Chip Schenck de Shutterstock
The pandemic forced every brand to rethink how they source, produce, program and optimize content, and the consequences are shaping up to be long-term. Consider traditional production processes, which had to become more flexible and agile when full-scale shoots that had typically involved a variety of crew members were put on hold. Meanwhile, campaign lead times have been further reduced by COVID-19. That’s because the unpredictability of the virus has put a greater onus on creatives to turn around ideas quickly and make near-real-time adjustments to ensure that campaign messaging is an accurate reflection of consumers’ experience. (We’re currently seeing this play out with the rise of the Delta variant; brands that had conceived fall campaigns pegged to the “return to normalcy” had to pivot quickly as mask mandates returned). The pandemic also accelerated the need for digital content at a massive scale to populate social channels, websites, e-commerce plays, digital campaigns and more. That’s because digital was virtually the only way that people stuck at home could consume information outside of TV, and once original programmed content started running low on broadcast, digital became people’s main entertainment outlet as well. To stand out, publishers had to up their content game—whether that meant focusing on high-quality, viral content or pumping out higher quantities of content in an effort to stay visible. Meanwhile, new technology in the form of AI-driven metadata, workflow integrations and predictive creative intelligence is transforming the way content is produced. Data has now infused every step of the content production process, from ideation to creation to optimization. The traditional, pre-COVID creative process still holds for tentpole and integrated campaigns with massive budgets, as well as brand kits and long-form content, but we’re seeing the rise of a much more streamlined process for smaller campaigns (e.g., email or social only). With new collaboration software, creative teams are reducing unproductive back-and-forths which historically took hours, days or even weeks. They’re also leveraging data to help steer their decision-making process about which types of images, messaging and even color palettes to use—which helps them get more projects done, faster. Here’s a rundown of how each pillar of the creative process is being transformed: 1. Ideation Historically, campaign and content planning has been a slow and highly manual process (and especially so within agencies). Strategists would conduct extensive research on cultural trends, creative and audience insights, and creative that was already in-market. Then they would get feedback from business intelligence teams, which would try to extrapolate meaningful insights from existing campaign data. This approach was time-consuming for obvious reasons, and while it might make sense for a hero asset or to test new messaging for a product launch, it isn’t scalable for marketers who are launching hundreds of campaigns, optimized for thousands of audiences across multiple platforms. In a media landscape where the volume of content has exploded in order to meet the requirements of optimization-driven campaigns, machine-aided recommendations and predictive capabilities to help with ideation are a must. Through adoption of machine-learning technology, campaign insights will start being more robust (going well beyond cyclical and seasonal insights) and be delivered in closer to real-time. 2. Creation Creatives have long been given access to campaign data, but effective utilization has been another story for a couple of reasons. Many creatives are happy to listen to media strategists deliver an analysis as part of their preparation process, but they don’t have the time or, in some cases, the skill set to dive deeper into the data, leaving creative decision-making to their gut instinct. The other challenge is the sheer volume of data; there’s too much for any one person to take in. This underscores the importance of machine-aided recommendations to help creatives make decisions about style and formats. Significant data scale is required to inform powerful ML models that can deliver these actionable recommendations—and not just of campaign results but also of the taxonomy of the underlying dataset. At Shutterstock, we leverage our immense first-party dataset and ML models to tell creatives more about the quality, uniqueness and resonance of the content they’re considering, allowing them to make informed choices about imagery they select for campaigns. In this new data-driven approach, a social media planner can see if a particular image has already been licensed by her colleague, or how often it’s been used in a campaign. 3. Optimize When it’s time to optimize campaigns, brands have historically relied on A/B testing, which is expensive and only highlights a moment in time (i.e., whether someone clicked on an ad or not)—leaving it to media-buying algorithms to allocate spend to the assets that drive the most clicks. In other words, it doesn’t tell a creative team why their ad did or didn’t work, so there’s no applied learning for campaigns going forward. With dynamic creative optimization (DCO), you can answer the question, “Is the creative resonating with this particular audience?” and optimize the creative and message based on audience engagement. The concept of auto-optimization is already prevalent (wherein numerous versions of creative assets are created and then swapped in and out based on media performance), but AI-driven dynamic creative optimization will further reduce guesswork and drive a more sustainable real-time solution for creative teams. Instead of simply being told that an ad isn’t performing, creatives can learn about which aspect of the asset is hurting results (e.g., the background or colors of a featured dress). From there, the AI can make the change on its own or provide controls for creators to quickly handle it. ** The creative process has historically been highly disjointed, and COVID-19 accelerated the industry’s need to find new efficiencies and collaboration approaches. It’s important for agencies, brands and publishers to sustain this momentum for change, and they can start by integrating new tools into their creative stacks that focus on spending more time creating and less time searching; creating more transparent collaboration; and instilling confidence into creative decision-making processes.ByChip SchenckVP of Sales Innovation at Shutterstock
Shutterstock Editorial lanza el servicio de Derechos y Autorizaciones
La historia del camuflaje en la guerra y la moda en imágenes de Shutterstock
