5 habits of highly effective marketing analytics experts
By Michael J. McDermott
The winners of the 2016 ANA Genius Awards are a disparate lot, but a closer examination of the winning entries from The Clorox Company, Hilton Worldwide, Turner Broadcasting, and Syngenta reveals they have a lot in common when it comes to how they approach marketing analytics. Among the teams responsible for these companies' marketing analytics success, there is a shared passion for the work they do and a common commitment to making analytics an effective driver of business outcomes.
As might be expected with such a diverse group, there are differences among the approaches each winner takes with its marketing analytics strategy. Some of those differences are reflected in the various categories for which they were honored: Clorox for analytics adoption, Hilton for analytics impact, Turner for analytics science, and Syngenta for analytics innovation. However, a thorough parsing of the entries submitted by the Genius Awards winners and conversations with team leaders reveal certain traits, characteristics, and processes they have in common — what you might call the habits of highly effective marketing analytics experts.Here are five that stand out.
1. Defining the problem. Ashish Joshi, senior director of global data, analytics, and data science at The Clorox Company, says this is the most important thing for him when faced with a marketing analytics challenge. "Often, your business partners will approach you with a definition of their need, but when you peel the onion down a couple of layers, you may find that what they think they need is not necessarily what they actually need," he says. Joshi makes it a point to start asking questions from the get-go to make sure everyone is on the same page, "and that we are really solving a problem that needs to be solved."
2. Establishing a "North Star" for every project. In today's marketing analytics world, a project is rarely a discrete event with a well-defined start and end. "For the most part, these are massive journeys," says Dan Aversano, SVP of ad innovation and programmatic solutions for Turner Ad Sales. "That being the case, it's really important that we have a well-defined and clearly laid-out end objective or goal." Aversano and his team are developing Turner's next generation of ad capabilities around the North Star of greatly improved audience targeting and ROI guarantees, a goal they set almost three years ago. The ultimate goal is to more closely align advertising with each client's KPIs and optimizing for outcomes.
Making such a massive undertaking manageable is a major challenge, and the only way to meet it is by "chunking up" the process, Aversano says. Over the past three years, Turner has rolled out multiple solutions, each building on what came before, such as: CAE (Competitive Audience Estimation), which utilizes a best-in-class predictive model to build true audience estimates to fuel its targeting solutions; TargetingNOW and AudienceNOW, which provide the ability to estimate and optimize against impressions, reach, and/or frequency; and roiNOW, which enables Turner to better understand the causal impact of promotions and integrations on a client's business.
Each success along the way helps demonstrate efficacy and build organization-wide support for the overall project. "Essentially, we are building individual components of that North Star, knowing that it might take us another five or 10 years to fully get to where we want to be," Aversano explains. "We've been able to chunk it up in a way where we're slowly building toward the macro solution that touches everything."
3. Securing necessary buy-in. Marketing analytics doesn't exist in a vacuum, so getting buy-in across the organization is key to moving most projects forward. In the case of large-scale projects, starting with smaller proof-of-concept solutions can be a powerful tool for securing needed buy-in, Joshi says. "At the end of the day, we are trying to build capability and put a long-term solution in place, but it all starts by solving someone's business problem," he notes. A proof-of-concept solution must demonstrate the ability to solve a specific business problem, but the process used to create that solution must be scalable and repeatable if it's going to secure broad acceptance across the organization.
For Joshi's team it's all about proving the short- and long-term value of investment in marketing analytics and guiding Clorox to the right level of digital spend by integrating the analytics program seamlessly into the company's existing processes. This requires the analytics program be visible not only to marketing but to the company's GMs and sales and finance leaders as well. To achieve that level of visibility, Joshi upended the model of quarterly meetings with key decision makers and generally vague agendas, replacing it with a constant stream of communication between analytics and Clorox's CMO, VPs, and GMs. The new model puts decision makers from the analytics team in constant contact with other decision makers — especially GMs — and allows the former to understand the problems of the latter and develop solutions that can be integrated into existing processes to help solve those problems.
The results for Clorox have been impressive: Advertising and sales promotion spend levels, which had been declining, are up; ROI on total advertising spends are growing; and Clorox's ability to measure ROI on digital ad spend has effectively doubled.
4. Maintaining perspective. There's nothing like striving for perfection, but in the fast-paced world of marketing analytics, it's not always a good idea, admits Rouben Karakachian, director of CRM, contact strategy, and optimization at Hilton Worldwide. He advocates creating solutions that do most of what you want and can be deployed quickly.
The marketing optimization project that earned Hilton a 2016 Genius Award in the analytics impact category was a resounding success. It resulted in an analytically driven customer contact strategy; delivered scalable and sustainable processes that allow Hilton's direct marketing channels to deploy about two million personalized customer communications a month and generate $60 million in incremental revenue annually; and increased consumer engagement while reducing customer fatigue.
But making that happen required the analytics team to build more than 150 predictive models, along with sustainable and scalable platforms for the monthly and daily execution and deployment of five optimization processes. "Scalability, repeatability, and practicality are becoming more and more critical in the analytics world," Karakachian says.
Those are the characteristics that enable analytics to provide an immediate return. "An offshoot of that is the need for a balance between complexity or sophistication and practicality," Karakachian adds. "An analytics solution has to deliver solid results, but it doesn't have to be perfect. The reality is, virtually every solution will have to be changed or enhanced within a few years, so obsessing over the development of a 'perfect' solution today can be counterproductive."
Scalability, repeatability, and practicality are becoming more and more critical in the analytics world."
— Rouben Karakachian, director of CRM, contact strategy, and optimization at Hilton Worldwide
5. Acknowledging the importance of people. Syngenta, the winner in the analytics innovation category, created a competitive edge for its brand by showing farmers how they could boost yields of soybeans, the top-dollar value crop in the U.S., through data-driven seed selection. Creating the powerful predictive and interactive models needed to implement the idea required an intimate knowledge of both plant biology and cutting-edge analytics, which is "not a combination that exists in the wild," according to Joe Byrum, senior R&D and strategic marketing executive in Life Sciences, global product development, innovation, and delivery, at Syngenta.
Since its ability to compete with employers such as Google and the NSA for the kind of talent it needed was "highly limited," Syngenta decided its best bet was to adopt a creative approach. It augmented in-house resources by partnering with experts in fields unrelated to biology and agriculture and used open innovation platforms to connect with talent all over the world. This novel approach to the talent problem in the data analytics field worked, and Byrum notes that not only has the analytics methodology it developed been good for the company's bottom line, it may also help address a looming global food shortage problem by helping Syngenta's customers increase crop yields.
Syngenta's situation was unusual, but even organizations with a deep roster of marketing analytics talent are well-advised to pay close attention to the people aspect of their organizations, Joshi says. "It's very important for us to ensure that [team members] develop and learn from the experiences the leaders in the organization have had driving analytics," he adds. "Coaching and mentoring becomes extremely important because there is a lot of learning-by-doing in the analytics field."
Photo credit: Peshkova/Shutterstock.com
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