We all want to assume that we have a data-driven organization. But what does that mean, and more importantly, is that a good thing? More than two-thirds (67%) of CMOs are drowning in data. CMOs are also struggling with the growing number of channels and platforms available to their teams. 33% of marketing managers said understanding what data to use is the most significant impact on marketing today. Harriet Durnford-Smith, CMO of Adverity, said, "CMOs have become overly bogged down by data challenges and lost sight of their core purpose: meeting consumer needs." Being so bogged down in data that you aren't focused on customer needs should be considered a crisis for any marketer! Then, why are we so focused on being data-driven?
What is Data-Driven?
Being a data-driven organization means making business decisions based on data and insights rather than relying solely on intuition or anecdotal evidence. Data-driven organizations use data analytics to inform decision-making across all aspects of the business, from marketing and sales to operations and finance. This approach enables organizations to be more agile, responsive, and effective in achieving their goals. The benefits of being a data-driven organization are numerous. By using data to inform decision-making, organizations can:
Make more accurate and informed decisions: Data-driven decision-making allows organizations to make more precise and informed decisions based on objective evidence rather than intuition or anecdotal evidence.
Increase efficiency and productivity: Data-driven organizations can identify inefficiencies in their processes and optimize them for greater efficiency and productivity.
Improve customer satisfaction: By analyzing customer data, data-driven organizations can identify patterns and trends in customer behavior and preferences, which can improve customer experience and increase customer satisfaction. Here's where customer needs come into play!
Increase revenue and profitability: Data-driven decision-making can help organizations identify new revenue opportunities, optimize pricing strategies, and reduce costs, leading to increased revenue and profitability.
Stay ahead of the competition: Data-driven organizations can use analytics to stay ahead by identifying market trends, customer needs, and areas for improvement.
What Could Possibly be Bad about Being Data-Driven?
While being a data-driven organization has many benefits, there are also potential downsides. One potential risk is becoming too reliant on data and losing sight of the human element of decision-making. Additionally, collecting and analyzing data can be time-consuming and resource-intensive, challenging for smaller organizations with limited resources. A "track everything" mindset can lead to data overload and hinder the ability to deliver valuable insights. While collecting data is essential, it's equally important to prioritize and focus on what is most relevant and necessary to achieving business goals. To avoid the pitfalls of data overload, it's essential to clearly understand what data is needed to achieve specific business goals.
What Should You Track?
Frequently, CMOs state "everything" when asked what they wish to monitor without a clear understanding of their business objectives. This default approach stems from a lack of knowledge about what they want or need. However, tracking all possible data generates overwhelming information, which is seldom examined or utilized. Moreover, excessive data creates clutter, making it challenging to identify essential insights and hindering team efforts to provide valuable contributions to their organization. The "track everything" mentality prioritizes desires over necessities and is a common occurrence. This mentality is prevalent among people for various reasons. Here are four of the most common ones:
Absence of key performance indicators (KPIs)
Inability to distinguish between non-essential and vital data
Concerns about not capturing enough data
Lack of awareness about the potential benefits of data and available analytics capabilities
Creating measurable KPIs from desired business outcomes poses a significant challenge for organizations, which ultimately impairs their ability to track progress efficiently, especially when determining the necessary data from digital touchpoints. As running a business or being involved in its daily operations can be time-consuming, teams often lack the bandwidth to focus on developing KPIs.
Moreover, the research department is often separate from the marketing department, and the two never meet to establish KPIs. I once consulted for a market research training company. When editing their materials, I noted they had no educational content about figuring out what metrics to track. I received shocking feedback. "Please remove that. It is not relevant." I still can't believe this response yet I know how common it is. Market researchers conduct research and feel their job is done if they collect data. There is never much back and forth about how the data is being used or the most important KPIs at the time. If KPIs change based on industry conditions, and these conversations need to be ongoing.
