Case Study: Bringing Data Science to your Customer Acquisition Strategy

Scarlett Swerdlow, Applied Data Science Manager
Civis Analytics
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Key Takeaways

What? Lyric Opera of Chicago wanted to use data to intelligently target new ticket buyers. 

So what? Civis Analytics helped Lyric Opera understand who its most likely ticket buyers were based on segmentation.

Now what? Look-alike modeling strategies can help identify your best customers so that you can increase the reach and precision of your targets.

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Brought to you by: Civis Analytics

As a marketer, how do you stretch your dollars in an effort to maximize ROI? When discussing optimization for efficiency, it’s often defined by how you acquire the most customers for the least cost. No secret here—data can make a big difference in your ongoing optimization strategy. It’s something that’s been refined in the digital landscape but been slower to adopt when it comes to other channels. But why?

There’s an overwhelming amount of data around the online consumer journey, from how a user spends their time to when and where they click, to how many touch points it takes for a conversion, making it easier than ever to use data to target and reach your audience. Alternatively, the consistent story around offline data is that it's simply not accessible to the same extent and remains significantly more difficult to analyze and act on. In reality, this data exists in ample quantities, but the collection and analysis change dramatically with the medium. 

In the following case study, we’ll briefly explore how leveraging data correctly can translate analytical learnings into offline success.

Applying data science to your offline marketing

To illustrate the impact data science can have on your offline customer acquisition strategy, we’ll show you in a project we did with Lyric Opera of Chicago. In case you’re already sensing a case of TL;DR, here’s a spoiler that should keep you intrigued: Using look-alike modeling, Lyric Opera successfully converted prospects into ticket-buyers at 3.7 times the rate of traditional sources. And it had the data to prove it. That’s a big deal.


Learn more from Civis Analytics to better understand how your costumers behave and how to find more like them.

But how did the opera do it?

Like many nonprofits in the arts, Lyric Opera of Chicago faces the familiar challenges of aging audiences, technological disruptions and changes in cultural consumption. In response, Lyric Opera, one of the leading opera companies in the United States, has intensified its online presence, creating a new state-of-the-art website and investing in content creation. It began to build loyalty among its core audience by scaling marketing and programming into a relevant digital experience for new and existing users. But the opera wanted to take it a step further by expanding its audience with the precision of data science. No more guesswork, no more offline attribution question marks. Just actionable information rooted in facts.

To start, we needed to understand the demographic and behavioral characteristics of Lyric Opera’s core audience. We gained those insights by matching Lyric Opera’s current ticket-buyers to our national database. Consistent with national trends, Lyric’s typical audience member is more likely to be a woman, more likely to be 50 or older and more likely to be from a high-income household than the average Chicagoan who has not attended the opera.

Some of the results, though, were surprising. The single most powerful predictor of being a ticket-buyer was the likelihood of voting in an election—more predictive than age, gender or income. Lyric Opera ticket-buyers were also much more likely to have made political and charitable contributions than their Chicagoland counterparts.

Lyric Opera wanted to take our analysis further and find its top marketing prospects. We used machine learning algorithms to take into account hundreds of dimensions at once and figured out which features beyond traditional segmentation buckets were most predictive of whether a person would purchase a ticket. Using those characteristics we identified the set of individuals who were most likely to buy a ticket.

Our methods take into account the important differences between arts donors and other nonprofit donors and even the differences between opera fanatics and museum-goers. For instance, while all arts attendees are significantly more likely to have a graduate degree than the average U.S. adult, opera-goers are the most likely of all arts patrons to have completed advanced studies.

Because Lyric Opera wanted to be completely data-driven in its marketing efforts, we supplemented the targets we identified for Lyric with prospects it could have sourced through a traditional market segmentation. In this case, we supplemented our targets with households from Chicago’s wealthiest neighborhoods. Lyric Opera mailed to both lists—the Civis targets and the high-income segment.

The targets we identified (through lookalike modeling, for those that know a bit about data science) converted to ticket-buyers at 3.7 times the rate of the other prospects. Through an individualized approach to marketing, Lyric Opera experienced an almost four-fold lift in ticket-buyers compared to more traditional tactics.

Lyric Opera is renowned in Chicago and around the world for its artistic programming and has been one of the nation’s leading opera companies for more than 50 years. If it was able to completely revamp its offline customer acquisition for something as timeless as the opera, you can too. It’s all in the data.

So what are you waiting for?  Learn more from Civis Analytics to better understand how your costumers behave and how to find more like them.

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Author Bio:
Scarlett Swerdlow, Applied Data Science Manager
<p>Scarlett leads a team of applied data scientists at Civis Analytics who develop data-driven, computational solutions to pressing problems in the public sector. She has helped organizations increase participation in anti-poverty programs, expand the use of college savings accounts, and connect middle-skill workers with training and jobs. Prior to Civis, Scarlett spent ten years in the nonprofit sector. She holds a Master’s in Public Policy from the University of Chicago Harris School and a B.A. from the University of California at Berkeley.</p>
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