Data and media strategists often work together to create effective marketing campaigns that deliver measurable results, with each role playing a unique and integral part in creating an effective media and analytics strategy. This may seem like a straightforward marriage of two teams with related skillsets, but there is a chasm between the knowledge and strategic thought of each team. Understanding how to best leverage this knowledge and meaningfully weave these two together in the analysis, planning, and execution of campaigns is key to delivering success.
Data analysts bring a depth of knowledge and analytical prowess to the data ingestion, planning, and reporting phases of media campaigns (and so much more), while media strategists bring an ability to drive technical and growth-oriented results from seemingly disjointed data. In this blog, we will walk through how to leverage the strengths of both data analysis and media strategy to gain valuable and actionable insights and how to combine those insights into strategic media plans to drive growth for your business.
Discovering User Trends Using Data
By the end of 2023, global spending on digital transformation is expected to reach $6.8 trillion. As businesses continue working to keep up with the competition, an online presence has become essential to thrive and grow.
In today’s complex digital landscape, business’s use analytics tools to collect data on their online users or customers. From the total number of users visiting a site each day to the average age of high spending customers, there is no limit to the valuable data that can be collected. Every business is different, so the data points that are most important will vary depending on the goals of the business. The data provides information that will allow you to identify trends and the driver of those trends, which can be meaningful to your marketing strategy.
By analyzing the data captured, businesses can identify patterns in behavior or preferences of their customers. Anything from user demographics, purchasing behavior, or general website behavior can shine a light on trends that may already exist within the userbase. For example, a business may find that nearly half of their purchasers are in the 24-35 age range which means that you can tailor campaigns to that demographic. Another insight could be that a specific product is being heavily clicked on but is rarely purchased. Trends like these can be analyzed to make further campaign decisions.
As the raw data can be difficult to use on its own, visualization tools, such as graphs and tables, can be used to help paint a picture of the trends, especially for those who do not have a deep understanding of data points. Data quality is the backbone of any truly effective media strategy, and a comprehensive understanding of the tools and inputs involved is essential in accurate data analysis.
Data Quality Is Key
Not all data is quality data, so it must be verified as part of the discovery process, which includes a data health check, recognizing notable trends and an overview of current data. Clean data is usable data, but what does that mean? Clean data is accurate, complete and error-free (i.e. duplication or misattribution).
Why is clean data so critical? Inaccurate, incomplete or inconsistent data can lead to false conclusions and thus poor marketing decisions. Dirty data can also mean improper site tracking, disjointed naming conventions, poor tagging configuration, and more. The accuracy of data is critical to conducting meaningful analysis, incomplete data will hinder you from painting a full picture and inconsistent data will create confusion and inconsistencies in the analysis. Additionally, irrelevant data will only waste resources and prompt inaccurate insights. Often companies do not have their data well organized, which can also make analysis difficult. The quality of data acts as the backbone to citing useful patterns of user behavior.
Using Data Analysis for Media Strategy
In the same way the analytics team is key in determining what data is useful, it’s the responsibility of the media team to effectively make use of that data to inform campaign strategy. Every company is different, but the key to successfully turning scrubbed and analyzed data into an actionable and successful media strategy comes down to cross-departmental communication.
In our experience, it is critical to have both the media and analytics teams working side-by-side throughout the process to ensure that client needs are understood and addressed by both disciplines. One way to ensure continued communication and collaboration is to have regular check-ins with both teams. Acquiring current data and access to client accounts is key in the discovery phase, while communication about actionable insights, setting expectations for campaign flighting, reporting, optimization timelines, and ongoing data analysis are important next steps.
Asking the right questions is critical. For example, for an ecommerce company, asking “what affinity audiences are over-indexing for sales?” is a key question to address. For a client seeking leads, this could be “are we seeing form submissions from specific zip-codes or industries that have not previously been targeted?” Ultimately, the goal is to leverage meaningful data to strategically outline the most effective channels, audiences, and ad types to drive successful media campaigns that will exceed client goals.
Conclusion
It’s important to take a proactive approach in educating clients about the value of data and analytics. Doing our homework and building actionable media strategies based on a solid analytical foundation is the key to success. By merging analytics insights with media strategy, a comprehensive roadmap can be tailored to a company’s unique goals.