Magic Numbers: Jason Carmel on Why Data Should Be Part of the Great Idea Itself

Jason leads the creative data practice globally for Wunderman Thompson, inspiring creativity and amazing expertise in data and technology. Jason’s team put a camera on a camel named Sarha to demonstrate Saudi Telecom’s wide and deep network coverage, even in the most remote parts of the desert.  They utilized a 20 year old CDC paper written about smallpox spread into code and updated it with COVID data to helping health and defense ministries predict and visualize the spread of disease. 

Wunderman Thompson, with Jason at the helm, created an algorithm for a paint client that translated a person’s description of a color (imagine “Rose in the Sunshine”) into RGB values, aiming to create one of the largest language-based color libraries in the world, which is shortlisted for a Cannes Innovation Lion.  Over his 20+ year career, has helped large brands, including Volkswagen, Microsoft and Coca-Cola, evolve and maximize their digital experiences based on real-time feedback and behavioral data. His favorite project was for WeCounterHate, where the Creative Data Group used Natural Language Processing to identify and score toxic speech on Twitter in a way that persuaded 20% of the perpetrators to remove their own hateful content from the platform.

LBB> What’s the number one question that clients are coming to you with when it comes to how they can better use data to enhance the creativity of their content and experiences?

Jason> Interestingly enough, no one is asking me questions on how to use data to enhance creative. Our clients just want great creative in the spaces where their customers are, full stop. The more we can do that by positioning data as the backbone, even if we never mention the 'D' word to the client at all, the bigger our competitive advantage will be and the more creative we can get.

LBB> How can you make sure that data is elevating creative rather than forming a wind tunnel effect and knocking all the interesting or unique edges off that make something distinctive?

Jason> I hate using data to say 'no' to things. That becomes dangerously reductive after a while and it’s the laziest thing you can use data to do. The best use of exploratory data is to find edge cases and prove to someone why the reward of doing something weird will be worth the effort and risk.

LBB> Can you share with us any examples of projects you’ve worked on where the data really helped boost the creative output in a really exciting way?

Jason> We built an app for Sherwin-Williams that allows architects to use natural language to describe a color they want, and the machine will present options to them. A lot of that tech uses AI and ML built by our data scientists, which allows us to be a lot more fluid and accurate with our guessing than we would have been otherwise. Obviously, that elevates the output for the end user quite a bit. But more importantly, as architects use this app, we are collecting data on how this essential audience describes color so that we can build an ever-evolving dictionary for that language that our client can own. When I say “the colour of a Maui sunset,” are you thinking of an orange or a pink or a lavender? Thanks to the data strategy built into this app, Sherwin-Williams will now know the answer to that. The way that elevates the creative output of this app for the client is immeasurable.

LBB> More brands are working to create their own first party data practice - how can a brand figure out whether that’s something that is relevant or important for their business? 

Jason> It’s easy. If you have a business that has customers, then you should be thinking about first party data. No one should outsource their customer relationships.

LBB> We talk about data driving creativity, but what are your thoughts about approaching the use of data in a creative way?

Jason> It’s less about using data in a way that promotes creativity and more about putting data scientists and creative technologists in a room with what marketers have traditionally labeled 'the creative team' to come up with ideas that are unique and impactful.

LBB> "Lies, damned lies, and statistics" - how can brands and creative make sure that they’re really seeing what they think they’re seeing (or want to see) in the data, or that they’re not misusing data?

Jason> We’re all subject to cognitive bias and we all fall in love with and want to defend interesting creative ideas. As a data person, I need to tread a careful line between not ignoring data to make everyone feel good, but also not wielding data exclusively as a scalpel that cuts away people’s work. There are lots of approaches to make this happen. I like the principles of improv- agree to the idea- don’t try to block or deny it, but make it a conversation with “yes, and….” 

LBB> What are your thoughts about trust in data - to what extent is uncertainty and a lack of trust in data (or data sources) an issue and what are your thoughts on that?

Jason> The credibility of the data one uses is certainly a factor when deciding how to move forward. A quant study with 10,000 of your customers hits different than asking your niece and her friends about something because you think they might be Gen Z.  That said, all data in support of a creative idea is directional at best. There’s an art to using a variety of data sources (including your niece and her friends) to build a credible case to try something new.

LBB> With so many different regulatory systems in different markets regarding data and privacy around the world - as well as different cultural views about privacy - what’s the key to creating a joined up data strategy at a global level that’s also adaptable to local nuances?

Jason> The notion of 'Big Data' has beguiled us into thinking that more data is always better. I think that’s where well-intentioned data practices most often go awry. Putting one’s data practice on a diet- forcing ourselves to ask questions about what is essential for the business and jettisoning the rest- is a great way to limit complexity and liability at a global level, while at the same time doing right by the customer at a local level.

LBB> What does a responsible data practice look like?

Jason> There is a lot of flexibility in the make-up and processes of a responsible data practice. The one non-negotiable is that somewhere in all the formality, someone needs to represent the consumer.

LBB> In your view, what’s the biggest misconception people have around the use of data in marketing?

Jason> People assume that data is only involved in great marketing before the idea happens (e.g. predictive modeling) or after the idea is launched (e.g., measurement and optimization). That’s missing a trick. Data should be part of the great idea itself. Look at something like the Spotify Wrapped campaigns for an object lesson.

LBB> In terms of live issues in the field, what are the debates or developments that we should be paying attention to right now?

Jason> I think we should be paying attention to the decentralisation conversations that are happening as part of this explosion of blockchain initiatives. At some point, data itself will need to go through a similar exercise where rights are redistributed more equitably among participants. We should watch this space not so we can emulate it but rather so we can avoid making the mistakes we are witnessing.