Common Data Tracking Errors
While curiosity is admirable when gathering insights through data analysis, it's crucial to exercise restraint in determining the data to track. Overindulging one's interest can easily result in excessively irrelevant data being collected, which may not impact an organization's KPIs. As companies evolve and digital functionality changes, these "nice-to-haves" often become outdated. Collecting such data can create more problems than benefits, mainly when cost implications are associated with collecting, processing, and storing additional data. Also, keep in mind the connection between the data and the KPIs.
When required, the absence of relevant data can be a significant concern for organizations, leading them to track everything as a safety measure. However, this approach typically results in vast amounts of data being collected, which is then overlooked due to the significant effort required to analyze it. This can leave critical business questions unanswered or generate a distorted view of the data's implications.
It's crucial to strike a balance between not tracking enough data, which can hinder effective analysis, and tracking too much data, which can cause information overload and result in analysis paralysis.
Information overload can result in outcomes that are at odds with the organization's goals. Rather than providing an ideal data framework for delivering valuable insights and driving better business outcomes, the sheer volume of data can become overwhelming. As a result, the organization may struggle to comprehend the available data and its potential for improving business results.
Data and Analytics Go Together
The analytics industry continually evolves, and many organizations struggle to keep up with the changes. We all thought we understood Universal Google Analytics well, and then came GA4! (Ok, they are pretty much the same with an added 36-hour delay). Additionally, knowledge barriers often impede organizations from understanding which tools will meet their requirements and how to use them effectively. Hence, organizations must eliminate these knowledge barriers.
As a result of the limited knowledge stores, organizations tend to resort to the "track everything" mindset. Furthermore, without comprehending how various analytics platforms can aid them (tool capability) and understanding the terminology (tool jargon), organizations often underutilize certain aspects of their analytics platforms while over-utilizing others.
If KPIs and analytics are misunderstood, their match will certainly be misunderstood! For every KPI, there must be associated data or analytics. Typically, primary, secondary, and tertiary goals and a few supporting objectives are identified. Next, the goals that will help achieve the objectives are outlined, such as increasing online purchases, form submissions, or maintaining an acceptable bounce rate. Ideally, the above process should result in a manageable number of KPIs. The recommended approach is to aim for 1-2 metrics per goal and 2-3 goals per objective, with approximately 5-7 key objectives. This provides enough data to comprehensively understand the digital presence's objectives without being overwhelmed with information.
Then, as business needs and digital functionality evolve, analytics must evolve as well. Organizations cannot always predict their future data requirements. While it's essential to ensure that tracking remains valuable in the long run, it's equally important to understand that analytics can be changed as an organization's data needs change. By focusing on key areas and refining tracking as necessary, organizations can avoid the high costs of collecting and storing excessive data.
Become Data Knowledgeable and Get to Know Analytics Tools
While core analytics platforms such as GA4, Facebook Business Manager, and Amazon Seller Central are the backbone of data needs, organizations should not limit themselves to just these platforms on their data journey. In fact, there are numerous other tools that can be used in conjunction with these platforms to broaden an organization's ability to view and analyze its data.
In addition to quantitative data, qualitative data can offer user-driven feedback that puts these numbers in perspective. Surveys, polls, ratings and reviews, and NPS questions are just a few ways to gather qualitative data.
A/B testing should routinely be utilized with all ad campaigns. These tools help determine which designs are most attractive to users, which copy will encourage the most clicks, and enable real-time personalization of site content for different users.
Upon incorporating these additional tools, an organization may find that they don't need to track as much data as they initially thought. Qualitative data tools can help clarify the purpose of quantitative platforms, guiding an organization's focus toward monitoring the most critical metrics and dropping those that aren't necessary. This approach leads to a more robust understanding of end-users and their experiences with the brand.
Understanding the terminology used within an organization's platforms is crucial for long-term success. It helps to determine when tracking certain things is appropriate and how to interpret the data once it's in the platform. This knowledge is essential for making the most of the collected and reported data.
Tracking everything does not make an organization data-driven. Instead, it suggests a lack of awareness and effort required to become a truly data-driven organization.
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