Media organizations, facing unprecedented disruption, can tap into these seven opportunities to maximize their data-driven decision-making capabilities.
For the business community, 2020 was the year of adapting to survive. The media industry felt the economic, digital, and consumer behavior effects of the pandemic acutely. Emerging trends accelerated at alarming rates: customers flocked to media streaming channels, content and commerce continued to converge, and channels like podcasting rose significantly — today podcasts reach 100 million Americans every month and half of American households report they are podcast fans.
At the same time, a number of previously unpredicted trends emerged, including a rise in local radio and news consumption — much of which was associated with consumers’ desire to stay informed about the spread of COVID-19 — and a jump in gaming as an advertising channel. Among the most significant shifts felt in the media space, however, has been the movement toward a more data-centric operating model.
Media companies are known for their creativity and their ability to surprise, delight, and shock — an art that requires a human touch. Indeed, some of the most memorable campaigns would be difficult to predict — think of the successes of the Geico gecko and Dove’s “Real Beauty” campaign. However, as media organizations face intense downward pressure to demonstrate the business value, the industry has adopted increasingly digital and data-driven approaches. This approach continues to allow media organizations to exercise creativity and push the envelope for clients — on a smaller scale before being measured and expanded.
Access to accurate and precise data enables media organizations to adapt with greater agility, better assess and mitigate risk, and execute on creative strategies that are informed by key performance metrics. However, it’s not just data scientists and the marketing team that need to get behind new data strategies; to retain — and strengthen — their competitive edge, the entire organization needs to help move the needle. Here are seven key elements to incorporate in a company’s journey to become more data-driven:
1. Access 360-Degree Customer Views
In advertising, first-party data is king. The ability to curate data across various channels to create a 360-degree view of the customer is a gold mine. Depending on the size of the organization, full visibility into customer data may be achieved with in-house resources or outsourced to a trusted partner.
As data regulations become increasingly stringent — in light of recent legislation including the GDPR and CCPA — the value of first-party data grows as access to reliable second- and third-party sources becomes more expensive and the data itself less granular.
Effective omnichannel marketing is dependent on the ability to recognize consumers across various channels. As such, a unified view of customer data equips organizations with the tools they need to thrive in an omnichannel environment. Further, a reliable customer repository enables identity resolution for people-based marketing strategies.
2. Establish a Partnership Strategy
The media ecosystem is one of partnerships. Choosing your partners carefully across a defined vetting process is crucial to ensure quality and value. Define a data partner strategy that includes data quality, need, security, and compatibility.
A defined partnership vetting and onboarding process allows an organization to react to the market quickly and secure first-mover advantage. Customers are shifting ever more quickly between platforms, and advertisers need to be where their customers are. At SEI, we worked with a large media agency to define the end-to-end partnership data process, greatly improving client satisfaction and reducing operational cost.
3. Build a High-Powered Data Team
A key to success is effective staffing and a strategic organizational structure that optimizes skill and resource application while mitigating redundancies. Organizations must assess whether they have the right players in the right positions.
Data & analytics talent is especially expensive and difficult to acquire. What’s more, data & analytics talent must be positioned in such a way that their skills benefit the whole organization and its partners and clients.
A DataOps model is an especially useful strategy for streamlining organizational design and improving talent and resource allocation. DataOps seeks to validate data solutions with business users first, then delivers production-ready pipelines to support these users. Methodologies like rapid prototyping, paired with modern visualization tools, can be deployed to accomplish this goal.
4. Integrate Data Security Controls and Training
As data becomes more heavily utilized across the organization, it must also be protected. DataOps and data security should be considered in parallel to minimize risk and improve organizational knowledge.
As organizations make data more accessible, for productivity and self-service purposes, effective controls need to be considered. However, controls and safeguards are only part of the equation; employees’ awareness and sense of personal responsibility for data security and data best practices are crucial for overall compliance.
5. Kickstart a Culture Shift Toward a Data-Focused Mindset
An organization can’t depend on its data scientists and data engineers to solve every problem. Team members across the organization need to ask the right questions and utilize these data tools accordingly.
Organizations should aim to achieve a balance in which the company can answer its data questions with self-service tools 80% of the time and involve data professionals for the remaining 20% of instances requiring a higher level of technical analysis. User-friendly data platforms like Tableau, Power BI, and Microsoft Excel are not enough to create an organization-wide culture shift.
Employees must adopt a new mindset that empowers them to become active participants in the organization’s data-driven decision-making processes. In light of this objective, the leadership team should establish key data literacy and training goals up-front and the data team should develop initiatives to improve the data literacy of the broader organization. Organizations should aim to continuously evolve its data mindset; team members should always be asking themselves: “Can we answer this question with data?” and, “How can we automate this process?”
6. Recognize Opportunities Across the Entire Delivery Chain
There are opportunities to implement data-driven process improvements across the entire value chain — from building audience segments and optimizing channel distribution to creating headlines with AI.
During one client engagement, SEI worked with media buyers to automate local TV ratings. Although buyers had their own unique methodologies for establishing ratings, these disparate techniques generally led to the same answer. Identifying an opportunity, a standard algorithm was established and implemented across the entire TV rating process, leading to 70% of buyer ratings being automatically calculated.
Another example is applying a combination of topic and sentiment analyses to customer reviews in order to identify both recurring themes and general sentiment of the review. Coupling these analyses provides a clear picture of both areas while highlighting opportunities for improvement. Essentially, an organization’s data model, processes, and talent can and should be applied to generate value throughout the delivery chain.
7. Enable Performance-Based Advertising
Advertisers are under tremendous pressure to prove the value of the marketing dollars they spend. As the industry shifts towards performance-based advertising, media organizations need to be able to report on the KPIs of their campaigns clearly and efficiently. Whether directly tied to revenue or not, client performance reporting is a pivotal part of maintaining strong, trusting relationships. Reporting should aim to be transparent, as up-to-date as possible, and accessible via self-serve models. Just as powerful, creative campaigns dazzle, the internal metrics that reflect ROI should do the same.
SEI partners with some of the world’s most influential media & entertainment clients to optimize and scale their data & analytics capabilities, equipping them for sustained success in a disrupted landscape. To learn more — and find out why we’ve been named one of Consulting Magazine’s “Best Firms to Work For” for ten years running — get in touch today.