Trying to Integrate Data Science and Creative Design? 4 Things Modern CMOs Should Consider

Intelligent and imaginative machine learning is key

The proliferation of digital channels today presents an additional challenge for CMOs.
Illustration: Beate Sonnenberg/Getty Images

Whirlpool won the Data Lions Grand Prix this past June for its creative use of insights (and washing machines) to keep kids in school. If Whirlpool is any indication, data activation through creativity heralds a tipping point that modern marketers should take note of if they are to take the lead.

David Steinberg
Headshot: Alex Fine

But, integrating data and design is not new. Budget cuts and decreasing demand during the Great Depression forced brands to improve the effectiveness of mass-market advertisements. Today, the opportunities to design for the individual are overwhelming and amplified by the accelerated access to machine learning. Consumer insights have never looked so plentiful, but the challenge remains how you find and act on them.

Ultimately, the winning formula for the modern CMO must always be backed by the power of intelligent imagination.

For the CMO, the proliferation of digital channels today presents an additional challenge: now more than ever, consumers expect moment-to-moment recognition of their interactions with your brand, including their likes and preferences. The CMO must integrate customer intelligence teams with imaginative creative that brings to life a moment, or concept, in a meaningful and seamless way for the consumer. Creative directors will continue to attempt to capture lightning in a bottle, but a successful holistic strategy depends on executing empathetic designs tailored to individual needs. Machine learning enables individual customer experiences at every moment of the day, keeping customers happy and converting.

Going to the heart of it, it appears that the true formula lies at the intersection of a unified view of each customer and a data science approach that identifies an actionable insight. With data increasingly commoditized, yet provided freely by consumers, machine learning is the new currency, leading organizations rich with AI.

So, what does a data-rich and creatively driven interaction look like when defined as intelligent and imaginative machine learning at scale? Let’s call this intelligent imagination. Here are four actions to consider on this journey:

Create clear and cross functionally aligned marketing objectives that drive a specific outcome

Cultivate imaginative problem solving in your intelligence teams, and data-driven designs in your creative teams, through setting objectives and goals that bridge both functions. The CMO of today should focus on pressing customer experiences that are specific to the functional team developing them, while tying each experience to a clear business objective.

Facilitate brainstorming between intelligence and imagination functions

The CMO must develop cross-functional processes that take consumer insight from intelligence functions and use this to collaborate among intelligence, strategic and creative teams. Traditionally, the intelligence function may recommend a hypothesis with a loss of interpretation when handing off between teams. This human-led process can be improved through live cross-functional exchanges that refine ideas through an evaluation of creativity, discovery and feasibility while solutions are in their infancy.
Collaboration between intelligence and imagination functions also encourages each function to design solutions creatively and holistically through repeated exposure. For example, Spotify has done this well by tapping into listener data and humanizing tech through its hyperlocal out-of-home billboards and advertising campaigns.

Observe people, behavior and content while integrating marketing infrastructure to deliver 1:1 at scale

Supporting technology infrastructure must enable fast information transference from consumer interactions with your brand—in any channel—so that it can be integrated, layered and acted upon in real time. But this alone is not enough. The CMO must recognize that data processed today is a far more powerful predictor of behavior tomorrow. A live triangulation between changing people, behavior and content interactions provides richer contextual depth compared with data models that assume tomorrow will replicate behavior that is months or years old. Such depth allows for more accurate assumptions about likes and interests.

Delivery company Postmates does a nice job with using real-time data to optimize campaigns that inform the present and the future. By utilizing a steady stream of data into its tech stacks, the company conceptually orientates all data points around customers and their touch points.

Your moment-to-moment outcomes must be measurable and used to inform future experiences

Your data platform should enable you to understand the constellation of points that match human behavior and experience across their lifecycle. All of these outputs should be measured, but it shouldn’t stop there. Your system should continually create and test new hypotheses because behavior, content and inventory are all transient and your algorithms must automatically digest this change. The pattern of taking data offline, testing it and trying to replicate it in production leads to old insights that will not lead to human results.

Ultimately, the winning formula for the modern CMO must always be backed by the power of intelligent imagination. Machine learning is at the seat of intelligent imagination, and a sophisticated yet often subtle understanding of needs will yield long-term gains.

David Steinberg is co-founder and CEO of Zeta Global, a CRM data and marketing tech firm valued at over $1.3 billion.

This story first appeared in the Sept. 4, 2017, issue of Adweek magazine. Click here to subscribe.