When I go back home to New York, I no longer have to tell family and friends that “I do web design.” A white lie told over the years to avoid explaining my job in analytics. What’s changed? The conversations over meals:
- Bagels with a schmear of Adwords. A relative asks me how to spend a $100 credit from Google.
- Sushi rolls wrapped in revenue. A friend speaks about redesigning a website and how she achieved a 50% revenue growth for her client (she still didn’t get paid – such can be the plight of freelance website designers).
- Cocktails with a splash of engagement. The same friend wonders aloud, “What does it mean for BuzzFeed to report on their engagement metrics?”
Suddenly there is broad consensus that doing business requires analytics.
At Digitaria, our creative team says, “everyone is creative” and “a creative idea can come from anyone.” Let’s flip that around to say, “everyone is an analyst,” and “deeper understanding through data analysis can come from anyone.”
In our big data world, “trust your gut” is losing its resonance. Edward Tufte, a pioneer in data visualization, recently tweeted:
— Curt Wehrley (@curtwehrley) March 1, 2014
Previously, non-analysts banked on their experience and instinct to inform decisions. They leaned away from the data. Reports went unopened. Analysts had to devise ways to independently understand fluctuations in website data:
- Analysts watched TV. To shed light on social, search and website usage trends that are correlated with a TV commercial airing.
- Without prior knowledge of any website references (URL, social icon, search term and/or hashtag) used in the commercial, the analyst jotted them down.
- Without a schedule for which commercials aired when, the analyst backed into a schedule to correlate the impact of a TV commercial airing with how people acted online.
- Analysts surfed the web. To capture campaign banner ad screenshots.
- Without confirmation of the banner ad designs, the placements (which sites the banner ads lived on), or landing pages (the pages the banner ads linked to) used in the campaign, the analyst pieced together a representative customer journey through their personal observations.
Analysts also had to devise sneaky ways to encourage interest in the the deeper understanding of the data: aka the what, the so what and the now what. The very same tactic that content marketers use to gain consumer interest in products attracted content marketer interest in campaign performance. Analysts made infographics.
Times have changed. Instead of leaning out, everyone is leaning in for a deeper understanding of the data. Marketers are obsessed with “big data” and have an unquenchable thirst for interpretations of it. Analysts are developing new processes to improve their interpretations by capitalizing on the popularity of data. Here’s how:
- Analysts access and share information in real-time. A few monitors displaying a few data sources is a sufficient beginning for a real-time data hub. Contributions come from the brand, the agencies and the analysts. Spotlight all of the data – not just social media data. Audience triggers, industry news, search trends, the weather, the traffic (whatever will enhance understanding) is included. Why this works:
- Using real-time data as fast content.
- Grabbing attention and helping focus decisions around campaign purpose and impact.
- Sharing external factors that influence campaigns.
- Representing everyone’s data.
- Analysts get deep meaning from data through collaboration. Real-time data is helpful in the short-term, but for long-term studies additional rigor must be applied to understand business impact and actions required. Starting with a research question, such as, “how did our campaign do?” – everyone comes to the table to share information that will shed light on the business impact behind the data. The analysis is better because everyone buys into it, and faster because together actions can be decided. Here’s who is sitting together at the table:
- The brand brings the original creative idea.
- The agencies bring details on their activation strategies and executions.
- The analysts bring the data.
- Analysts reuse their data models. Creating reusable code libraries, templates and models makes reporting portable and scalable. Analysts set expectations for consistency in their approach by developing repeatable solutions. If a model worked once, it can be adapted and applied in different circumstances.
When I submitted my college thesis as a website (pre-Kozmo), my professors did not yet have internet connections at home. Now, popular culture and news stories are punctuated with digital data facts and figures. Analytics is part of water cooler conversations. Everyone is an analyst.
How about you? Have you noticed increased attention towards analytics among the non-analysts in your life?