Robyn Cauchy oversees GALE’s suite of data partnerships, insights platforms, and integrations. Her work powers the agency’s core workflows and the Alchemy.Ai ecosystem: GALE’s proprietary AI tool. Speaking with LBB, Robyn gave her insights on the tenets of responsible data practice, the relationship between data and creative, and more.

This piece was originally published in Little Black Book.

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?

Robyn: This question has long been, “Who gets what content, and why?” But the flavor of how I’ve answered that has changed over time. In the past, the focus has been building out and segmenting a structured, quantitative data set that includes demographics, attitudes and a 360 view of behaviors. That’s still a big part of answering, but generative AI has also inspired new ways to mine unstructured data on the open internet, and we’re increasingly experimenting with that approach. 

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?

Robyn: It’s all about where in the creative process you use data. If your data is about testing creative concepts and moving forward with versions that get the most “votes,” that’s a recipe for blandness. But, if you use data to discover unexpected connections, and get you thinking outside the box, this can surface novel creative ideas and elevate brands and CX. This kind of discovery data can be machine-learning analysis of behavioral data to expose intriguing patterns. Plus, rich qualitative insights from observational field research should also be valued as data.  

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?

Robyn: GALE is really proud of the work we’ve done with MilkPEP to sponsor women athletes of all sorts, and runners in particular, through #TeamMilk. This was a pretty big experiment compared to MilkPEP’s historical focus on celebrity influencers and Olympian sponsorships. So, to make sure we executed successfully, we ran a series of focus groups and in-depth interviews with women runners on what they would particularly value in a sponsorship package. Their feedback led us to scale back the swag elements and scale up aspects of community (such as an end-of-race lounge for #TeamMilk members) and impact (donating funds to Free To Run for every new team member). The first sponsorship was such a success–with #TeamMilk sign ups blowing past targets–that we scaled up the initiative to host runners in multiple marathons the next year and even created a net new marathon specifically for women runners (Every Woman’s Marathon, which will take place on November 16). 

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?

Robyn: Data tells you what’s working, and ideally tells you–or hints at–why. And, even more ideally, with whom. I can’t think of a brand or category in which that insight isn’t important. There are always surveys or syndicated data sets, but they are self-reported, infrequent and expensive. That said, first-party data is not yet *critical* to success in all categories. In disintermediated categories like CPG, your competitors are struggling as much as your own brand in terms of first-party-data strategy. But in DTC categories like financial services, subscriptions, mass retail or hospitality, I’m not sure how you’d compete without knowing your customers deeply. One more note: definitely not all brands need a strategy to *monetize* and resell their first-party data! 

LBB: We talk about data driving creativity, but what are your thoughts about approaching data in a creative way?

Robyn: GALE is an extremely analytical agency, but we’re also super creative. So we can get creative about building a value exchange between brands and clients to motivate more or better data collection. For instance, we once built a “Find Your Dream Dress” quiz experience on the David’s Bridal website. Brides-to-be were shown several screens on which they clicked their preferred venue type, color scheme, dress features, accessories, etc. Then, they provided an email address to receive their “dream dress” recommendations. It was interactive and fun, captured leads for CRM, and seeded David’s Bridal with rich insight into tailored creative to send the new contact right from the very first email. 

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?

Robyn: The main thing is they have to have the will to be skeptical and curious about understanding data. For example, questioning the provenance of a statistic and the underlying data collection, weighting, segmentation approaches to produce it. Reading the whole article for context, not just parroting a headline. We all have confirmation bias; not everyone has the will or self-awareness to question their biases. For those who do, it’s helpful to develop data literacy skills. In particular, understanding of research methodologies and data collection best practices, along with understanding of statistical analysis and probability theory. Cultivating these skills in your organization’s employees is increasingly important in preparation for using generative AI to democratize access to data and queries beyond your trained analytics teams.

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?

Robyn: It’s good to maintain skepticism about data, to question it enough and understand how it’s sourced and how it’s been analyzed. The issue is not the skepticism; the issue is not having enough data-literacy skills to poke holes until you develop confidence in the good data and figure out which data to ignore or take with a grain of salt. That’s what makes GALE such a powerful creative agency. We have data scientists and statisticians and quant researchers working alongside creatives and business strategists to elevate and scrutinize analytics techniques. 

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?

Robyn: Regulatory considerations are one of the reasons a first-party-data strategy is so important. Consumers generally expect the companies they do business with to know them. A loyalty program exchanging value for data is even more powerful. GALE has seen the power of loyalty data for insights and targeting with clients in North America, Europe and East Asia. But we know regulations and attitudes will continue to evolve, and that’s a reason for our press into greater usage of open internet and social media data to connect brands to local and subculture, especially in markets where there are greater barriers to identity-based targeting or personalization. 

LBB: What does a responsible data practice look like?

Robyn: I think about data responsibility in three main camps: Consent, Security, and Interpretation. Consent is about making sure your customers know what you’re collecting, how you’ll use it, and sticking by those disclosures. Security is about making sure you’ve stored data in trusted environments and have firm, documented controls on who has access rights (to protect privacy) and edit rights (to maintain data accuracy and integrity). We house a lot of clients’ sensitive first-party data, so we take this aspect very seriously and maintain ISO 27000 and 27001 certification. Interpretation is about hiring for, coaching for, and giving time for employees to apply critical-thinking skills to data they are presented with, check the data and themselves for bias, and filter out inaccuracy or noise that may drive the wrong business or creative decision.   

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

Robyn: All too often, people underestimate qualitative data, especially human observation. Watching how people behave, in nuanced detail, can contextualize what you “see” in structured quantitative data. There’s a perception that qual research takes a long time and is expensive. But it doesn’t have to be. There are more and more tools for conducting remote ethnographies, automating moderation of online discussions, and scaling analysis of transcripts, videos and open text answers using generative AI. 

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

Robyn: Here are five:

• How agencies should pay expanding client expectations of their data layers; how much should agencies foot the bill versus pass through costs to clients?
• More broadly, how should revenue and profit models of agencies shift as generative AI drives efficiencies in the hours required to complete some tasks? Who should reap the benefits of those efficiencies?
• As agencies seek to build custom AI assistants to improve their workflows, how should they incorporate product development processes into their professional services working models?
• How should leaders balance the urgency to find efficiencies from AI with concerns about ethics & norms that are not yet clear?
• How should organizations prioritize building efficiencies into old workflows with building net new innovative experiences? Both matter, but what should be done first, or how can they balance investment to do both in parallel?