Separating Data from Insight, and More Lessons on Social Media Insights
At SMWLDN, L’Oreal, Huawei, and McCann London shared insight into what’s possible when your data insight strategies move beyond counting mentions.
We are excited to announce the first round of leaders who will bring our 2020 theme HUMAN.X to life at the Broad stage this June (17-18).
When it comes to McCann’s Mariam Asmar, the question isn’t whether marketers have the data they have to be successful. Instead, she wants to position herself in a way that allows her to ask: “what’s the insight? What’s the behavior? What’s the takeaway from that?”
Alongside Huawei’s Thomas Curwen, L’Oreal’s Jane Fieldsend and moderator John Tyrell of NetBase, Asmar was thoughtful about the reasoning behind carefully distilling the volume of data that marketers have access to on a daily basis, and managing it in a meaningful way…with the indispensable help of AI.
During the panel, all the panelists mused on how AI had helped them make meaning of the data they were faced with in their industry, and how these insights were not just driving their work, but the success of their respective organizations.
Here are the primary insights and takeaways:
- Not Everything Can Be Answered Through Social Media Analytics
- Go Beyond Vanity Metrics
- Deciding What Data’s Important to You
Putting a Pretty Face on Data from New Places: L’Oreal’s Newfound Emphasis on Data
When asked during the panel about the major milestones her company had reached in terms of adopting and accepting social, Fieldsend was frank: without such acceptance, her role wouldn’t exist. Originally a researcher in a more general capacity, she advocated for a position that explicitly aggregated and sought to make sense of the data that was arising on social. This data went far beyond conversations had about the brand and more into the nature of beauty and skincare as concepts the Internet cared about. AI has been particularly important for their data analysis with the advent of visual and video analyzing capabilities, detecting the frequency and use of color for cosmetics and hair. “[It] is at the core of social listening [for us,” she shared.
At the same time, she was open about the fact that her work can’t—and shouldn’t—be framed as a cure-all for anything that ails the organization. While it is a place to gather a great deal of data, and that data is valuable for discerning brand insight and guiding corporate decision making, “we need to hold up our hands and say, ‘not everything can be answered through social media analytics.’”
Social as Part of a Larger Marketing Mix with McCann
Asmar agreed with her fellow panelist on this last point, touting social not as a panacea for any challenges a company might have but as part of a larger marketing mix. In her experience, she’s had more success “selling” social media to higher-level executives at her client’s companies when they are part of a strategy, rather than the bit that all success hinges on.
Moreover, she acknowledges that while there’s value in being able to make these decisions with the knowledge that data can bring, this doesn’t mean just using metrics—particularly vanity metrics—all the time. “I’m super happy that something like the Fyre Festival happened,” she said, “because it forced us to stop and think.” Now, rather than trying to select influencers based on their number of followers or engagements on posts, marketers and those seeking influencer relationships know to instead look for things like consistent engagement across both branded and typical posts, or how often the influencer successfully drives their followers to the brand’s handle and account. “How are people living across the [buyer’s] journey?” she asked. “You have to look after your folks, and the market [they’re participating in].”
Deciding What Data’s Important to You: Handling Huawei’s High Profile Year
Huawei’s placed particular importance on looking at their own brand and other brands in tandem, after a particularly high-profile year. The data they’ve been looking at has come in largely in the form of news mentions, and Curwen praised AI’s ability to help the company sift through those seemingly countless data points in search of insight that could drive conversations about—or even changes to—product. But in finding ways to effectively distill and share this information, even if it’s not always positive, an organizational shift has taken place.
“Now there’s a sense of looking forward to reporting,” he told the panel. “At first, it was basic and only a few people knew what it was about. Now, even a few hours after campaigns go out, people want to know how things are performing.” A related, and crucial, result? Colleagues the literal world over are asking “more and better questions” about the impact their work can have. “Social analytics is a tool to create a picture of how we’re doing, and to compare ourselves against others within the industry.”
Even after a robust conversation about the importance of data points, categorization, and pulling insight from seemingly endless information, the panel was adamant in their final words about how we classify the consumers we’re hoping to reach. Both Jane and Mariam were adamant about dispelling mythology behind generational divides in marketing. “It’s more about life stage than age,” said Asmar, while Fieldsend insisted that even though generational categories can make our data discussion easier, “it devalues differences in human beings.” Affirming the respective takes of the panelists, NetBase’s John Tyrell chuckled, closing the panel by highlighting that a key part of doing this work of seeking insight “is knowing how to ask the right questions.”
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