is to have a good set of historical data. You might implement something and it could be six months to get anything back. I’m incredibly impatient.”

As she tries to look into the future, she predicts the continual push toward digital, with more personalization, quick recommendations and articles targeted at readers that they’re actually interested in.

“All things should lead to that click.”


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